An Improved Intrusion Detection System Based on KNN Hyperparameter Tuning and Cross-Validation

Plant and Salamander Inspired Network Attack Detection and Data Recovery Model

The number of users of the Internet has been continuously rising, with an estimated 5.1 billion users in 2023, which comprises around 64.7% of the total world population. This indicates the rise of more connected devices to the network. On average, 30,000 websites are hacked daily, and nearly 64% of companies worldwide experience at least one type of cyberattack. As per IDC's 2022 Ransomware study, two-thirds of global organizations were hit by a ransomware attack that year. This creates the desire for a more robust and evolutionary attack detection and recovery model. One aspect of the study is the bio-inspiration models. This is because of the natural ability of living organisms to withstand various odd circumstances and overcome them with an optimization strategy. In contrast to the limitations of machine learning models with the need for quality datasets and computational availability, bio-inspired models can perform in low computational environments, and their performances are designed to evolve naturally with time. This study concentrates on exploring the evolutionary defence mechanism in plants and understanding how plants react to any known external attacks and how the response mechanism changes to unknown attacks. This study also explores how regenerative models, such as salamander limb regeneration, could build a network recovery system where services could be automatically activated after a network attack, and data could be recovered automatically by the network after a ransomware-like attack. The performance of the proposed model is compared to open-source IDS Snort and data recovery systems such as Burp and Casandra.

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well in handling large datasets such as the Bot-IoT dataset, which represents a huge amount of data about botnet attacks on IoT networks. Therefore, improving the kNN performance in classifying IoT botnet attacks is the main concern in this study by applying several feature selection techniques. The whole research process was conducted in the Rapidminer environment using three prebuilt feature selection techniques, namely, Information Gain, Forward Selection, and Backward Elimination. After comparing accuracy, precision, recall, F1 score and processing time, the combination of the kNN algorithm and the Forward Selection technique (kNN-FS) achieves the best results among others, with the highest level of accuracy and the fastest execution time among others. Finally, kNN-FS is used in developing SUKRY, which stands for Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi.