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What is Behavioral Analysis and Signature Analysis?

Forensic investigators look for patterns and this investigation often takes two forms that complement each other. The first is a very focused and efficient search for the unique sequences of bytes found inside known malware. Finding these attack signatures is fast and effective.

The second part is more general. The patterns of events like API calls, or database queries offer a historical record of what happened in the computer. Deconstructing this data can reveal how and when malicious behavior began and an attacker gained access. This analysis is more complex and time consuming, but it can be more effective at detecting new or unknown attacks.

Deployed together, the approaches can speed detection, evidence collection and analysis.

How is Behavioral Analysis used in DFIR?

Behavioral analysis focuses on examining the patterns and activities associated with the attacks that took place. The most important indicators may be found in the log files which track activity on the network or the file system. Many applications also keep logs to help with debugging and troubleshooting.

Another good source of insight can be found in the cache files which are normally used to save time by storing copies of data that may be time-consuming to compute or download from the Internet. The DNS cache, for instance, will keep a copy of all recent domain names that were visited by either the user or the system software. The browser cache will keep copies of large images or website data making it possible to reconstruct exactly what the user saw.

Many parts of the operating system also keep records of their actions which can also reveal what happened. The disk system, for instance, may contain either scraps or the entire copy of recently deleted files. These traces can help identify just what happened.

What are some sample applications where it works well?

DFIR teams often rely upon behavioral analysis to uncover new attacks using unknown techniques or malware. Searching for clues that indicate malicious actions is a good, general solution for detecting security failures.

Some common types of attacks where behavioral analysis is best are:

  1. Detecting Zero-Day Attacks: Behavioral analysis can detect new or unknown threats that lack traditional signatures, making it possible for proactive identification of emerging attacks. Some of the most obvious indications are large amounts of data flowing to unfamiliar destinations.
  2. Uncovering Malicious Insider Activity: Behavioral analysis can reveal subtle changes in user behavior that may indicate malicious intent or compromise. If a user visits a new section of the file system or the network storage, it may mean the user is searching for unauthorized access.
  3. Understanding Attacker Tactics: Analyzing behavioral patterns can provide insights into the attacker’s methods, motivations, and goals, aiding in threat hunting and mitigation strategies. Does the attacker want just a copy? Is someone attempting to change the stored data?

When is Signature Analysis used with DFIR?

Signature analysis relies on predefined signatures or patterns that are characteristic of known malware or attacks. They are often a particular pattern of bytes that correspond to the instructions or code inside of dangerous software. These signatures are compiled by security researchers after they observe previous attacks using copies of the malicious code, network traffic patterns, or file characteristics.

Security researchers maintain central databases containing well-known signatures and they will update them periodically as new threats emerge. DFIR teams may subscribe to several different signature databases.

Some automated tools like virus detectors will also use the same signatures while scanning email and downloaded files.

What are some sample strategic areas where signature analysis is key?

Signature analysis is prized for faster response times and the ability to jumpstart investigations. When teams can identify the type of threat immediately and find some historical context, they can react quickly with confidence provided by the past record.

Some areas where DFIR teams depend upon signature analysis are:

  1. Rapid Detection of Known Threats: Signature analysis can quickly identify and flag files, network packets, or system activities that match known threat signatures. Many examples of malware have been seen in the past and researchers have isolated good signatures that will flag them immediately.
  2. Speeding active collection: Some researchers use smart evidence collection packages that are able to look for malicious patterns and signatures while the data is being gathered. When malware is found during this process, mitigation can begin immediately.
  3. Reducing False Positives: Signatures are crafted to be highly specific and this allows them to effectively distinguish between legitimate and malicious activity, reducing false positives.
  4. Automating Threat Detection: Some systems are constantly scanning network and file system tracking looking for malware. Signature-based detection systems can automate the process of providing constant security.

How do Behavioral Analysis and Signature Analysis Work Together?

Behavioral analysis and signature analysis complement each other. Behavioral analysis excels at detecting new and unknown threats, while signature analysis efficiently identifies known threats. When teams deploy both together, they can deliver more comprehensive coverage.

The strategic approaches can co-evolve. Good behavior analysis lays the groundwork for creating better lists of signatures in the future. All new examples of malware must first be detected using behavioral analysis. When the files are isolated, the researchers can study them to find unique patterns that identify the malware in the future.

In specialized circumstances, signature analysis can help behavioral analysis. When researchers discover patterns in some signatures, they can isolate the patterns to help guide future behavioral analysis. For example, some malware signatures contain a particular pattern of network access. If behavioral analysts suspect a similar attack, they can look for that same pattern. It’s like a meta signature.

How is Behavioral Analysis Being Reinvented?

Researchers and DFIR teams are constantly refining their tactics, both by incorporating better software for tracking behavior and also integrating better analytical algorithms.

Some of the latest techniques include:

  1. Machine Learning Integration: Behavioral analysis is increasingly incorporating machine learning algorithms to enhance its ability to identify and classify anomalous behaviors. Machine learning models can analyze vast amounts of data to detect patterns and deviations that may indicate malicious activity.
  2. Real-time Monitoring: Behavioral analysis is moving towards real-time monitoring capabilities, enabling continuous observation and evaluation of system behavior. This real-time analysis allows for immediate detection of threats and prompt intervention to mitigate potential damage.
  3. Contextual Awareness: Behavioral analysis is incorporating contextual information to make more informed decisions. By understanding the context in which an action occurs, behavioral analysis can better distinguish between legitimate and malicious activities.
  4. Adaptive Thresholds: Behavioral analysis is adopting adaptive thresholds to adjust its sensitivity based on the current threat environment. This adaptability ensures that the system remains effective in detecting threats while minimizing false positives.

What are the latest trends in Signature Analysis?

Researchers are constantly working to create more comprehensive lists of signatures when new forms of malware appear. They’re also studying better approaches to automate the process to speed the process and make signature analysis more timely.

Some of the newer approaches include:

  1. Dynamic Signature Generation: A dynamic approach searches for common meta patterns and creates signatures automatically when files exhibit this behavior. This allows for faster detection of new and unknown threats
  2. Genetic Algorithm Optimization: Signature analysis is employing genetic algorithms which can mutate and combine signatures to produce better, more accurate signatures.
  3. Threat Intelligence Integration: Signature analysis is integrating threat intelligence.
  4. Automated Signature Updates: Signature analysis is automating the process of distributing updated signatures, reducing the time and effort required to maintain an effective defense. Automated updates ensure that the system is always protected against the latest threats.

What are some key takeaways for CSOs, CIOs and Security team leadership?

  1. Good defenses depend upon both signature and behavioral analysis.
  2. The two forms of malware detection can complement each other with signature analysis mainly targeting older threats and behavioral mainly detecting newer ones.
  3. Researchers can use both forms of analysis to inform each other, sharing patterns and general approaches.