Machine Learning Vs. Antivirus
Antivirus tools (AV) have been available since the 1980’s. Early malware had limited functionality. Defending against attacks was mostly a task of maintaining a list of bad software was enough.
The lists of known malware that appeared in early tools are now called “Signatures”. The Signature database grows as the list of known malware expands. While time consuming, it is critical to keep antivirus tools current and updated.
As antivirus tools evolve, heuristic methods are being implemented to find similar patterns in different viruses, making it easier to detect a different version of the same virus. This is what antivirus vendors call “Families”.
Antivirus tools have also evolved over time to provide real-time scanning. Every time a new file is used, downloaded or saved to a computer, the antivirus tool scans it, and tries to identify if it matches a signature in a virus family. Once identified, it deletes or quarantines the file. These tools are still limited to looking for known malware.
Ransomware software exploits any existing vulnerability and is often triggered unintentionally by the user through phishing or other exploits. Hackers continuously change and update their applications to avoid detection. This is a technology arms race and traditional antivirus tools have a difficult time keeping up As a result, Zero-day attacks are much more intrusive and successful.
The latest attack on the Environmental Protection Agency is just one of the latest examples of how traditional antivirus tools could not stop an attack. Hackers are using more advanced techniques (including Machine Learning) to penetrate networks, identify valuable content and vulnerable systems, and then attack. Machine Learning must be used to defend networks and devices.
To stop ransomware attacks, it is not sufficient to look for signatures and similar families. Artificial Intelligence is enabling information security teams to create new strategies and methods to detect an attack.
The Hackerstrike Machine Learning algorithms are trained to detect patterns of behavior that malware must execute in order to successfully encrypt data.
Hackerstrike is able to identify and stop processes based on abnormal behavior, not simply based on the file names or techniques used by the hacker.
One of the main objectives of Machine Learning is to go beyond detecting known issues and be able to predict attacks in advance. The algorithm can anticipate and react before the attack is successful. New attacks can be stopped even if the hackers have found new methods and approaches.
Artificial Intelligence and Machine Learning are the new technologies enabling both hackers and security teams new ways to both attack and defend systems. Legacy products such as antivirus are trying to stay current, but these older systems have much greater overhead, performance degradation and complexity for administrators and users.
- Legacy systems
- Based on Signatures
- Heuristics based on malware families
- Struggle with Zero-day attacks
- Catch up with new technologies (ML) affects performance
- New technology used by attackers and defenders
- Able to keep up with new types of attacks
- Detects and predicts: stops attack before damage is done
- Specially designed for Zero-day attack detection
- Designed from scratch to work with ML without affecting performance
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