Network Intrusion Detection Models based on Naives Bayes and C4.5 Algorithms

All around the world, the rapid spread of the pandemic (COVID-19) has brought an enormous challenge, especially to the ICT industry. The total lockdown which prevailed had increased the use of the internet, which is a challenge to safety and security. Thus, an Intrusion Detection System (IDS) is needed to maintain this emergence of the boundless communication paradigm. This paper proposed an optimized Network IDS by applying two machine learning algorithms in intrusion dataset and feature selection techniques to optimize the IDS model. The viability of this work is shown by comparing, the result of the model with existing work. The decision tree applied outperformed the Naïve Bayes algorithm with 89.27% and 75.09% accuracy, respectively. © 2022 IEEE.

A distributed denial of service attack detection system using long short term memory with Singular Value Decomposition

The increase in online activity during the COVID 19 pandemic has generated a surge in network traffic capable of expanding the scope of DDoS attacks. Cyber criminals can now afford to launch massive DDoS attacks capable of degrading the performances of conventional machine learning based IDS models. Hence, there is an urgent need for an effective DDoS attack detective model with the capacity to handle large magnitude of DDoS attack traffic. This study proposes a deep learning based DDoS attack detection system using Long Short Term Memory (LSTM). The proposed model was evaluated on UNSW-NB15 and NSL-KDD intrusion datasets, whereby twenty-three (23) and twenty (20) attack features were extracted from UNSW-NB15 and NSL-KDD, respectively using Singular Value Decomposition (SVD). The results from the proposed model show significant improvement when compared with results from some conventional machine learning techniques such as Naïve Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) with accuracies of 94.28% and 90.59% on both datasets, respectively. Furthermore, comparative analysis of LSTM with other deep learning results reported in literature justified the choice of LSTM among its deep learning peers in detecting DDoS attacks over a network. © 2021 IEEE.