Lei Wang¹, Hongji Luo², Yong Heng², Jingnan Tang², Xiaochuan Ju², Jianwei An^1,*, Haitao Xu¹, Xianwei Zhou¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082418 - 15 June 2026

Abstract In intelligent connected vehicles (ICVs) system, driving users connect to service providers (SPs) to obtain location-based services (LBS). Users transmit large volumes of encrypted sensitive information related to their itineraries to SPs to access value-added services. Attackers may launch chosen-ciphertext attacks (CCA) against SPs by exploiting the malleability of homomorphic encryption. This enables adversaries to infer or steal private key information, thereby threatening the long-term privacy of user data. Furthermore, existing key management technologies in ICVs system predominantly rely on passive defense strategies and suffer from limitations such as single protection mechanisms, delayed updates, and More >

Asma Sattar¹, Maryam Bukhari², M. Saud Khan³, Anam Mustaqeem⁴, Mi Young Lee⁵, Seungmin Rho^5,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078413 - 15 June 2026

Abstract Video surveillance systems play an important role in maintaining security in smart city environments. In this context, person identification (Re-ID) systems based on deep learning are currently drawing substantial academic interest. However, these systems remain vulnerable to adversarial attacks. In existing methods, several attacks against Re-ID systems have been designed; nevertheless, they operate in the spatial domain. Existing attacks often suffer from perturbation visibility and low imperceptibility, making them easily detectable by human observers or automated detection systems. From this line of research, this study proposed a novel and potent alternative by designing frequency domain… More >

Darlington Chigozie Okeke^*

Journal of Cyber Security, Vol.8, pp. 319-356, 2026, DOI:10.32604/jcs.2026.080477 - 08 June 2026

Abstract Background: The evolution of modern networked systems in complexity, volume, and diversity has markedly increased the cyber-attack area. Conventional signature-based intrusion detection systems (IDS) will no longer be adequate for identifying advanced threats. A data-driven, adaptive approach that can identify malicious network activity is provided by machine learning (ML) techniques. This review aims to study, compare, and analyze ML-based approaches in IDS and improve the security defense mechanism. Methods: This systematic review followed the PRISMA 2020 guidelines. ML-based IDS peer-reviewed papers were identified from five scientific databases. Abstracts, full texts, and titles were filtered using… More >

Darlington Chigozie Okeke^*

Journal of Cyber Security, Vol.8, pp. 281-317, 2026, DOI:10.32604/jcs.2026.079750 - 29 May 2026

Abstract Phishing has become the most common cybersecurity threat and increasingly exploits human factors rather than technical vulnerabilities. This study examined the relationships between cybersecurity awareness, training frequency, user cyber-hygiene behaviour, organisational culture, risk perception, and self-reported phishing vulnerability and the theoretical basis of this research is the Technology Threat Avoidance Theory (TTAT). A quantitative correlational design was used for data collection and analysis with Pearson correlation in structured questionnaires. The results indicated that the five independent variables have a significant positive relationship with phishing vulnerability. The increased awareness and regular training correlate with greater recognition… More >

Nader Karmous¹, Leila Bousbia¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077454 - 08 May 2026

Abstract This study proposes an intelligent Intrusion Detection and Prevention System (IDPS) integrated into a centralized Ryu Software-Defined Networking (SDN) controller to mitigate replay attacks within Internet of Things (IoT) environments. To address the scarcity of specialized datasets, a comprehensive dataset was generated using a real-time SDN-IoT testbed encompassing Mininet, multiple OpenFlow 1.3 switches, and a single Ryu controller. The experimental setup featured the exchange of legitimate and malicious Message Queuing Telemetry Transport (MQTT) traffic between hosts and IoT devices to simulate realistic network behaviors and attack vectors. Our methodology introduces a novel feature engineering framework… More >

Sarmad Dheyaa Azeez¹, Saadaldeen Rashid Ahmed^2,3, Muhammad Ilyas^4,*, Abu Saleh Musa Miah⁵, Fahmid Al Farid^6,7,*, Md. Hezerul Abdul Karim^6,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076283 - 08 May 2026

Abstract Traditional Intrusion Detection Systems (IDSs) that rely on fixed signatures or basic machine learning often struggle with sophisticated, multi-stage cyberattacks and previously unknown threats. To fix these problems, this paper introduces IntrusionNet, a mixed deep learning system that combines Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders in a two-part design. Differing from typical stacked models, IntrusionNet works on two levels at the same time. First, a supervised CNN-RNN process pulls spatial-temporal data from traffic flows to sort well-known attack patterns. Second, an unsupervised Autoencoder process spots new anomalies by looking at reconstruction… More >

Benedict Djouboussi^1,*, Elie Fute Tagne^1,2

Journal of Cyber Security, Vol.8, pp. 171-187, 2026, DOI:10.32604/jcs.2026.079617 - 15 April 2026

Abstract The Distributed Network Protocol 3 (DNP3) is widely deployed in SCADA-based microgrids; however, it was not originally designed to meet the cybersecurity requirements of modern decentralized energy infrastructures. Although DNP3 Secure Authentication (DNP3-SA) introduces HMAC-based session-level protection, it does not ensure fragment-level integrity, leaving the protocol vulnerable to fragmentation disruption, replay attacks, and sequence manipulation. Such vulnerabilities can cause desynchronization between master and outstation devices, compromising the operational reliability of distributed energy resources. This paper proposes DNP3Chain, a blockchain-enabled framework that provides real-time fragment-level validation and enforces end-to-end message integrity in DNP3 communications. An OpenDNP3-based… More >

Bakhtawar Gul¹, Atif Elahi^1,2, Tahir Saleem³, Noor Gul¹, Fahad Algarni⁴, Insaf Ullah^5,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.069776 - 12 March 2026

Abstract Orthogonal Frequency Division Multiplexing (OFDM) enables efficient Dynamic Spectrum Access (DSA) but suffers from high sidelobe that causes excessive out-of-band (OOB) emissions and expose the system to spectrum-layer cyberattacks such as man-in-the-middle (MITM), eavesdropping, and primary user emulation (PUE) attacks. To address both spectral leakage and its security implications, this paper introduces a secure and intelligent hybrid optimization strategy that combinesan Eigenspace-based Generalized Sidelobe Canceller (ES-GSC) with a Genetic Algorithm (GA), to derive optimally weighted cancellation carriers. The proposed method jointly suppresses sidelobes and reinforces resistance to leakage-based attacks. MATLAB Simulation demonstrate considerable reductions in More >

Ka-Kyung Kim, Joon-Seok Kim, Dong-Hyuk Shin, Ieck-Chae Euom^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077315 - 26 February 2026

Abstract As attack techniques evolve and data volumes increase, the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential. Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems. However, its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm. To address these concerns, this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments, examining both their benefits and associated… More > Graphic Abstract

Enzo Hoummady^*, Fehmi Jaafar

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074897 - 10 February 2026

Abstract With the proliferation of Internet of Things (IoT) devices, securing these interconnected systems against cyberattacks has become a critical challenge. Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic. This paper presents a comparative benchmark of classic machine learning (ML) and state-of-the-art deep learning (DL) algorithms for IoT intrusion detection. Our methodology employs a two-phased approach: a preliminary pilot study using a custom-generated dataset to establish baselines, followed by a comprehensive evaluation on the large-scale CICIoTDataset2023. We benchmarked algorithms including Random Forest, XGBoost, CNN, and Stacked LSTM. The… More >