Abd Rahman Wahid^*

Journal of Cyber Security, Vol.7, pp. 589-614, 2025, DOI:10.32604/jcs.2025.071622 - 11 December 2025

Abstract The rapid increase in cyber attacks requires accurate, adaptive, and interpretable detection and response mechanisms. Conventional security solutions remain fragmented, leaving gaps that attackers can exploit. This study introduces the HI-XDR (Hybrid Intelligent Extended Detection and Response) framework, which combines network-based Suricata rules and endpoint-based Wazuh rules into a unified dataset containing 45,705 entries encoded into 1058 features. A semantic-aware autoencoder-based anomaly detection module is trained and strengthened through adversarial learning using Projected Gradient Descent, achieving a minimum mean squared error of 0.0015 and detecting 458 anomaly rules at the 99th percentile threshold. A comparative… More >

Yiming Guo, Hongyu Ma^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3485-3505, 2025, DOI:10.32604/cmc.2025.067590 - 23 September 2025

Abstract In dynamic 5G network environments, user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching. Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device (D2D) cooperative caching, limiting the reduction of transmission latency. To address this issue, this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning. First, a Transformer-based geolocation prediction model is designed, leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.… More >

Kuncham Sreenivasa Rao¹, Rajitha Kotoju², B. Ramana Reddy³, Taher Al-Shehari⁴, Nasser A. Alsadhan⁵, Subhav Singh^6,7,8, Shitharth Selvarajan^9,10,11,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1899-1916, 2025, DOI:10.32604/cmc.2025.064728 - 29 August 2025

Abstract Protecting Supervisory Control and Data Acquisition-Industrial Internet of Things (SCADA-IIoT) systems against intruders has become essential since industrial control systems now oversee critical infrastructure, and cyber attackers more frequently target these systems. Due to their connection of physical assets with digital networks, SCADA-IIoT systems face substantial risks from multiple attack types, including Distributed Denial of Service (DDoS), spoofing, and more advanced intrusion methods. Previous research in this field faces challenges due to insufficient solutions, as current intrusion detection systems lack the necessary accuracy, scalability, and adaptability needed for IIoT environments. This paper introduces CyberFortis, a… More >

Yi-Hao Tu, Yi-Wei Ma^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4563-4582, 2025, DOI:10.32604/cmc.2025.066899 - 30 July 2025

Abstract This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network (SETL-DDQN) and an extended Gumbel distribution method, designed to optimize the Contention Window (CW) in IEEE 802.11 networks. Unlike conventional Deep Reinforcement Learning (DRL)-based approaches for CW size adjustment, which often suffer from overestimation bias and limited exploration diversity, leading to suboptimal throughput and collision performance. Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions. First, SETL adopts a DDQN architecture (SETL-DDQN) to improve Q-value estimation accuracy and enhance training stability. Second, we incorporate a… More >

Zhuoyan Xie¹, Qi Wang^1,*, Bin Kong^2,*, Shang Gao¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3013-3027, 2025, DOI:10.32604/cmc.2025.064147 - 03 July 2025

Abstract In the current era of intelligent technologies, comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring, emergency rescue, and agricultural plant protection. Owing to their exceptional flexibility and rapid deployment capabilities, unmanned aerial vehicles (UAVs) have emerged as the ideal platforms for accomplishing these tasks. This study proposes a swarm A*-guided Deep Q-Network (SADQN) algorithm to address the coverage path planning (CPP) problem for UAV swarms in complex environments. Firstly, to overcome the dependency of traditional modeling methods on regular terrain environments, this study proposes an improved cellular decomposition… More >

Wen-Tsai Sung¹, Indra Griha Tofik Isa^1,2, Sung-Jung Hsiao^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3733-3759, 2024, DOI:10.32604/cmc.2024.053634 - 12 September 2024

Abstract Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks (“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: “optimal,” “sub-optimal,” or “not-optimal” conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.… More >

Yutao Hu¹, Yuntao Zhao^1,*, Yongxin Feng², Xiangyu Ma¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 509-530, 2024, DOI:10.32604/cmc.2024.042216 - 25 April 2024

Abstract In the face of the increasingly severe Botnet problem on the Internet, how to effectively detect Botnet traffic in real-time has become a critical problem. Although the existing deep Q network (DQN) algorithm in Deep reinforcement learning can solve the problem of real-time updating, its prediction results are always higher than the actual results. In Botnet traffic detection, although it performs well in the training set, the accuracy rate of predicting traffic is as high as%; however, in the test set, its accuracy has declined, and it is impossible to adjust its prediction strategy on… More >

Asier Garmendia-Orbegozo¹, Jose David Nunez-Gonzalez^1,*, Miguel Angel Anton²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2649-2671, 2024, DOI:10.32604/cmes.2024.045912 - 11 March 2024

Abstract In a network environment composed of different types of computing centers that can be divided into different layers (clod, edge layer, and others), the interconnection between them offers the possibility of peer-to-peer task offloading. For many resource-constrained devices, the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity. In this scenario, it is worth considering transferring these tasks to resource-rich platforms, such as Edge Data Centers or remote cloud servers. For different reasons, it is more exciting and… More > Graphic Abstract

Tian Lu, Bohong Liu^*

Journal on Artificial Intelligence, Vol.5, pp. 113-129, 2023, DOI:10.32604/jai.2023.043970 - 06 November 2023

Abstract To realize a better automatic train driving operation control strategy for urban rail trains, an automatic train driving method with improved DQN algorithm (classical deep reinforcement learning algorithm) is proposed as a research object. Firstly, the train control model is established by considering the train operation requirements. Secondly, the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem. Finally, the priority experience playback and “restricted speed arrival time” are used to reduce the useless experience utilization. The experiments are carried out to verify the train operation strategy method by simulating More >

Zhiwei Lin¹, Tianding Chen^1,*, Yingtao Jiang², Hui Wang¹, Shuqin Lin¹, Ming Zhu²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2145-2164, 2023, DOI:10.32604/iasc.2023.035555 - 05 January 2023

Abstract Directly applying the B-spline interpolation function to process plate cams in a computer numerical control (CNC) system may produce verbose tool-path codes and unsmooth trajectories. This paper is devoted to addressing the problem of B-spline fitting for cam pitch curves. Considering that the B-spline curve needs to meet the motion law of the follower to approximate the pitch curve, we use the radial error to quantify the effects of the fitting B-spline curve and the pitch curve. The problem thus boils down to solving a difficult global optimization problem to find the numbers and positions… More >