Wei Zhang¹, Ping He^2,*, Ting Li², Fan Yang¹, Ying Liu³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1893-1910, 2023, DOI:10.32604/cmc.2023.044253

Abstract Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification. These limitations can result in the misjudgment of models, leading to a degradation in overall detection performance. This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block (CLME) to overcome the above limitations. The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations. The memory block can record normal patterns of these representations through the utilization of… More >

Adel Alkhalil^1,*, Abdulaziz Aljaloud¹, Diaa Uliyan¹, Mohammed Altameemi¹, Magdy Abdelrhman^2,3, Yaser Altameemi⁴, Aakash Ahmad⁵, Romany Fouad Mansour⁶

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2049-2078, 2023, DOI:10.32604/cmc.2023.044506

Abstract Supervisory control and data acquisition (SCADA) systems are computer systems that gather and analyze real-time data, distributed control systems are specially designed automated control system that consists of geographically distributed control elements, and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based decisions. In recent years, there has been a lot of focus on the security of industrial control systems. Due to the advancement in information technologies, the risk of cyberattacks on industrial control system has been drastically increased. Because they are so inextricably tied to human life,… More >

Ju Hyeon Lee¹, Jiho Shin², Jung Taek Seo^3,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 757-779, 2023, DOI:10.32604/cmc.2023.039461

Abstract As energy-related problems continue to emerge, the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration. Renewable energy is becoming increasingly important, with solar power accounting for the most significant proportion of renewables. As the scale and importance of solar energy have increased, cyber threats against solar power plants have also increased. So, we need an anomaly detection system that effectively detects cyber threats to solar power plants. However, as mentioned earlier, the existing solar power plant anomaly detection system monitors only operating information such as power generation, making it difficult to detect cyberattacks.… More >

Sun-Woo Yun¹, Na-Eun Park¹, Il-Gu Lee^1,2,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2897-2917, 2023, DOI:10.32604/iasc.2023.039940

Abstract As time and space constraints decrease due to the development of wireless communication network technology, the scale and scope of cyberattacks targeting the Internet of Things (IoT) are increasing. However, it is difficult to apply high-performance security modules to the IoT owing to the limited battery, memory capacity, and data transmission performance depending on the size of the device. Conventional research has mainly reduced power consumption by lightening encryption algorithms. However, it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security performance. In this study, we propose wake-up… More >

Mingguang Yu^1,2, Xia Zhang^1,2,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1787-1806, 2023, DOI:10.32604/iasc.2023.038798

Abstract As cloud system architectures evolve continuously, the interactions among distributed components in various roles become increasingly complex. This complexity makes it difficult to detect anomalies in cloud systems. The system status can no longer be determined through individual key performance indicators (KPIs) but through joint judgments based on synergistic relationships among distributed components. Furthermore, anomalies in modern cloud systems are usually not sudden crashes but rather gradual, chronic, localized failures or quality degradations in a weakly available state. Therefore, accurately modeling cloud systems and mining the hidden system state is crucial. To address this challenge, we propose an anomaly detection… More >

Bingcheng Jiang, Qian He^*, Zhongyi Zhai, Hang Su

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2335-2353, 2023, DOI:10.32604/iasc.2023.039989

Abstract Software-defined networking (SDN) enables the separation of control and data planes, allowing for centralized control and management of the network. Without adequate access control methods, the risk of unauthorized access to the network and its resources increases significantly. This can result in various security breaches. In addition, if authorized devices are attacked or controlled by hackers, they may turn into malicious devices, which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted. To solve those problems, an anomaly detection and access control mechanism based on SDN and neural… More >

Qurat-ul-Ain Arshad¹, Mudassar Raza¹, Wazir Zada Khan², Ayesha Siddiqa², Abdul Muiz², Muhammad Attique Khan^3,*, Usman Tariq⁴, Taerang Kim⁵, Jae-Hyuk Cha^5,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1103-1125, 2023, DOI:10.32604/cmc.2023.039752

Abstract Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the detection accuracy of various anomalous… More >

Tianzi Zhao^1,2,3,4, Liang Jin^1,2,3,*, Xiaofeng Zhou^1,2,3, Shuai Li^1,2,3, Shurui Liu^1,2,3,4, Jiang Zhu^1,2,3

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 329-346, 2023, DOI:10.32604/cmc.2023.038595

Abstract The widespread usage of Cyber Physical Systems (CPSs) generates a vast volume of time series data, and precisely determining anomalies in the data is critical for practical production. Autoencoder is the mainstream method for time series anomaly detection, and the anomaly is judged by reconstruction error. However, due to the strong generalization ability of neural networks, some abnormal samples close to normal samples may be judged as normal, which fails to detect the abnormality. In addition, the dataset rarely provides sufficient anomaly labels. This research proposes an unsupervised anomaly detection approach based on adversarial memory autoencoders for multivariate time series… More >

Shiming He¹, Tuo Deng¹, Bowen Chen¹, R. Simon Sherratt², Jin Wang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 517-541, 2023, DOI:10.32604/cmc.2023.037392

Abstract Log anomaly detection is an important paradigm for system troubleshooting. Existing log anomaly detection based on Long Short-Term Memory (LSTM) networks is time-consuming to handle long sequences. Transformer model is introduced to promote efficiency. However, most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing, which introduces parsing errors. They only extract simple semantic feature, which ignores other features, and are generally supervised, relying on the amount of labeled data. To overcome the limitations of existing methods, this paper proposes a novel unsupervised log anomaly detection method based on multi-feature (UMFLog). UMFLog includes… More >

Adnan Hasan Bdair Aighuraibawi^1,2, Selvakumar Manickam^1,*, Rosni Abdullah³, Zaid Abdi Alkareem Alyasseri^4,5, Ayman Khallel⁶, Dilovan Asaad Zebari⁹, Hussam Mohammed Jasim⁷, Mazin Mohammed Abed⁸, Zainb Hussein Arif⁷

Computer Systems Science and Engineering, Vol.47, No.1, pp. 553-574, 2023, DOI:10.32604/csse.2023.037948

Abstract Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system… More >