The Dual Impact of AI and Machine Learning: Revolutionizing Cybersecurity and Amplifying Cyber Threats
How do AI and ML enhance defensive cybersecurity capabilities?
AI and ML enhance defensive cybersecurity capabilities by efficiently processing large datasets to identify patterns and anomalies, improving intrusion detection systems' accuracy and responsiveness. They enable better asset management, risk assessment, and governance, strengthening cybersecurity infrastructures against complex modern attacks.
What techniques in AI improve intrusion detection system accuracy?
AI techniques that improve intrusion detection system accuracy include machine learning algorithms such as clustering, self-organizing maps, and classification and regression trees (CARTs). These techniques enable efficient processing of extensive datasets and effective identification of patterns and anomalies, enhancing the accuracy and responsiveness of intrusion detection systems.
How do attackers use AI and ML to sophisticate traditional cyber attacks?
Attackers use AI and ML to sophisticate traditional cyber attacks by automating and adapting them. AI-driven malware, such as Deep Locker, can bypass conventional security measures by remaining inactive until specific conditions are met. In phishing, AI enables the creation of highly targeted spear phishing campaigns that leverage AI models to mimic human communication patterns, making fraudulent messages harder to detect. AI's impact on DDoS attacks is also significant, as AI-driven botnets can adapt offensive measures and launch attacks with unprecedented sophistication.