School of Information Technology and Engineering

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Browsing School of Information Technology and Engineering by Author "Admasu Awash"

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    Optimizing Intrusion Detection Systems with Ensemble Deep Learning: A Comparative Study of RNN and LSTM Architectures
    (Addis Ababa University, 2024-10) Admasu Awash; Henock Mulugeta (PhD)
    Nowadays, due to the complexity and severity of security attacks on computer networks attackers can launch a variety of attacks against organizational networks using a variety of methods in order to access, modify, or delete crucial data. The rise in cyberattacks has made it necessary to create reliable and effective intrusion detection systems (IDS) that can instantly recognize malicious activity. IDS, which can automatically and quickly detect and categorize cyberattacks at host and network levels, has made substantial use of machine learning techniques. Although ML techniques like K Nearest Neighbor and Support Vector Machines have been used to building IDSs, those systems still have a high false alarm rate and poor accuracy. Many security researchers are integrating different machine learning approaches to protect the data and reputation of the organizations. Deep learning algorithms have emerged as a forceful instrument in this field and these can detect with better precision than conventional techniques. Recently, Deep learning has become more well-known in network-based intrusion detection systems, enhancing their efficiency in safeguarding hosts and computer networks. In the field of deep learning, ensemble learning has appeared as a potent method that improves the performance of single models by combining several of them. The present study employed two architectures of recurrent neural networks (RNNs), namely simple recurrent neural networks and long shortterm memory (LSTM), in order to investigate the possible applicability of ensemble learning in intrusion detection systems (IDS). RNNs are suited for predicting sequential data in IDS by identifying temporal relations in network traffic. LSTMs, which are a kind of RNN, can deal with long-term dependencies well and help avoid vanishing gradient problem that is important in identifying complicated intrusion model.The performance of designed model and the IDS were evaluated using LITNET2020 publicly available dataset under performance evaluation metrics. In multiclass classification the ensemble model fared better than LSTM, yielding accuracy and precious 99.981% and 99.965%, respectively, whereas LSTM provided accuracy and precious of 99.638% and 99.451 %, respectively. Additionally, the suggested ensemble approach produced superior in multi-classification results for the various types of intrusions.