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Search Results (190)
  • Open Access

    ARTICLE

    Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

    Fei Li1, *, Jiayan Zhang1, Edward Szczerbicki2, Jiaqi Song1, Ruxiang Li 1, Renhong Diao1

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 653-681, 2020, DOI:10.32604/cmc.2020.011264

    Abstract The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to verify the accuracy and efficiency… More >

  • Open Access

    ARTICLE

    Applying Stack Bidirectional LSTM Model to Intrusion Detection

    Ziyong Ran1, Desheng Zheng1, *, Yanling Lai1, Lulu Tian2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 309-320, 2020, DOI:10.32604/cmc.2020.010102

    Abstract Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data… More >

  • Open Access

    ARTICLE

    Using Object Detection Network for Malware Detection and Identification in Network Traffic Packets

    Chunlai Du1, Shenghui Liu1, Lei Si2, Yanhui Guo2, *, Tong Jin1

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1785-1796, 2020, DOI:10.32604/cmc.2020.010091

    Abstract In recent years, the number of exposed vulnerabilities has grown rapidly and more and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In this paper, aiming to detect… More >

  • Open Access

    ARTICLE

    High Speed Network Intrusion Detection System (NIDS) Using Low Power Precomputation Based Content Addressable Memory

    R. Mythili1, *, P. Kalpana2

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1097-1107, 2020, DOI:10.32604/cmc.2020.08396

    Abstract NIDS (Network Intrusion Detection Systems) plays a vital role in security threats to computers and networks. With the onset of gigabit networks, hardware-based Intrusion Detection System gains popularity because of its high performance when compared to the software-based NIDS. The software-based system limits parallel execution, which in turn confines the performance of a modern network. This paper presents a signature-based lookup technique using reconfigurable hardware. Content Addressable Memory (CAM) is used as a lookup table architecture to improve the speed instead of search algorithms. To minimize the power and to increase the speed, precomputation based CAM (PBCAM) can be used,… More >

  • Open Access

    ARTICLE

    A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data

    Xiaonian Wu1,*, Chuyun Zhang3, Runlian Zhang2, Yujue Wang2, Jinhua Cui4

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 61-72, 2018, DOI: 10.3970/cmc.2018.02449

    Abstract There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as… More >

  • Open Access

    ARTICLE

    A Hierarchy Distributed-Agents Model for Network Risk Evaluation Based on Deep Learning

    Jin Yang1, Tao Li1, Gang Liang1,*, Wenbo He2, Yue Zhao3

    CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.1, pp. 1-23, 2019, DOI:10.32604/cmes.2019.04727

    Abstract Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic, which is especially relevant in Network Intrusion Detection. In this paper, as enlightened by the theory of Deep Learning Neural Networks, Hierarchy Distributed-Agents Model for Network Risk Evaluation, a newly developed model, is proposed. The architecture taken on by the distributed-agents model are given, as well as the approach of analyzing network intrusion detection using Deep Learning, the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented, and the hierarchical evaluative framework for Network Risk Evaluation of the proposed… More >

  • Open Access

    ARTICLE

    Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

    Ling Tan1,*, Chong Li2, Jingming Xia2, Jun Cao3

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 275-288, 2019, DOI:10.32604/cmc.2019.03735

    Abstract Due to the widespread use of the Internet, customer information is vulnerable to computer systems attack, which brings urgent need for the intrusion detection technology. Recently, network intrusion detection has been one of the most important technologies in network security detection. The accuracy of network intrusion detection has reached higher accuracy so far. However, these methods have very low efficiency in network intrusion detection, even the most popular SOM neural network method. In this paper, an efficient and fast network intrusion detection method was proposed. Firstly, the fundamental of the two different methods are introduced respectively. Then, the self-organizing feature… More >

  • Open Access

    ARTICLE

    An Intrusion Detection Algorithm Based on Feature Graph

    Xiang Yu1, Zhihong Tian2, Jing Qiu2,*, Shen Su2,*, Xiaoran Yan3

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 255-274, 2019, DOI:10.32604/cmc.2019.05821

    Abstract With the development of Information technology and the popularization of Internet, whenever and wherever possible, people can connect to the Internet optionally. Meanwhile, the security of network traffic is threatened by various of online malicious behaviors. The aim of an intrusion detection system (IDS) is to detect the network behaviors which are diverse and malicious. Since a conventional firewall cannot detect most of the malicious behaviors, such as malicious network traffic or computer abuse, some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches. However, there are very few… More >

  • Open Access

    ARTICLE

    MalDetect: A Structure of Encrypted Malware Traffic Detection

    Jiyuan Liu1, Yingzhi Zeng2, Jiangyong Shi2, Yuexiang Yang2,∗, Rui Wang3, Liangzhong He4

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 721-739, 2019, DOI:10.32604/cmc.2019.05610

    Abstract Recently, TLS protocol has been widely used to secure the application data carried in network traffic. It becomes more difficult for attackers to decipher messages through capturing the traffic generated from communications of hosts. On the other hand, malwares adopt TLS protocol when accessing to internet, which makes most malware traffic detection methods, such as DPI (Deep Packet Inspection), ineffective. Some literatures use statistical method with extracting the observable data fields exposed in TLS connections to train machine learning classifiers so as to infer whether a traffic flow is malware or not. However, most of them adopt the features based… More >

  • Open Access

    ARTICLE

    Multi-VMs Intrusion Detection for Cloud Security Using Dempster-shafer Theory

    Chak Fong Cheang1,*, Yiqin Wang1, Zhiping Cai2, Gen Xu1

    CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 297-306, 2018, DOI:10.32604/cmc.2018.03808

    Abstract Cloud computing provides easy and on-demand access to computing resources in a configurable pool. The flexibility of the cloud environment attracts more and more network services to be deployed on the cloud using groups of virtual machines (VMs), instead of being restricted on a single physical server. When more and more network services are deployed on the cloud, the detection of the intrusion likes Distributed Denial-of-Service (DDoS) attack becomes much more challenging than that on the traditional servers because even a single network service now is possibly provided by groups of VMs across the cloud system. In this paper, we… More >

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