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  • Open Access


    Active Detecting DDoS Attack Approach Based on Entropy Measurement for the Next Generation Instant Messaging App on Smartphones

    Hsing‐Chung Chen1,2, Shyi‐Shiun Kuo1,3

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 217-228, 2019, DOI:10.31209/2018.100000057

    Abstract Nowadays, more and more smartphones communicate to each other’s by using some popular Next Generation Instant Messaging (NGIM) applications (Apps) which are based on the blockchain (BC) technologies, such as XChat, via IPv4/IPv6 dual stack network environments. Owing to XChat addresses are soon to be implemented as stealth addresses, any DoS attack activated form malicious XChat node will be treated as a kind of DDoS attack. Therefore, the huge NGIM usages with stealth addresses in IPv4/IPv6 dual stack mobile networks, mobile devices will suffer the Distributed Denial of Service (DDoS) attack from Internet. The probing More >

  • Open Access


    A DDoS Attack Information Fusion Method Based on CNN for Multi-Element Data

    Jieren Cheng1, 2, Canting Cai1, *, Xiangyan Tang1, Victor S. Sheng3, Wei Guo1, Mengyang Li1

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 131-150, 2020, DOI:10.32604/cmc.2020.06175

    Abstract Traditional distributed denial of service (DDoS) detection methods need a lot of computing resource, and many of them which are based on single element have high missing rate and false alarm rate. In order to solve the problems, this paper proposes a DDoS attack information fusion method based on CNN for multi-element data. Firstly, according to the distribution, concentration and high traffic abruptness of DDoS attacks, this paper defines six features which are respectively obtained from the elements of source IP address, destination IP address, source port, destination port, packet size and the number of… More >

  • Open Access


    A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

    Jieren Cheng1, 2, Junqi Li2, *, Xiangyan Tang2, Victor S. Sheng3, Chen Zhang2, Mengyang Li2

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1423-1443, 2020, DOI:10.32604/cmc.2020.06176

    Abstract Distributed Denial of Service (DDoS) attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security. Existing detection methods cannot effectively detect early attacks. In this paper, we propose a detection method of DDoS attacks based on generalized multiple kernel learning (GMKL) combining with the constructed parameter R. The super-fusion feature value (SFV) and comprehensive degree of feature (CDF) are defined to describe the characteristic of attack flow and normal flow. A method for calculating R based on SFV and CDF is proposed to select the combination More >

  • Open Access


    DDoS Attack Detection via Multi-Scale Convolutional Neural Network

    Jieren Cheng1, 2, Yifu Liu1, *, Xiangyan Tang1, Victor S. Sheng3, Mengyang Li1, Junqi Li1

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1317-1333, 2020, DOI:10.32604/cmc.2020.06177

    Abstract Distributed Denial-of-Service (DDoS) has caused great damage to the network in the big data environment. Existing methods are characterized by low computational efficiency, high false alarm rate and high false alarm rate. In this paper, we propose a DDoS attack detection method based on network flow grayscale matrix feature via multiscale convolutional neural network (CNN). According to the different characteristics of the attack flow and the normal flow in the IP protocol, the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary. Based on the network flow More >

  • Open Access


    Novel DDoS Feature Representation Model Combining Deep Belief Network and Canonical Correlation Analysis

    Chen Zhang1, Jieren Cheng1,2,3,*, Xiangyan Tang1, Victor S. Sheng4, Zhe Dong1, Junqi Li1

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 657-675, 2019, DOI:10.32604/cmc.2019.06207

    Abstract Distributed denial of service (DDoS) attacks launch more and more frequently and are more destructive. Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense. Most DDoS feature extraction methods cannot fully utilize the information of the original data, resulting in the extracted features losing useful features. In this paper, a DDoS feature representation method based on deep belief network (DBN) is proposed. We quantify the original data by the size of the network flows, the distribution of IP addresses and ports, and the diversity of packet sizes of More >

  • Open Access


    A DDoS Attack Situation Assessment Method via Optimized Cloud Model Based on Influence Function

    Xiangyan Tang1, Qidong Zheng1,*, Jieren Cheng1,2, Victor S. Sheng3, Rui Cao1, Meizhu Chen1

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1263-1281, 2019, DOI:10.32604/cmc.2019.06173

    Abstract The existing network security situation assessment methods cannot effectively assess the Distributed denial-of-service (DDoS) attack situation. In order to solve these problems, we propose a DDoS attack situation assessment method via optimized cloud model based on influence function. Firstly, according to the state change characteristics of the IP addresses which are accessed by new and old user respectively, this paper defines a fusion feature value. Then, based on this value, we establish a V-Support Vector Machines (V-SVM) classification model to analyze network flow for identifying DDoS attacks. Secondly, according to the change of new and… More >

  • Open Access


    An Abnormal Network Flow Feature Sequence Prediction Approach for DDoS Attacks Detection in Big Data Environment

    Jieren Cheng1,2, Ruomeng Xu1,*, Xiangyan Tang1, Victor S. Sheng3, Canting Cai1

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 95-119, 2018, DOI:10.3970/cmc.2018.055.095

    Abstract Distributed denial-of-service (DDoS) is a rapidly growing problem with the fast development of the Internet. There are multitude DDoS detection approaches, however, three major problems about DDoS attack detection appear in the big data environment. Firstly, to shorten the respond time of the DDoS attack detector; secondly, to reduce the required compute resources; lastly, to achieve a high detection rate with low false alarm rate. In the paper, we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and… More >

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