Noveela Iftikhar¹, Mujeeb Ur Rehman¹, Mumtaz Ali Shah², Mohammed J. F. Alenazi³, Jehad Ali^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 639-671, 2025, DOI:10.32604/cmes.2025.062788 - 11 April 2025

Abstract Intrusion attempts against Internet of Things (IoT) devices have significantly increased in the last few years. These devices are now easy targets for hackers because of their built-in security flaws. Combining a Self-Organizing Map (SOM) hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting (XGBoost) for multi-class classification can improve network traffic intrusion detection. The proposed model is evaluated on the NSL-KDD dataset. The hybrid approach outperforms the baseline line models, Multilayer perceptron model, and SOM-KNN (k-nearest neighbors) model in precision, recall, and F1-score, highlighting the proposed More >

Fatma S. Alrayes¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1457-1490, 2024, DOI:10.32604/cmc.2024.051996 - 18 July 2024

Abstract This study describes improving network security by implementing and assessing an intrusion detection system (IDS) based on deep neural networks (DNNs). The paper investigates contemporary technical ways for enhancing intrusion detection performance, given the vital relevance of safeguarding computer networks against harmful activity. The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset, a popular benchmark for IDS research. The model performs well in both the training and validation stages, with 91.30% training accuracy and 94.38% validation accuracy. Thus, the model shows good learning and generalization capabilities with minor losses of… More >

Fatma S. Alrayes¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, DOI:10.32604/cmc.2024.050586 - 20 June 2024

Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct… More >

Mohammed Zakariah¹, Salman A. AlQahtani^2,*, Abdulaziz M. Alawwad¹, Abdullilah A. Alotaibi³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4025-4054, 2023, DOI:10.32604/cmc.2023.043752 - 26 December 2023

Abstract Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic. By consuming time and resources, intrusive traffic hampers the efficient operation of network infrastructure. An effective strategy for preventing, detecting, and mitigating intrusion incidents will increase productivity. A crucial element of secure network traffic is Intrusion Detection System (IDS). An IDS system may be host-based or network-based to monitor intrusive network activity. Finding unusual internet traffic has become a severe security risk for intelligent devices. These systems are negatively impacted by several attacks, which are… More >