Muhammad Reazul Haque¹, Saw Chin Tan¹, Zulfadzli Yusoff^2,*, Kashif Nisar^3,7, Rizaludin Kaspin⁴, Iram Haider³, Sana Nisar³, J. P. C. Rodrigues^5,6, Bhawani Shankar Chowdhry⁷, Muhammad Aslam Uqaili⁷, Satya Prasad Majumder⁸, Danda B. Rawat⁹, Richard Etengu¹, Rajkumar Buyya¹⁰

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 875-894, 2022, DOI:10.32604/cmc.2022.018505 - 07 September 2021

Abstract In the design and planning of next-generation Internet of Things (IoT), telecommunication, and satellite communication systems, controller placement is crucial in software-defined networking (SDN). The programmability of the SDN controller is sophisticated for the centralized control system of the entire network. Nevertheless, it creates a significant loophole for the manifestation of a distributed denial of service (DDoS) attack straightforwardly. Furthermore, recently a Distributed Reflected Denial of Service (DRDoS) attack, an unusual DDoS attack, has been detected. However, minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem. Moreover, recently the high More >

Rajendra Prasad Nayak¹, Srinivas Sethi², Sourav Kumar Bhoi³, Kshira Sagar Sahoo⁴, Nz Jhanjhi⁵, Thamer A. Tabbakh⁶, Zahrah A. Almusaylim^7,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3513-3529, 2021, DOI:10.32604/cmc.2021.018930 - 24 August 2021

Abstract Reliable vehicles are essential in vehicular networks for effective communication. Since vehicles in the network are dynamic, even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences. In this paper, a Trust-Based Distributed DoS Misbehave Detection Approach (TBDDoSA-MD) is proposed to secure the Software-Defined Vehicular Network (SDVN). A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood. It uses the jamming technique by sending unnecessary signals in the network, as a result, the network performance degrades. Attacked vehicles in… More >

Isha Sood^*, Varsha Sharma

Journal of Cyber Security, Vol.3, No.2, pp. 89-106, 2021, DOI:10.32604/jcs.2021.018623 - 02 August 2021

Abstract The Internet is often targeted by the Distributed Denial of Service (DDOS) Attacks that deliberately utilize resources and bandwidth to prohibit access to potential users. The attack possibility is that the packets are filled massively. A DOS attack is launched by a single source, while a DDOS attack is originated from numerous resources. DDoS attacks are not capable of stealing website user’s information. The prime motive of the DDoS attacks is to devastate the website resources. Distributed Denial of Service (DDoS) attacks are disruptive to internet access on the Network. The attitude of the customer… More >

Mohammad Aladaileh¹, Mohammed Anbar^1,*, Iznan H. Hasbullah¹, Yousef K. Sanjalawe^1,2, Yung-Wey Chong¹

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 373-391, 2021, DOI:10.32604/cmc.2021.017972 - 04 June 2021

Abstract The Software-Defined Networking (SDN) technology improves network management over existing technology via centralized network control. The SDN provides a perfect platform for researchers to solve traditional network’s outstanding issues. However, despite the advantages of centralized control, concern about its security is rising. The more traditional network switched to SDN technology, the more attractive it becomes to malicious actors, especially the controller, because it is the network’s brain. A Distributed Denial of Service (DDoS) attack on the controller could cripple the entire network. For that reason, researchers are always looking for ways to detect DDoS attacks against More >

Muhammad Reazul Haque¹, Saw Chin Tan¹, Zulfadzli Yusoff^2,*, Kashif Nisar^3,5,6, Lee Ching Kwang^2,7, Rizaludin Kaspin⁴, Bhawani Shankar Chowdhry⁵, Rajkumar Buyya⁸, Satya Prasad Majumder⁹, Manoj Gupta¹⁰, Shuaib Memon¹¹

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3147-3165, 2021, DOI:10.32604/cmc.2021.016591 - 06 May 2021

Abstract In software-defined networks (SDNs), controller placement is a critical factor in the design and planning for the future Internet of Things (IoT), telecommunication, and satellite communication systems. Existing research has concentrated largely on factors such as reliability, latency, controller capacity, propagation delay, and energy consumption. However, SDNs are vulnerable to distributed denial of service (DDoS) attacks that interfere with legitimate use of the network. The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design, especially in critical applications such as military, health care, and financial services networks requiring high… More >

Jieren Cheng^{1, 2}, Canting Cai^{1, *}, 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 - 30 March 2020

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 >

Jieren Cheng^{1, 2}, Junqi Li^{2, *}, 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 >

Jieren Cheng^{1, 2}, Yifu Liu^{1, *}, 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 >

Hsing‐Chung Chen^1,2, Shyi‐Shiun Kuo^1,3

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 >

Chen Zhang¹, Jieren Cheng^1,2,3,*, 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 >