Distributed denial of service (DDOS) attacks and mitigation method using logistic regression-based GoogLeNet for real time in security games

Abstract

Distributed Denial of Service (DDoS) attacks remain a persistent and formidable threat in the ever-changing world of cyber security. These attacks have the potential to disrupt internet services and cause substantial financial and reputational concerns. The major challenge is developing an adaptable and real-time Intrusion Detection System (IDS) that can detect and neutralize DDoS attacks effectively and quickly, even when attackers use increasingly advanced ways to avoid detection. The problem concerns the development of a dynamic and real-time intrusion detection technique that combines the benefits of logistic regression for anomaly detection with GoogLeNet for deep learning-based network traffic analysis. This paper proposes a unique framework for intrusion detection that blends logistic regression-based anomaly detection with GoogLeNet deep learning capabilities. The combination of these technologies makes it easier to identify and mitigate DDoS attacks, hence improving the security of internet-based systems. The proposed IDS framework utility is proved through experimental evaluations, which highlight its capacity to effectively identify DDoS attacks while minimizing false positives. The use of this technology in real-time during security games demonstrates its potential to improve online service security infrastructure and reduce the impact of DDoS attacks on critical networks and data resources.