Manzheng Yuan^1,2, Kai Yang^2,*

Journal of Cyber Security, Vol.7, pp. 197-219, 2025, DOI:10.32604/jcs.2025.066089 - 07 July 2025

Abstract Currently, network intrusion detection systems (NIDS) face significant challenges in feature redundancy and high computational complexity, which hinder the improvement of detection performance and significantly reduce operational efficiency. To address these issues, this paper proposes an innovative weighted feature selection method combining mutual information and Extreme Gradient Boosting (XGBoost). This method aims to leverage their strengths to identify crucial feature subsets for intrusion detection accurately. Specifically, it first calculates the mutual information scores between features and target variables to evaluate individual discriminatory capabilities of features and uses XGBoost to obtain feature importance scores reflecting their… More >

Khadija Bouzaachane^1,*, El Mahdi El Guarmah², Abdullah M. Alnajim³, Sheroz Khan⁴

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2391-2410, 2025, DOI:10.32604/cmc.2025.065031 - 03 July 2025

Abstract The rapid increase in the number of Internet of Things (IoT) devices, coupled with a rise in sophisticated cyberattacks, demands robust intrusion detection systems. This study presents a holistic, intelligent intrusion detection system. It uses a combined method that integrates machine learning (ML) and deep learning (DL) techniques to improve the protection of contemporary information technology (IT) systems. Unlike traditional signature-based or single-model methods, this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification. This combination provides a more nuanced and adaptable defense. The research utilizes the NF-UQ-NIDS-v2… More >

Junbin He^1,2, Wuxia Zhang³, Xianyi Liu¹, Jinping Liu^2,*, Guangyi Yang⁴

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1227-1252, 2025, DOI:10.32604/cmc.2025.064402 - 09 June 2025

Abstract The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System (ICPS), enhancing intelligence and autonomy. However, this transition also expands the attack surface, introducing critical security vulnerabilities. To address these challenges, this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection. Specifically, an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering (IVB-NCA-NLKF) method is developed to model nonlinear system dynamics, enabling optimal state estimation in multi-sensor ICPS environments. Intrusions within the physical sensing system are identified by More >

Jun Wang^1,2, Chaoren Ge^1,2, Yihong Li^1,2, Huimin Zhao^1,2, Qiang Fu^1,2,*, Kerang Cao^1,2, Hoekyung Jung^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5129-5153, 2025, DOI:10.32604/cmc.2025.062094 - 19 May 2025

Abstract Network Intrusion Detection System (NIDS) detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments. To improve the detection capability of minority-class attacks, this study proposes an intrusion detection method based on a two-layer structure. The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic, majority class attacks, and merged minority class attacks. The second layer further segments the minority class attacks through Stacking ensemble learning. The datasets are selected from the generic network dataset CIC-IDS2017, NSL-KDD, and the… More >

Federica Uccello^1,2, Marek Pawlicki^3,4, Salvatore D'Antonio¹, Rafał Kozik^3,4, Michał Choraś^3,4,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1607-1621, 2025, DOI:10.32604/cmc.2025.062801 - 16 April 2025

Abstract The growing sophistication of cyberthreats, among others the Distributed Denial of Service attacks, has exposed limitations in traditional rule-based Security Information and Event Management systems. While machine learning–based intrusion detection systems can capture complex network behaviours, their “black-box” nature often limits trust and actionable insight for security operators. This study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules, thereby enhancing the detection of Distributed Denial of Service (DDoS) attacks. The proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules More >

Yue Yang^1,2, Xiangyan Tang^2,3,*, Zhaowu Liu^2,3,*, Jieren Cheng^2,3, Haozhe Fang³, Cunyi Zhang³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4389-4408, 2025, DOI:10.32604/cmc.2025.060357 - 06 March 2025

Abstract With the rapid development of Internet of Things technology, the sharp increase in network devices and their inherent security vulnerabilities present a stark contrast, bringing unprecedented challenges to the field of network security, especially in identifying malicious attacks. However, due to the uneven distribution of network traffic data, particularly the imbalance between attack traffic and normal traffic, as well as the imbalance between minority class attacks and majority class attacks, traditional machine learning detection algorithms have significant limitations when dealing with sparse network traffic data. To effectively tackle this challenge, we have designed a lightweight… More >

Jizhao Liu, Minghao Guo^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 817-842, 2025, DOI:10.32604/cmc.2024.057660 - 03 January 2025

Abstract The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats. Intrusion detection systems are crucial to network security, playing a pivotal role in safeguarding networks from potential threats. However, in the context of an evolving landscape of sophisticated and elusive attacks, existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts. To address these issues, this paper proposes a real-time network intrusion detection method based on… More >

Zhenxiang He^*, Xunxi Wang, Chunwei Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607 - 30 January 2024

Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long… More >

Lelisa Adeba Jilcha¹, Deuk-Hun Kim², Julian Jang-Jaccard³, Jin Kwak^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3261-3284, 2023, DOI:10.32604/csse.2023.037615 - 03 April 2023

Abstract Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces… More >

Fahad Mazaed Alotaibi^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2479-2490, 2023, DOI:10.32604/cmc.2023.033792 - 31 March 2023

Abstract With the progress of advanced technology in the industrial revolution encompassing the Internet of Things (IoT) and cloud computing, cyberattacks have been increasing rapidly on a large scale. The rapid expansion of IoT and networks in many forms generates massive volumes of data, which are vulnerable to security risks. As a result, cyberattacks have become a prevalent and danger to society, including its infrastructures, economy, and citizens’ privacy, and pose a national security risk worldwide. Therefore, cyber security has become an increasingly important issue across all levels and sectors. Continuous progress is being made in More >