Cyber-adversaries are becoming more sophisticated in their efforts to avoid detection, and many modern malware tools are already incorporating new ways to bypass antivirus and other threat detection measures. Because networks and organizations use sophisticated methods to detect and respond to attacks, the response can be so strong that criminals try to respond with something even stronger. The complexity of cybercriminals is increasing, combined with the widening potential of artificial intelligence (AI) attacks.

Cybersecurity, however, is at a critical juncture, and the field must focus future research efforts on cyber-attack prediction systems that can anticipate critical scenarios and outcomes, rather than relying on defensive solutions and focusing on mitigation. Computer systems around the world need systems based on a comprehensive, predictive analysis of cyber threats.

Artificial intelligence (AI), which relies heavily on machine learning (ML), has the ability to recognize patterns arising from past experiences and make predictions based on them. In recent years, swarm technology, which can use things like machine learning and artificial intelligence to attack networks and devices, has shown new potential.

Useful patterns of attack can be defined by understanding patterns of behavior, analyzing patterns and connections between malicious activities, predicting future moves, and ultimately preventing or detecting potentially malicious behavior.

The aforementioned cyber-threat prediction systems offer promising and limited possibilities, but large-scale coordinated attacks require progress on several fronts, including the detection and prediction of events generated in computer systems. Obfuscation techniques are used to bypass detection by deliberately making malicious code difficult to understand in order to bypass the detection of the network.

When assessing network security risks, hackers’ behavior must be taken into account, which can be a daunting task, given the number of known vulnerabilities and the choices an attacker could make to infiltrate a network.

#machine-learning #detection #prevention #deep learning