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  • Open Access

    ARTICLE

    Machine Learning with Dimensionality Reduction for DDoS Attack Detection

    Shaveta Gupta1, Dinesh Grover2, Ahmad Ali AlZubi3,*, Nimit Sachdeva4, Mirza Waqar Baig5, Jimmy Singla6

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2665-2682, 2022, DOI:10.32604/cmc.2022.025048 - 29 March 2022

    Abstract With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately… More >

  • Open Access

    ARTICLE

    Cooperative Detection Method for DDoS Attacks Based on Blockchain

    Jieren Cheng1,2, Xinzhi Yao1,2,*, Hui Li3, Hao Lu4, Naixue Xiong5, Ping Luo1,2, Le Liu1,2, Hao Guo1,2, Wen Feng1,2

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 103-117, 2022, DOI:10.32604/csse.2022.025668 - 23 March 2022

    Abstract Distributed Denial of Service (DDoS) attacks is always one of the major problems for service providers. Using blockchain to detect DDoS attacks is one of the current popular methods. However, the problems of high time overhead and cost exist in the most of the blockchain methods for detecting DDoS attacks. This paper proposes a blockchain-based collaborative detection method for DDoS attacks. First, the trained DDoS attack detection model is encrypted by the Intel Software Guard Extensions (SGX), which provides high security for uploading the DDoS attack detection model to the blockchain. Secondly, the service provider… More >

  • Open Access

    ARTICLE

    Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

    Fatmah Alanazi*, Kamal Jambi, Fathy Eassa, Maher Khemakhem, Abdullah Basuhail, Khalid Alsubhi

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 923-938, 2022, DOI:10.32604/iasc.2022.024668 - 08 February 2022

    Abstract Software-defined network (SDN) is an enabling technology that meets the demand of dynamic, adaptable, and manageable networking architecture for the future. In contrast to the traditional networks that are based on a distributed control plane, the control plane of SDN is based on a centralized architecture. As a result, SDNs are susceptible to critical cyber attacks that exploit the single point of failure. A distributed denial of service (DDoS) attack is one of the most crucial and risky attacks, targeting the SDN controller and disrupting its services. Several researchers have proposed signature-based DDoS mitigation and… More >

  • Open Access

    ARTICLE

    DDoS Detection in SDN using Machine Learning Techniques

    Muhammad Waqas Nadeem, Hock Guan Goh*, Vasaki Ponnusamy, Yichiet Aun

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 771-789, 2022, DOI:10.32604/cmc.2022.021669 - 03 November 2021

    Abstract Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of… More >

  • Open Access

    ARTICLE

    SDN Based DDos Mitigating Approach Using Traffic Entropy for IoT Network

    Muhammad Ibrahim1, Muhammad Hanif2, Shabir Ahmad3, Faisal Jamil1, Tayyaba Sehar2, YunJung Lee4, DoHyeun Kim1,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5651-5665, 2022, DOI:10.32604/cmc.2022.017772 - 11 October 2021

    Abstract The Internet of Things (IoT) has been widely adopted in various domains including smart cities, healthcare, smart factories, etc. In the last few years, the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms. The IoT fitness devices collect trainee data that is being used for various decision-making. However, it will face numerous security and privacy issues towards its realization. This work focuses on IoT security, especially DoS/DDoS attacks. In this paper, we have proposed a novel blockchain-enabled protocol (BEP) that uses the notion of… More >

  • Open Access

    ARTICLE

    Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

    Nazarii Lutsiv1, Taras Maksymyuk1,*, Mykola Beshley1, Orest Lavriv1, Volodymyr Andrushchak1, Anatoliy Sachenko2, Liberios Vokorokos3, Juraj Gazda3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 413-431, 2022, DOI:10.32604/cmc.2022.018773 - 07 September 2021

    Abstract The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous… More >

  • Open Access

    ARTICLE

    Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

    Muhammad Reazul Haque1, Saw Chin Tan1, Zulfadzli Yusoff2,*, Kashif Nisar3,7, Rizaludin Kaspin4, Iram Haider3, Sana Nisar3, J. P. C. Rodrigues5,6, 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 >

  • Open Access

    ARTICLE

    TBDDoSA-MD: Trust-Based DDoS Misbehave Detection Approach in Software-defined Vehicular Network (SDVN)

    Rajendra Prasad Nayak1, Srinivas Sethi2, Sourav Kumar Bhoi3, Kshira Sagar Sahoo4, Nz Jhanjhi5, Thamer A. Tabbakh6, Zahrah A. Almusaylim7,*

    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 >

  • Open Access

    ARTICLE

    Computational Intelligent Techniques To Detect DDOS Attacks : A Survey

    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 >

  • Open Access

    ARTICLE

    Entropy-Based Approach to Detect DDoS Attacks on Software Defined Networking Controller

    Mohammad Aladaileh1, Mohammed Anbar1,*, Iznan H. Hasbullah1, Yousef K. Sanjalawe1,2, 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 >

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