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What do you think is the most important aspect of a company? Performance? Perhaps you’re thinking of profits. True, performance and profits are crucial. But security tops the list. Every company caters to different users regularly. But does the necessity of security change whether the user base is narrow or wide? Users have access to a lot of information, and often, this leads to the risk of unauthorized access and data breach.
So, what’s the solution? Well, if we monitor user activity, we can curb these issues. This post will look at what user activity monitoring is in detail. We will discuss what UAM is and how it works. We will also discuss why we need UAM. Finally, we will discuss some best practices that will help you to create a UAM program. So, buckle up your seat belts and let’s get started!
What Is User Activity Monitoring?
Suppose your company just deployed a Salesforce app for a customer. There are four or five users in the client company. Two of them need to have admin rights. What happens if one of the users with admin rights gives access to a hacker with ill intent? Maybe it was not the user’s intention; frankly speaking, customers often do not have in-depth knowledge about security practices. What is a solution to prevent this scenario?
You guessed the right answer! User activity monitoring works as a rescuer. UAM is a set of software tools. Companies use these tools is to track the activities of a user or an employee. The purpose is simple: to manage security risks by checking if someone is misusing their extended rights in a system.
How Does User Activity Monitoring Work?
You might be wondering how you would monitor the activity of a user Well, UAM is done with the help of tools that record how a user works with a site or an app. The record is then stored in a log. A UAM tool can have the following features:
Whatever activity a user is performing on the keyboard is captured via keystroke logging. Whether the user is browsing a site or typing, the keystroke logging feature stores the record in a log file.
This feature takes automated screenshots of a device’s screen, which allows the user’s activity to be monitored by another party.
Malicious Site Blocking
A user may not know whether the site they’re about to browse is secure or not. With this feature, you can restrict a user’s access to a malicious site. You can also block sites that reduce productivity or distract a user, such as gaming portals or video streaming sites.
This feature monitors all the actions a user performs on their computer. The record is captured in text form or screen records and is stored in a log file. In case of any suspicious activity, you can analyze the records in the log file.
Why Is User Activity Monitoring Important for Firms?
There are many ways a user can create a security risk. For example, a user could download a movie from an insecure site, or shop online on a site that does not have a secure web host. Some more serious things a user could do include tampering with sensitive company data, like financial and property information. Let’s take a detailed look at why UAM is vital for firms.
Fast Detection of Social Media Malware
When there is no one to monitor activities, employees go off track. We’ll illustrate this with a simple example. We all know that social media is important for companies. So, someone from the marketing team will have access to the social media accounts of the firm.
We also know that not all employees will have work all the time. They might start checking their personal social media accounts in their free time. And what comes with increased social media usage? Increased chance of spam and malware. So, you should monitor activities to enable precise asset use. As a result, even if a system accidentally gets infected with a malware originating from social media, you will detect the threat immediately.
Detects Data Breaches Better
The most prominent source of data breaches is user activity. For instance, trusted employees and vendors often have access to sensitive data. What if any of them misuse the data? There’s no tried and true way to prevent these threats. But they can be reduced by user activity monitoring tools.
If an employee has access to any secured data, you must monitor their workstation. But there are some rules to implement before you monitor their activities.
Be transparent with the employees. Let them know why you need to monitor their activities. Make them aware that you are recording their actions protect the company’s data, not to get them in trouble. They will likely cooperate with you if you provide a solid reason.
Tackles Insider Threats
Every firm expects to have loyal employees. But let’s get real for a moment: Do you honestly believe that everyone wants the business to flourish? What about the competitors and black sheep? Now, let’s consider the worst-case scenario.
The employees of a company have access to private and sensitive data. Access to these data can be a boon when employees use it for the benefit of the company. For example, your sales team often has access to the details of your site’s visitors. The aim is simple: to find out potential customers.
But what if malice is someone’s intention? The person with ill intent can collect the aforementioned data for their personal benefit.
Insider threats are the hardest to tackle as they seem like devils in disguise. When UAM is in place, you can see what a user is doing at any point in time. Thus, insider threats are eliminated.
Now that we know why user activity monitoring is important, let’s discuss some best practices that will help you to create a UAM program for your company.
How to Create a User Activity Monitoring Program
Whether due to misuse of privileges or negligence, data breaches can be fatal for a business. But that doesn’t mean you can’t do anything about it. Here are some tips on how you can develop a UAM program.
Have an Idea of What You Should Watch
Now, every company has a different definition of improper user activity. The most common improper user activities are the use of social media and browsing inappropriate sites like gaming portals. So, how do you decide what to watch? Well, for starters, you can record a few employees and find out what exactly they do on their computers. You can also use keystroke logging to watch what the staff types. Kernel monitoring and capturing screenshots can help collect further data. These methods will help you know when someone is breaking the rules and how frequent certain activities are across your employees—and how to prevent them from happening.
Use Tools That Provide Real-Time Alerts
Consider two scenarios:
- A user makes a malicious attempt to steal data that results in a data breach. Your security team learns about the damage much later. They fix the blunder long after it happened.
- A user performs an activity that he is not supposed to. A UAM tool detects this activity and generates a real-time alert, notifying the security team. They instantly spring into action and prevent a data breach.
Which method do you think is more useful?
A well-known phrase comes to mind: an ounce of prevention is worth a pound of cure. In our case, the second scenario results in better security. A security breach is bad enough—it’s even worse when it goes unnoticed for a long time! It’s important to use real-time alerting tools to prevent any damage in the first place.
Avoid Giving Unlimited Access
Your firm might have some privileged users. But there is no need to give complete access unless it’s necessary. This means that you should give as few privileges as possible.
Before giving someone privileged access, make sure whether they really need access or not.
Also, restrict their usage and limit access to the data that users need for work. Finally, make it a must for your security team to monitor the activities of such users.
Have a Strong Password Policy
Since many employees have access to sensitive data, you need to protect their accounts. This would be a lot of work for you if you were doing it by yourself. After all, there are so many accounts you’ve got to take care of! And of course, not every user is an expert on cyber threats. In short, you’ve got to implement some password policies.
For instance, a user’s password should be unrelated to their date of birth, name, address, or other identifying information. It should have a combination of capital and lowercase letters. Adding numbers and special characters will further increase security. You also need to have a policy that prohibits the sharing or reusing of passwords.
Having a strong password policy will help reduce the risk of password leakage. Not only that, but it will also make the employees practice this policy in their daily life, thus keeping their personal data and social media accounts secure.
Practice Multi-Factor Authentication
You can have as many strict password policies as you want, there’s no sure way to prevent a user from sharing their password with someone else. An employee may be on leave. They may share the password with their colleague to work on some unfinished tasks. You need to ensure that only authentic users get data access. And how do you check that? One way is to enable multi-factor authentication. It’s simple. For example, the user first enters their password. Then, they have to enter a code that arrives via SMS on their personal phone number or answer an automated phone call. Multi-factor authentication makes it harder for bad actors to impersonate a user.
Enhance Company Security With User Activity Monitoring
CCTV cameras have been commonly used to check what goes on in a company. Consider user activity monitoring to be just like CCTV cameras. A UAM tool will record the activity of users and report if anything looks off.
It’s common to get distracted while working. When you have an active internet in your workplace, it’s common to think, “I’ll just play for a few minutes.” However, the problem arises when that gaming site leads malware into your system, messing with IT security.
User activity monitoring helps employees stay focused. Why do we follow traffic rules while driving? Because we don’t want to get caught breaking the rules, and because we know it’s unsafe. The same goes for employees in an IT environment.
So, what are you waiting for? Enable user activity monitoring across your company’s system. You can monitor the activities of your employees and users with privileged access with XPLG’s log management solution. This will help prevent a security breach before it happens.
This post was written by Arnab Roy Chowdhury. Arnab is a UI developer by profession and a blogging enthusiast. He has strong expertise in the latest UI/UX trends, project methodologies, testing, and scripting.
All you telecom engineers out there must have already heard of fault management, right? Well, those of you who haven’t yet heard of it and need to understand what it’s about, worry no more! I’ll help you. Let’s start by defining the term.
First, What’s Fault Management All About?
ISO (International Organization for Standardization) defined a network management framework of which fault management is a component. That framework is FCAPS (Fault management, Configuration, Accounting, Performance, and Security). In other words, fault management relates to network management. In the same vein, a network management system must include a fault management system. The latter finds network problems and takes action to correct them. It also identifies and prevents potential or known problems that may occur in the future.
Therefore, the prime goal of fault management is to maintain network connectivity at all times. And by managing faults in a network, applications and services that rely on that network remain up and running. Most importantly, those applications and services stay accessible and properly functioning.
However, you know networks fail and go down, don’t you? So, what we all want are applications and services with fault tolerance and downtime minimization mechanisms in place. That’s when fault management systems come into play! Take a look at the next section for an explanation of what fault management systems do exactly.
How Can You Fight Network Faults?
Fault management systems are tools to prevent network faults from happening. Their major motto is fault tolerance, and downtime minimization is their main concern. But let’s first understand where those faults come from.
Network faults originate from events in the network that have an impact on service delivery. Those events may only interfere with service delivery, but, worse than that, they can diminish or block service delivery. Hardware failure, connectivity loss, and power outages are three examples of network faults.
Upon fault detection, a fault management system notifies the network administrator by triggering alarms. This means that a fault management system embeds an alarm system. Think of an alarm as a notification that can be viewed in the fault management system itself. Additionally, the network administrator can receive an alarm via email or SMS.
The fault management system shall monitor a more faulty area more frequently and thoroughly. So, depending on the frequency that an area of a network experiences faults, the intensity of monitoring shall be adjusted.
Sometimes, fault management systems can automatically solve a fault, dismissing the network administrator’s manual action. They can even use programs or scripts to prevent some faults from occurring! Pretty cool, isn’t it? But not every problem that affects the operation of a network is major or requires special attention. Many problems just require a trivial automatic fix performed in no time. As a result, IT teams can focus on major problems that are more difficult to fix.
Next, we’ll see how exactly a fault management system works.
More About Fault Management Systems
First and foremost, a fault management system must have a clear picture of the network topology. That topology contains a map of every device and node connected to the network. This allows the fault management system to oversee every point of the network that may cause downtime.
But how does a fault management system work? It frequently queries devices and nodes to evaluate whether the hardware is behaving well or not. Then it collects the information retrieved from those queries and analyzes it. Its goal is to catch any network performance problem that requires a solution. Sometimes, devices and nodes send information on performance problems to the fault management system by their initiative.
Fault management systems keep networks operational with the features that follow.
Thresholds defined in fault management systems are based on prior knowledge of conditions that led to faults. Therefore, thresholds are a warning mechanism to prevent potential faults. Let me give you some examples of thresholds:
- A certain limit in the capacity of a node’s processing. If going over that limit led to faults in the past, a threshold shall be defined for the limit.
- A particular link utilization. A link is a connection between two nodes in a network. Sometimes, traffic over a link is high enough to cut access to the whole network. That happens when a link’s traffic consumes all of the network bandwidth. And if it caused problems in the past, then that amount of traffic shall become a threshold. To calculate the threshold, divide the average traffic over the link by the total link capacity. You can even calculate the threshold by millisecond, second, minute, hour, and so on. Some tools use a weighted average, meaning more recent values weigh more than older ones.
- The network utilization. Modern networks consist of many links. So, the average link utilization in a network may be a threshold.
To sum up, you must have a proper network infrastructure layout. If you can’t, at least determine improvement areas. Frequently, a single bottleneck in the infrastructure is a major source of faults in your network.
Constant Network Monitoring
A fault management system constantly monitors the status of a network.
Continuous Network Scan for Threats
For instance, viruses can lead to faults in networks. So, fault management systems must be able to detect them and either act accordingly or sound an alarm.
Network administrators receive fault event notifications sent by the fault management system. The same system may automatically solve those faults, but some faults demand major action.
Fault Location Tracing
A fault management system needs to trace the locations of faults. One of the main reasons for that is to adjust the intensity of monitoring for the most faulty areas. By doing so, the fault management system can better prevent faults in those areas.
Automatic Correction of Fault Conditions
If it doesn’t require much effort, a fault management system can automatically prevent faults. It does so by correcting the conditions that may cause those faults. To achieve that, the system executes programs or scripts to perform minor fixes that are neither complex nor time-consuming. The same programs or scripts also enable the fault management system to automatically solve actual faults.
A fault management system creates detailed logs of system status and the preventive or reactive actions it took. From the perspective of fault prevention, logging with details is extremely important.
Now you know how a fault management system works and what its main features are. The next step is to distinguish between active and passive fault management systems. Let’s take a look …
Active vs. Passive Fault Management
Fault management and fault management systems can be active or passive.
Active fault management systems use strategies such as ping or port status checks to query devices and nodes. That allows determining the status of those devices and nodes by routine. It’s an active approach to fault management. That is to say that the identification and correction of conditions that potentially lead to future faults are proactive.
On the other hand, passive fault management systems monitor the network for actual fault events that have already occurred. It’s more of a corrective than a preventive approach. To clarify, it may only discover faults until there’s nothing left to do.
Now, focusing on the passive fault management approach, what’s the process of detecting a fault and solving it? Check it out in the next section!
The Fault Management Cycle
The fault management workflow is cyclical and continuous. It starts with fault detection, follows some steps until fault resolution, and ends where it began: fault detection. This is the general fault management cycle, as you may find below in more detail. However, any fault management system may implement a specific process that goes beyond the basic steps below.
Consider that a fault management system is monitoring a network. Consequently, it discovers an interruption in the service delivery or that the service delivery performance is deficient.
Let the investigation begin! Go to the next step.
Fault Diagnosis and Isolation
The fault management system determines the source of the fault and its location in the network topology.
OK. So, the system already knows where smoke is coming from. But you know a bad thing never comes alone. What if there are a bunch of fault events all related to each other? It’s time for some alarm grouping!
Fault Event Correlation and Aggregation, Plus Alarming
A single fault can buzz multiple alarms. But that could be disturbing to the network administrators. And that’s why fault management systems combine related fault events and conduct a root cause analysis on them. Only after that, those systems fire an aggregated alarm for network administrators.
The network fault buzzer sounded! Now what?
Restoration of Service
Once the alarm is out to the network administrator, the fault management system automatically performs a quick and simple fix. It executes programs or scripts to get the service up and running again as soon as possible.
Service automatically restored, available, and working? Check. But what if the kind of fault demands a less quick and more complex fix?
Depending on the complexity of the fault, automatic restoration of service may not be possible. In those cases, the network administrator or a competent technician performs a manual intervention.
In this last step of the workflow, someone manually solves the fault. The resolution may be a correction, a repair, or a replacement.
At this point, you may be wondering what you need to do to put things into practice. Allow me to show you the way in the next section.
Ok, but How Can I Start With Fault Management?
You can either develop your own fault management system or buy one. If you’re going to develop your own, I must say that agile methodologies are appropriate. You can start by working on the most important root causes and observed signs of fault. Or focus on an area of your network. Or even on a type of device or node.
After that, here are the main steps that fault management systems specifically comprise:
- Define diagnostic goals for the system.
- Know and involve subject matter experts capable of providing reference knowledge.
- List possible root causes and observed signs of fault and prioritize them by impact and frequency.
- Specify thresholds.
- Test the system in a simulated environment with simulated values.
- Test the system in the real environment with live data.
Now, imagine you’ve got an IoT network. That demands special care like you’re about to find out next.
And What About IoT, Blockchain, and Cloud Computing?
FCAPS is useful to set a straightforward common ground for talking about network management with corporate management. And it still applies today. Nevertheless and when it comes to IoT solutions, they didn’t exist when FCAPS was defined. And IoT sensors are likely to generate measurements that can be confused with faults. Fault detection, diagnostics, and isolation are thus vital in IoT networks to ensure accurate data sets.
FCAPS is quite appropriate for centralized single-provider environments. But in a blockchain, how do you know which provider is responsible for a fault? In a blockchain, fault management takes place by sharing the state of each vendor’s network across the entire blockchain. In case of a fault, the whole blockchain receives a data log.
When applications are on a cloud, they go from server to server according to loads. For example, fault detection is tougher with virtualized servers. But there’s more:
- Different tenants may experience a fault that originated from the same source (an overloaded server or an overloaded link).
- Also, the high number of devices, nodes, and links contributes to the likelihood of fault occurrence.
- The constant addition, upgrade, or replacement of devices contributes to configuration errors and, consequently, opportunities for faults.
- And a change in one device can affect others.
Keep in Mind: Scan, Detect, and Solve
An operational fault management system is one of the most important assets against actual or potential faults in a network. That system can smell the fault and go after its source. It does it nonstop.
Once the fault management system gets to the source of the fault, it studies the fault. As a result, it suggests a solution to those in charge. It may even automatically execute restoration programs or scripts to instantly fix the fault.
Nobody wants to offer service over a network that’s down! So, set up your fault management system to prevent and react to fault events in your network.
This post was written by Sofia Azevedo. Sofia has most recently taught college-level courses in IT, ICT, information systems, and computer engineering. She is fond of software development methods and processes. She started her career at Philips Research Europe and Nokia Siemens Networks as a software engineer. Sofia has also been a product owner, working in the development of software for domains such as telecom, marketing, and logistics.
Logging is vital to the success of any IT project. With solid logging practice, you can troubleshoot errors, find patterns, calculate statistics, and communicate information easily. With the size and complexity of modern systems, performing these actions involves various analysis activities.
One of these important analysis activities is anomaly detection. What is anomaly detection, and where does it fit in all of this? That’s what this post is about. I’ll first present a succinct definition of what anomaly detection in log file analysis is. I’ll then explain the definition in detail, before discussing why it’s important for your business and introducing how it works.
Anomaly detection in log file analysis is the practice of automatically analyzing log files to uncover abnormal entries and behavior.
There’s quite a bit of information squeezed into those 14 words above. Let’s break it down.
Whether you arrived here from the XpoLog Blog or from your favorite search engine, you’re probably aware of what logging is. But, just as a quick recap, logs provide a history of actions performed by systems and the results of those actions. Given the complexity of many systems and the fact that they’re always on with 24-7 availability, logs can rapidly become difficult to manage. This is especially true when logs from multiple systems are unified. As a result, it’s not feasible to manually process logs. This is where the automated analysis part of the definition comes in. Automated analysis, such as XPLG’s patented approach, uses advanced technologies, such as machine learning, to process log contents.
As for the abnormal entries and behavior part of the definition, there’s no point investing in advanced technologies to process log contents unless you’re looking for something in particular. However, that something, if it’s known, can be hard to define. And searching for it is a long and tedious task. If you think about most of the logs you’ve seen, there’s probably one feature that stands out: they’re repetitive. Most of the entries simply say an event occurred. That’s it. What we want to do with anomaly detection is report when things aren’t following the normal pattern. It means the automated analysis needs to look at individual lines and groups of entries to determine if they’re expected. This can help us proactively find concerns before they’re a problem and help troubleshoot errors when they arise.
Why Anomaly Detection Is Important
Imagine you walk into work one day to find that a system you manage has been running slowly. Your team updated a few features in the last release, but that was over a week ago. There’s no reason why anything should be different now. Maybe it’s an integration that’s causing problems. Or maybe the server has a hardware issue. Whatever the case, you’re going to have to take a look at the logs.
Now you have a choice. Which of the following do you choose?
- Go through the raw log file with Ctrl+F and some regex. Maybe you’ll modify that script you tried to make last time. It didn’t quite work, but you think it sped the process up.
- Or run a log analyzer to identify entries and behaviors that don’t look like they fit (anomaly detection).
Keep in mind that the log has been recording on average one message every two seconds for the past two weeks. That’s over 600,000 entries you’ll need to search.
The first method will take hours, if not days, of effort. You don’t even know what you’re looking for. Or where to look first. The problem might not even be in the error and warning messages. It might be hidden in success messages that are fired too quickly or out of order. No amount of regex will find that.
The second method may not find the problem immediately. But it’s going to give you a subset to work with—things that you can investigate further without having to dive into those 600,000+ entries manually.
In short, it’s not feasible to manually inspect log entries in modern systems. Therefore, anomaly detection in log file analysis is important because it forms part of the arsenal of automated log analysis. This saves hours of effort and increases the likelihood of finding the root cause of a problem. This leads to increases in uptime, reduction in errors, and improvements in system design. All of which are likely vitally important to your business.
How Anomaly Detection Works
If you came to this page via a web search, you may have seen the GitHub repos and research articles that present various anomaly detection algorithms. These generally consist of three components: entry parsing, feature extraction, and anomaly detection.
Here’s a bit more detail about each:
- Entry parsing moves through the logs to process the entries into a consistent structure, determining relationships between entries and creating a map of the structured events.
- Feature extraction then looks at the map of structured events to turn the gathered information into a series of attributes about entries and groups of entries, known as feature vectors.
- Anomaly detection algorithms are then applied to the feature vectors to determine whether an entry or group of entries is abnormal.
The process is obviously much more complex once you dig into each component. If you’re familiar with machine learning, there are a range of supervised and unsupervised models that follow this pattern. For those interested, commonly used supervised methods include logistic regression and decision trees, while unsupervised methods include clustering and principal components analysis.
One Drawback: The Learning Curve
As with everything in this world, the bad must balance the good. With that in mind, the main drawback of anomaly detection is the learning curve. Without a detailed understanding of the different algorithms, it’s hard to know which method best suits your logs. Moreover, once you’ve made a decision, you need to train and optimize the model for your specific scenario. This process then needs to be repeated for each new implementation. It’s a nontrivial, time-consuming exercise that can easily be done incorrectly or suboptimally.
Before you get discouraged though, this is where the real strength of XPLG lies. By combining your logging with various forms of automated in-depth analysis, including anomaly detection, XPLG is uniquely suited to flatten out the learning curve. The tool has a patented analysis approach wrapped in a user-friendly GUI, which simplifies the process and means you’re not having to implement and re-implement an ill-fitting solution.
When and Where Can Anomaly Detection Be Used?
Anywhere you have a log file you can use anomaly detection. It’s best suited to large, complex systems and when you have a unified logging practice. This can range from access logs to runtime, development, and security logs. Anomaly detection can help troubleshoot why processes are failing, identify if you have any security concerns, and perform a sanity check on your software.
Importantly, anomaly detection isn’t just a reactive technology. It’s proactive. That means you don’t have to wait for an error to occur before you look into the logs. The process can run on any log at any time, including regularly in the background. By opening up proactive analysis, you can find and solve concerns before they become problems.
We now know what anomaly detection in log file analysis is. We also know the high-level process taken by anomaly detection algorithms and that there are a range of available algorithms to perform the detection. I’ve also discussed why it’s important and how it can not only save time, but allow for troubleshooting that would not otherwise be possible.
Where to Go From Here
If you haven’t already, download the XpoLog7 and request a demo. Try running anomaly detection on some of your logs. Who knows, you might find something interesting that would have otherwise passed unnoticed.
For those out there searching for “What is syslog?,” this post has answers to all of your questions.
Simply put, syslog handles a very important task—collecting events—and is present in almost all systems and peripherals out there. It’s the standard used to collect events in an ever-growing number of devices. Syslog can often be related to Ubuntu and servers, but it’s certainly much more than that. This protocol is also used by your printer, router, phone, and *nix OS. If you can name a device, it probably uses syslog or any of its versions and variants.
More than just storing events, an even more critical part of “logging” is being able to check the messages because having visibility on them is what makes them useful. This post is about syslog, and later in this article, I’m going to be covering in detail everything related to this essential protocol. Format, log levels, transmission, and visibility will be included here.
What It Does
First, just a tiny bit of a history lesson before we dig in. Syslog has been around for quite some time. It dates back to the ’80s (courtesy of one Eric Allman), and its adoption was immediate. As I mentioned above, it immediately comes to mind when I hear “Linux,” but its usage has extended way beyond that. Syslog is now present on network devices, specifically routers.
If you feel nerdy enough to take a look at a deeper level, the syslog protocol standard is available here.
Syslog is where the network collects events. The information about those events can include
- Access logging
- Wrong password login attempts
- Anomalies in the system functioning
- Hardware errors
- Software errors
- OS messaging
Its advantages don’t stop there. An everyday use case nowadays involves cybersecurity. Say that one of your servers has been compromised. The attacker can easily erase the server’s logs. But, if you have syslog configured and a different server receiving all of the events that are happening, you can timeline the attack and better respond to it.
Another everyday use case you might relate to involves auditing. Timestamping events and tracking severity levels are vital advantages that make this protocol essential when it comes to auditing a network and its responses to different situations.
The timestamping is, of course, a crucial part of the logged event. But it’s not all that a syslog message can carry. We’ll discuss further on how that looks with the different formatting and the several log levels available.
A typical syslog message should have the following elements:
- Structured data
Reading the specification, we learn that within the header are several parts:
- Priority: discussed below
- Version: the version of the syslog protocol in use, might come in handy for processing
- Timestamp: full timestamp of the event
- Hostname: hostname of the machine originating the message; The standard recommends using an FQDN in this field.
- Application: identifies the application or device originating the message.
- Process ID: good old PID, along with the application name, to identify the originating event
- Message ID: the type of message being sent, sometimes associated with the protocol being triggered; TCPIN or TCPOUT are good examples.
What follows is the structured data (SD) part. I won’t get too deep into what it does, but it’s metadata about the message itself. The SD field isn’t required, but if it’s not present, the NILVALUE character should be sent in its place. The specification, however, warns us about a possible conflict with collectors if the SD is malformed.
Last but not least, the final element of the syslog event is the message. It is required to be encoded in UTF-8 and is normally free-form—text that’s easy to read (one can hope) to provide context to what’s going on. The message should explain and, along with the context provided by the SD and the header information, should identify an event. It should tag the event to an element in the network, or the application running, or even the server transmitting the data.
At this point, it’s worth mentioning that although it’s standardized, syslog isn’t as consistent as it should be. The formatting can vary depending on the developer, the manufacturer, the system, and so forth. While many of the messages will be perfectly readable (in human terms), some applications may not care about that and will change the formatting. A potential cause might be an old version of rsyslog or syslog-ng. A nonstandard format might make it hard to curate and process the messages, for which you’ll need a specialized tool. More on that below.
Included as part of any syslog message being transmitted is the severity level of that message. The following table represents the standardized log levels available in the protocol.
|0||Emergency||The system is unusable (also referred to as a panic condition)|
|1||Alert||Action must be taken immediately|
|5||Notice||Normal but significant conditions|
A limitation of the UDP (used by syslog by default) is that it does not confirm receipt to the originator. This means that packet loss can be a problem. This raises concerns about syslog’s ability to adequately check and collect the logs. As we learned previously, syslog can be software agnostic. Which means that your syslog server can collect data from a number of different origins, including servers, *nix implementations, routers, IoT, etc., which can be overwhelming.
In order to transmit those messages, syslog operates in the UDP port 514. So, remember to keep that open for messages to go through. If you’re looking for alternatives to UDP (remember that UDP is, by nature, unreliable and doesn’t provide flow control, retransmission, or connection tracking), TCP can also handle syslog transmissions via the same 514 port. Should you take the security one step further, rsyslog provides the ability to transmit the messages using TLS, effectively securing your messages traveling over the wire.
As far as the packet size goes, a lot of information out there will tell you to keep it under 1K. In fact, the first versions (from 2001) of the spec suggested so. However, newer revisions (from 2009) exist, indicating that no upper limit is preset and that it’s up to each implementation to limit the size of the message. It’s something you have to work on with your collector and transport layer implementations, but it’s no longer a protocol-limiting factor.
Once the data is centralized in one location, you can visualize the information via graphs and diagrams. However, as I’ve mentioned a few times now, the amount of data being collected by the server can be astonishing. The best friend of anyone trying to work all of this out is a tool that can filter and interpret the sheer number of messages flowing through. You’re more than welcome to try for yourself. But, you’ll spend a lot of time navigating through endless timestamps and hardware events. Being able to sort through an infinite number of lines and events quickly is particularly important when your infrastructure is under attack. If you’re going through a DevOps incident, you’ll wish you had an automated tool in your corner.
If you’re willing to add more tools to your skill set, check out XPLG’s syslog server. It’s not just capable of streaming events from any source out there. This tool will alert you in real time if custom data rules are met. Deploying it will only take five minutes. And if you’re not ready to commit, XPLG’s Free Forever pricing tier has you covered.
Ultimately, the need to implement syslog should be clear. There’s plenty more to it than what we can cover in a single post. What’s really important, though, is to understand that as your company operates and grows, the need to keep an eye out on the important data is imperative. You don’t want to be in a situation where you’d say to yourself, “If only I had that logged.”
Guillermo Salazar is the author of this post. Guillermo is a solutions architect with over 10 years of experience across a number of different industries. While his experience is based mostly in the web environment, he’s recently started to expand his horizons to data science and cybersecurity.
Today’s post continues the trend of covering log-related topics, by answering the question: “what is log analysis?” What is this, and why is it essential for your organization?
We will discuss log analysis practices and show you a few examples of how to investigate logs for different purposes.
We have also gathered a list of log analysis solutions, elaborated on the pros and cons, and summarized when it’s recommended to choose each one.
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Logs are ubiquitous in the tech industry.
A medium-sized IT organization can generate gigabytes worth of log entries daily.
Those logs come from a large variety of sources: operating systems, network devices, web and application servers, applications, IoT devices, just to name a few.
The aggregate of all these logs has the potential to be an oracle.
They might offer a unique window to all facets of the organization.
Unfortunately, since most teams and organizations treat logging as a mere “putting out the fires” mechanism, all of this potential goes to waste. That’s where log analysis comes in helpful.
In this post, we’ll offer a guide on log analysis.
We start by defining the term.
Then we proceed to cover some of the justifications and use cases for log analysis.
After that, we show the workings of log analysis, start with normalizations, and then exploring the other phases or processes that it includes.
In the end, we gathered and covered a list of well-known log analysis tools for your convenience and recommend when it’s best to use each one.
Defining Log Analysis
You can find many different definitions of log analysis around the web, varying in their length and straightforwardness.
Here’s how I’d define it:
Log analysis is the process of reviewing and understanding logs to obtain valuable insights.
So, this process allows organizations to analyze their logs in order to obtain knowledge that they wouldn’t be able to obtain otherwise.
They can then use such knowledge to their advantage, not only by improving their decision-making process but also in a variety of different ways.
We’ll explore those in more detail next.
Log Analysis: Understanding Its Value Proposition
Why bother with log analysis? What are the benefits your organization can reap from this practice?
As you’ll see, there are many reasons why organizations do log analysis. We’ll divide our list into three main categories: Security/Compliance, Troubleshooting, and Insights.
Log Analysis for Troubleshooting
The first reason for performing log analysis is also some of the most important reasons to perform logging itself. Namely troubleshooting problems.
Software development—and IT as a whole—is terrifyingly complex. Even with huge investments into defect prevention, we can never know for sure that our project will work as intended. And when it inevitably fails, we want to be able to access as much information about the problem as possible. That way, we can assemble the puzzle, understand what went wrong and why, and fix it.
Let’s drill down with two examples from the application monitoring world:
Rules-based application monitoring
When you know what you should monitor, you can use log analysis tools to hunt download critical errors and optimize applications.
Log analysis then helps you to find the problem in real-time and fix it before it creates damages.
You create your own set of rules and get alerts to a verity of channels such as MS Teams, PagerDuty, email, more.
ML-based log analysis for application monitoring:
Searching for errors and problems can be compared to searching for a needle in a haystack.
For that reason, some log analysis tools offer automated log analysis (learn more about automated log analysis).
Basically we are talking about ML-powered engine that learns your environment and detects problems, exceptions and anomalies in the application behavior on its own. Where there is abnormal behavior, where there are events that you didn’t encounter in the past, unique errors.
Instead of creating rules, the tool scans the data and understands when problems and risks might occur.
In this case, the log analysis tool helps your department taking a proactive approach.
Security and compliance concerns are high on the list of motivations for performing log analysis.
And the reason for that is clear: security problems can have catastrophic consequences for any organization, even putting it out of business.
So, any investment you make in the security front is justified, since the costs of the lack of investment tend to infinity.
So, the first reason why organizations should care about log analysis in the context of security is to understand and respond to security incidents such as data breaches.
Organizations should be ready to act swiftly and decisively when security incidents happen since that can be the difference between staying in business or not.
Another important use case for log analysis is to help organizations to conduct forensics due to some investigation.
In our post on log forensics, we list the following as reasons for performing log forensics:
- Finding the vulnerability which was exploited to allow an invasion
- Finding proof of a crime or hack
- Enabling data recovery from disasters
- Tracking the activities of a malicious actor
Since log forensics is, in a nutshell, log analysis put to the service of computer forensics and the law, all of the above are justifications for using log analysis.
In the compliance side of things, organizations might find log analysis useful for complying with both their internal security policies and external regulations.Here is an example of how a log analysis tool visualizes and monitors users’ activity to enforce and verify security policies.
It visualizes and monitors: users’ patterns and operations, users’ access to organization assets, users’ journey, onboarded users/deleted users. for instance track past employees entrances.
Last but not least, the “insights” category. As already mentioned, log analysis can help organizations gain insights that wouldn’t otherwise be accessible.
By having those insights, teams and organizations can improve their decision-making process, reevaluating strategies, and changing them as needed.
One typical example would be applying log analysis to understand user behavior as mentioned.
By doing so, the organization could, for instance, find out that users barely touch the new feature they thought would be a game-changer.
Aware of this fact, the company can now make an informed decision about whether to continue supporting the feature or not.
Log Analysis: Basic Workings
As we’ve explained in our article on log collection, logs can come from a large variety of different sources. Operating systems generate logs, but so do user-facing applications, network devices, and more. A typical log file contains many log entries, sorted chronologically. Those entries are stored in a persistent medium such as a file in the disk or a database table.
In order for the logs to be processed and interpreted correctly, they need to go through some very specific changes in their content. Such changes are necessary to avoid confusion due to differences in terminology. For instance, logs that come from a certain source might use “WARN” as one of their levels, while others might employ the whole word “Warning,” or even a completely different word. It’s crucial that such divergences be found and normalized.
Keeping formats and terminology consistent across all logs will reduce the number of errors and also keep statistics accurate. As soon as you collect and process the logs, it’s time to analyze them to detect not only usual patterns but also anomalies.
Log Analysis Processes
In the last section, we’ve touched briefly on the subject of normalization, which is a process that changes the logs data in specific ways, to make the analysis easier and avoid errors.
Normalization, though, is just one of the processes log analysis includes. We’ll now cover these processes—including normalization—in more detail.
Normalization is a technique that aims for consistency. It converts messages—in our case, log entries—so all of them use the same terms and data formats. Normalization is an essential phase for every process that centralizes log data. That ensures that log entries from different types and sources express information in the same format, using the same vocabulary.
As soon as logs from all different sources are normalized, it’s time to start processing them. At the “pattern recognition” phase, log analysis software can compare incoming entries with stored patterns, allowing them to differentiate between routine, ordinary messages—that should be discarded—and extraordinary, abnormal ones, which should trigger alerts.
Classification and Tagging
Classification is precisely what its name suggests. It might be advantageous to group or categorize log entries according to their attributes. You might want to filter logs by a specific date range, or track occurrences of a given severity level across all log sources.
Correlation analysis is the process of obtaining information from a variety of sources, finding the entries from each of those sources that are relevant to a given known event. This process is valuable because when an incident occurs, it might leave pieces of evidence in log entries from many different sources.
Log Analysis: Make the Most Out of Your Logging Approach
Logs are omnipresent in IT and can come from a vast variety of sources. The primary purpose of logging is, as you’re aware, to help organizations troubleshoot problems in production. However, some techniques or processes enable organizations to use logging in exciting, novel ways. One such technique was the topic of today’s post: log analysis.
Log analysis is the process that helps you gather the raw data from your logs and discover meaning there. By analyzing your log entries, you’ll be able to find patterns you wouldn’t be able to find anywhere else. Having those insights helps you in your decision making, problem troubleshooting, and even with security and compliance.
Along with other techniques such as log analytics and log forensics, log analysis presents organizations with the opportunity of making the most out of their logging strategies. Most organizations treat logging as a mere troubleshooting facilitator. The mentioned techniques allow you to use logging more actively, as an insight generator and decision-making aid.
Log Analysis Tools – Comparison List
We will review 5 log analysis tools in this article. We have comprised an 11 log analysis tools comparison table, which you can get directly into your email.
Log Analysis With ELK Stack
ELK is an acronym that stands for Elasticsearch, Logstash, and Kibana.
Elasticsearch is a search and analysis tool. Logstash is a “data processing pipeline.”
It’s used to ingest data from many different sources, such as databases, CSV files, and logs.
Kibana is known as the charting tool for the ELK stack. It provides search and data visualization functionalities for data indexed on Elasticsearch.
As letters in the acronym refer to open-source solutions, you can deploy your own ELK stack without having to pay for it, which may prove a good alternative for organizations on tighter budgets.
ELK’s setup is labor-intensive, and it presents high storage and computation requirements. Also, the open-source version doesn’t offer some desirable features, such as alerting and monitoring capabilities, which would require the Gold tier in their subscription model.
Who should choose ELK?
This solution might be better suited to organizations who like the flexibility of open-source and can afford in a more DIY approach.
ELK might also be the natural choice for small development teams who already use Elasticsearch for other needs.
Splunk Log Analysis Solution
The first log management tool in our list is Splunk, which is a comprehensive utility very well-known by sysadmins. It’s available as a downloadable tool for Linux, Windows, and macOS X. A cloud version also exists, as well as a free version with limited capabilities.
Splunk is a complete solution with an extensive list of features, which includes machine data indexing, real-time and historical searching, advanced reporting functionalities, and more.
As already mentioned, Splunk is a popular tool among system administrators. Since the community is so large, you have many other fellow users who ask for help. This might also make the onboarding of new team members easier: it’s probable that they already know the solution.
Splunk is probably a better fit for organizations with larger budgets. Despite offering a free version, most of the more desirable features are only available for users of the Enterprise edition. Splunk is also somewhat harder to learn than its competitors, which is relevant when thinking about TCO.
Who should choose Splunk?
Splunk might be an excellent fit for organizations that are searching for reliable technology and a consolidated brand and have the budget for it.
Loggly is another cloud-based solution. It’s a log aggregation and analytics service that allows you to analyze all your log data in real-time from a single place.
Loggly comes with good search capabilities, combined with the capacity to collect and analyze logs from many different sources from a centralized place. In the visualization department, Loggly comes with pre-configured dashboards covering popular technologies but allows you to combine its advanced charts into customized dashboards.
Despite having good search capabilities and great visualization tools, Loggly might not be as feature-rich as some of the other tools on this list.
Who should choose Loggly?
Loggly might be the best fit for organizations looking to deploy primarily to cloud instead of on-prem and also can do without more advanced features.
SumoLogic Log Analysis Tool
SumoLogic is a cloud-based platform which provides centralized log analytics service.
It uses machine learning to detect patterns from your logs in real-time, allowing you to gain insights into your application’s behavior.
SumoLogic doesn’t require a labor-intensive install processing. It’s easy to set up and start using and doesn’t require a lot of upfront costs.
SumoLogic is a unified platform for all your logs and metrics. It presents an extensive list of features, which includes great search capabilities and the use of advanced analytics by leveraging machine learning and predictive algorithms.
Probably the major con of SumoLogic is its pricing model around log data retention.
With the free and trial accounts, you get seven and three days, respectively. To retain data for longer periods, you’d need a professional or enterprise account, which can get prohibitive depending on your organization’s budget.
Who should choose SumoLogic?
SumoLogic is a feature-rich and convenient—due to being SaaS—solution.
That, combined with the way its pricing model works in regards to log data retention, makes it an interesting choice for small, cloud-only organizations starting with a small number of logs.
XpoLog Log Analysis Platform
XpoLog is a fully automated log management platform, which makes use of AI to learn your environment and warns you about potential problems.
XpoLog is a feature-rich platform that is easy to maintain and deploy. It contains a marketplace featuring apps for a wide array of platforms.
The tool offers algorithms that automate analysis. Its AI-powered analysis layer allows teams to discover issues quicker.
When it comes to the pricing model, XpoLog is an affordable tool with great ROI and TCO.
XpoLog has a smaller community than other solutions on this list, so finding help might be slightly harder. Also, its focus is on IT and security and less on the developer’s community like some of the competitors.
In short, XpoLog is a great solution, it is not as known as some of the more high-profile items on this list.
Who should choose XpoLog?
XpoLog is a great fit for enterprises and SMEs that look for an affordable solution with quick deployment, but make a point of having great monitoring technology for their apps and IT infrastructure.
Log Analysis Uses Cases Examples
Log analysis solutions allow you to get crucial insights in order to optimize resources, support continuous delivery of services and troubleshoot errors, detect possible risks and get to the source, system health, and more.
Here are some guides on how you can perform the investigation manually and also how you can get the insights out-of-the-box with XpoLog:
- Linux security – How to Investigate Suspected Break-in Attempts In LINUX
- Amazon Monitoring and Analysis
- Windows server security: How to Look for Suspicious Activities In Windows Servers
- Monitoring, Analyzing, and Troubleshooting Your NGINX Logs.
- AWS S3 Security: How to Secure and Audit AWS S3 Buckets?
- Apache Error Log and Access Log Analyzing and Troubleshooting
Now that you know the basics about log analysis and understand the different offers of each tool, the next step for you is to roll-up your sleeves and start doing some work.
Take a look at a log analysis tool such as XpoLog’s, and start putting log analysis to work for you ASAP.