Intelligent Intrusion Detection Systems for Mitigating Cyber Attacks: A Comprehensive Review

As the digital world continues to grow more integrated, so too are the threats to cybersecurity, including Distributed Denial of Service (DDoS) and botnet attacks, which are among the most concerning. These attacks harness already compromised digital devices which flood the target with traffic, making them exceedingly difficult to detect and mitigate with standard Intrusion Detection Systems (IDS). The current review examines the state of the art in intelligent and adaptive IDS with a focus on machine learning (ML) and deep learning (DL) algorithms and hybrid feature selection that alleviate the systems weaknesses to DDoS and botnet assaults. The author analyzes models based on ensemble learning methods, including Random Forest (RF), XGBoost, and LightGBM, as well as advanced deep learning with Convolutional Neural (CNN) and Long Short-Term Memory (LSTM) networks, in relation to the CIS-DDoS2019, CTU-13, and BoT-IoT datasets. In addition to the essential complement of real-time detection and adaptive anomaly detection, the feedback loops in systems which are essential to mounting a defense to the multiple and ever-changing aspects of cyber threats are discussed. There is a focus on understanding and analyzing the issues connected to the relevance of research, such as scalability, false positive reduction, adversarial resilience, and the challenges of implementation in diversified IoT and cloud environments. Furthermore, the review identifies the integration of Software Defined Networking (SDN) and federated learning for privacy-preserving and collaborative threat detection as possible future avenues. This review provided a comprehensive and balanced overview of intelligent IDS systems by juxtaposing the current developments with the existing gaps, offering valuable insights for both scholars and practitioners working on cyber security systems that are adaptive, resilient, and scalable. Therefore, responding to the questions posed in the review will necessitate considerable ingenuity