Malware Detection Techniques and Their Use in EDR and EPP

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What Is Malware Detection, Exactly?

Threat actors use destructive software, also referred to as malware, to carry out specific hostile tasks. 

There are many different types of malware, each with a distinct set of objectives. For instance, spyware seeks to gather data from computers, trojans try to maintain a long-term hold on a computer, and ransomware tries to encrypt data and demand ransom from its owners.

Utilizing techniques and tools, malware detection comprises locating, thwarting, warning of, and responding to malware threats. 

Basic malware detection techniques like checksumming, application allowlisting, and signature-based detection can help identify and limit known dangers. 

Advanced malware detection solutions utilize machine learning and artificial intelligence (AI) to actively seek for and identify brand-new malware threats.

Methods for Detecting Malware

Signature-Based Detection

In signature-based detection, software programs that are operating on a protected system leave a distinct digital trail, or signature. 

Software is scanned by antivirus programs, which look for signatures and compare them to known malware signatures.

Antivirus solutions rely on a sizable database of recognized malware signatures, which is typically updated by the security research team of the antivirus vendor. The most recent version of this database is synchronized with secured devices on a regular basis.

The process is terminated and the software is quarantined or deleted when an antivirus program finds software that fits a recognized signature. 

This is a quick and effective method for finding malware as the first line of defense. The signature-based technique will, however, be unable to identify a wide variety of unique threats as attackers become more cunning.

Checksumming

In this technique, cyclic redundancy check (CRC) checksums are computed as a form of signature analysis. By using checksums, files are made sure not to be corrupt. 

Checksumming seeks to mitigate the fundamental drawback of signature-based detection, which is that it produces a vast database of false positives.

Hackers routinely use polymorphic harmful advertising to avoid being discovered by signature-based identification techniques. 

Consistent search strings can be removed by polymorphic viruses during replication. Typically, hackers encrypt non-constant keys to represent unpredictable decryption command sets in the viral code.

As a result, the virus no longer has the code fragment and cannot be found when a malicious signature is discovered by the security team. Other harmful code detection methods must be utilized because the variable code lacks a detectable signature. Examples include:

  • A statistical analysis looks at the frequency of processor operations to ascertain whether a file is infected.
  • Cryptoanalysis – known plaintext In cryptanalysis, viruses that are encoded are decoded using an equation system (like the classic cryptographic technique of decoding text without a decryption key). The decryption program’s algorithm and keys are rebuilt by the cryptanalysis system, which then uses the method to decode sections of the virus’s overall body.
  • Heuristics: To find odd activity, a malware detection feed team scans and examines behavioral data. The group must search for malicious code associated with odd activity, such as code that is delivered to tens of thousands of users in a matter of minutes. The security team can then organize inquiries into suspected incidents according to priority.
  • Reduced masks – in order to minimize the need for an encryption key, the malware detection team may use sections of the encrypted virus body while obtaining static code. The malware’s signature or mask may be revealed by the generated static code.

Application Allowlisting

Application allowlists, often called whitelists or blacklists, are the opposite of attack signature strategies. 

The antivirus program preserves a list of approved apps and blocks everything else rather than specifying which software to forbid.

Although this method is not perfect, it can be quite effective in high-security settings. It is quite common for trustworthy apps to have security weaknesses or extra features that increase the attack surface. 

The application may be risk-free in some situations, but its use could put the device in danger. For instance, in some settings, email and web browsing may need to be stopped.

Task-focused devices, such as web servers and internet of things (IoT) gadgets, are best for using application enable listing.

Machine Learning for Behavioral Analysis

The methods mentioned above are referred to be “static” detection methods since they rely on binary criteria that can either match or not match a process that is already operating in the environment. 

Static malware detection is incapable of learning; to increase its area of coverage over time, it can either add new rules or tweak its existing ones.

On the other hand, new dynamic techniques based on AI and ML can help security systems learn to distinguish between trustworthy and malicious files and processes, even if they do not fit any known pattern or signature. 

By keeping an eye on file activity, network traffic, process frequency, deployment patterns, and other elements, they are able to achieve this. 

Over time, these algorithms can figure out what “bad” files look like, which enables them to recognize fresh infections.

AI/ML malware detection is referred to as “behavioral” detection since it is based on an examination of the actions of suspicious processes. 

These algorithms have a threshold for malicious activity, and if a file or process displays unusual behavior above the threshold, it is identified as malicious.

Although behavioral analysis is useful, on occasion it may fail to detect malicious behavior or incorrectly classify harmless processes as harmful. Attackers can also obstruct AI/ML training procedures. 

In other cases, attackers trained a behavioral analysis system to recognize malicious software as safe by feeding it specially produced artifacts.

Advanced malware prevention is offered by Cynet.

The Cynet 360 Advanced Threat Detection and Response platform defends against advanced malware, trojans, and advanced persistent threats (APTs), which are immune to conventional signature-based security solutions.

Avoid exploitative actions.

Cynet searches endpoint memory for potentially dangerous behavioral patterns, like an unexpected request for process handling. 

The vast majority of known and unknown exploits fit these patterns, and they offer effective defense even against zero-day vulnerabilities.

Stop using exploit-derived malware.

In addition to machine learning-based static analysis, sandboxing, and process activity monitoring, Cynet provides multi-layered malware prevention. Additionally, they provide threat intelligence and fuzzy hashing. 

This implies that Cynet will prevent a successful zero-day exploit from executing, guaranteeing that no harm is done even if the exploit connects to the attacker and downloads additional malware.

Identify concealed threats

To identify risks at every level of the attack chain, Cynet uses an adversary-centric technique. By detecting patterns and signs across endpoints, files, people, and networks, Cynet views itself as an adversary. 

Regardless of where an attack tries to infiltrate, they offer a thorough overview of its activities.

Exact and precise

With the help of a powerful correlation engine, Cynet’s attack findings are free of excessive noise and yield almost no false positives. Security personnel’s response is made easier as a result, enabling them to react to urgent situations.

Your security teams will have a simple yet extremely effective means to identify, stop, and react to sophisticated attacks before they do damage if you choose to use automatic or manual remediation.

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