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  • Open Access

    ARTICLE

    Wake-Up Security: Effective Security Improvement Mechanism for Low Power Internet of Things

    Sun-Woo Yun1, Na-Eun Park1, Il-Gu Lee1,2,*

    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 >

  • Open Access

    ARTICLE

    Anomaly Detection for Cloud Systems with Dynamic Spatiotemporal Learning

    Mingguang Yu1,2, Xia Zhang1,2,*

    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 >

  • Open Access

    ARTICLE

    Anomaly Detection and Access Control for Cloud-Edge Collaboration Networks

    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 >

  • Open Access

    ARTICLE

    Anomalous Situations Recognition in Surveillance Images Using Deep Learning

    Qurat-ul-Ain Arshad1, Mudassar Raza1, Wazir Zada Khan2, Ayesha Siddiqa2, Abdul Muiz2, Muhammad Attique Khan3,*, Usman Tariq4, Taerang Kim5, Jae-Hyuk Cha5,*

    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 >

  • Open Access

    ARTICLE

    Unsupervised Anomaly Detection Approach Based on Adversarial Memory Autoencoders for Multivariate Time Series

    Tianzi Zhao1,2,3,4, Liang Jin1,2,3,*, Xiaofeng Zhou1,2,3, Shuai Li1,2,3, Shurui Liu1,2,3,4, Jiang Zhu1,2,3

    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 >

  • Open Access

    ARTICLE

    Unsupervised Log Anomaly Detection Method Based on Multi-Feature

    Shiming He1, Tuo Deng1, Bowen Chen1, R. Simon Sherratt2, Jin Wang1,*

    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 >

  • Open Access

    ARTICLE

    Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

    Adnan Hasan Bdair Aighuraibawi1,2, Selvakumar Manickam1,*, Rosni Abdullah3, Zaid Abdi Alkareem Alyasseri4,5, 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 >

  • Open Access

    ARTICLE

    Fine-Grained Multivariate Time Series Anomaly Detection in IoT

    Shiming He1,4, Meng Guo1, Bo Yang1, Osama Alfarraj2, Amr Tolba2, Pradip Kumar Sharma3, Xi’ai Yan4,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5027-5047, 2023, DOI:10.32604/cmc.2023.038551

    Abstract Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions. However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD). Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown. Therefore, to tackle the FGAD problem, this paper first verifies that the TSAD methods achieve low… More >

  • Open Access

    ARTICLE

    RRCNN: Request Response-Based Convolutional Neural Network for ICS Network Traffic Anomaly Detection

    Yan Du1,2, Shibin Zhang1,2,*, Guogen Wan1,2, Daohua Zhou3, Jiazhong Lu1,2, Yuanyuan Huang1,2, Xiaoman Cheng4, Yi Zhang4, Peilin He5

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5743-5759, 2023, DOI:10.32604/cmc.2023.035919

    Abstract Nowadays, industrial control system (ICS) has begun to integrate with the Internet. While the Internet has brought convenience to ICS, it has also brought severe security concerns. Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts. They are not aimed at the original network data, nor can they capture the potential characteristics of network packets. Therefore, the following improvements were made in this study: (1) A dataset that can be used to evaluate anomaly detection algorithms is produced, which provides raw network data. (2) A request response-based convolutional neural… More >

  • Open Access

    ARTICLE

    Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques

    Noura Alenezi, Ahamed Aljuhani*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2899-2915, 2023, DOI:10.32604/csse.2023.036657

    Abstract The rapid growth of the Internet of Things (IoT) in the industrial sector has given rise to a new term: the Industrial Internet of Things (IIoT). The IIoT is a collection of devices, apps, and services that connect physical and virtual worlds to create smart, cost-effective, and scalable systems. Although the IIoT has been implemented and incorporated into a wide range of industrial control systems, maintaining its security and privacy remains a significant concern. In the IIoT contexts, an intrusion detection system (IDS) can be an effective security solution for ensuring data confidentiality, integrity, and availability. In this paper, we… More >

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