Can Characteristic-Based AI Fight Malware?
Companies should adopt machine learning-based AI that depends on algorithms to detect, prevent, and mitigate malicious files and code based on characteristics.
Unused and cobbled-together technology increases cyber-security noise and could cause vulnerabilities, and C-level executives suffer from “security solution fatigue” because they must constantly evaluate products and cope with failures.
Organizations struggle to maintain and defend their assets, but cyber-criminals dedicate all their resources toward developing innovative new attack tools
Without defense-grade machine learning-based AI security solutions, critical infrastructure will lose the battle for cyber-space.
Most new malware includes intelligent deception, obfuscation and evasion components. It can alter its signature, regulate activities, generate lures, self-propagate, deliver other malware and maximize damage while minimizing its footprint.
Cyber-security for critical infrastructure should rely on innovative machine learning-based AI anti-malware solutions that do not operate based on signatures or heuristics.
Avoid vendors with solutions that use imprecise algorithms that don’t draw from large enough data pools or don’t analyze files according to enough features.
Some solution providers tout machine learning capabilities, but they really only offer exception-derived signatures to generic templates.
Instead of operating based on signatures or heuristics, solutions should be predictive and preventive, and should detect and mitigate threats before execution.
Machine learning AI endpoint security solutions should preempt and mitigate known and unknown malicious files and code based on characteristics.
Solutions should be able to scale to protect vital systems.
Detect and prevent authentication attacks using brute-force to access a data resource or sensitive system.
Monitor network traffic.
Detect applications that scan for network vulnerabilities.