Department of Cybersecurity and Data Science, Bethany College, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 22(01), 1983-1989
Article DOI: 10.30574/wjarr.2024.22.1.1305
Received on 20 March 2024; revised on 27 April 2024; accepted on 29 April 2024
This paper will delve into the integration of Artificial Intelligence (AI) and extended Berkeley Packet Filter (eBPF) technology to enhance cyber defense capability. The most important aspect of AI is detection of threats where it employs sophisticated algorithms in the analysis of large data sets to identify any form of pattern which signals a threat in cyberspace. It enhances behavioral analysis in the monitoring of user and system behavior to identify suspicious activities. AI also helps to instantly react against the threats by automatically taking actions like isolating an infected machine or blocking the suspicious network traffic in order to minimize the response time and consequent damage. AI can predict the attacks based on historical data and current trends and design proactive defense strategies. On the other hand. eBPF technology complements AI by providing programmable kernel tracing, real-time monitoring, low overhead, and security enhancements. Attaching eBPF programs to kernel hooks provides insights into network traffic, system events, and application behavior, intrusion detection, performance monitoring, and troubleshooting. Its implementation is still optimal with less performance impact on the system. The system retains its robust security stance due to the deployment of eBPF, enforcement of kernel-level security policies, and detection of malicious activities. This synergy between AI and eBPF would mean smarter cybersecurity solutions that can change adeptly with each emerging threat and help raise defenses for the organization.
Artificial intelligence (AI); Algorithm; Cyber Defense; Cybersecurity; eBPF
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Alex Mathew. AI Cyber Defense and eBPF. World Journal of Advanced Research and Reviews, 2024, 22(1), 1983-1989. Article DOI: https://doi.org/10.30574/wjarr.2024.22.1.1305