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  • Open Access

    ARTICLE

    Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

    Gul Nawaz1, Muhammad Junaid1, Adnan Akhunzada2, Abdullah Gani2,*, Shamyla Nawazish3, Asim Yaqub3, Adeel Ahmed1, Huma Ajab4

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2157-2178, 2023, DOI:10.32604/cmc.2023.026952 - 29 November 2023

    Abstract Distributed denial of service (DDoS) attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user. We proposed a deep neural network (DNN) model for the detection of DDoS attacks in the Software-Defined Networking (SDN) paradigm. SDN centralizes the control plane and separates it from the data plane. It simplifies a network and eliminates vendor specification of a device. Because of this open nature and centralized control, SDN can easily become a victim of DDoS attacks. We proposed a supervised Developed Deep Neural Network (DDNN) model that can… More >

  • Open Access

    ARTICLE

    Unweighted Voting Method to Detect Sinkhole Attack in RPL-Based Internet of Things Networks

    Shadi Al-Sarawi1, Mohammed Anbar1,*, Basim Ahmad Alabsi2, Mohammad Adnan Aladaileh3, Shaza Dawood Ahmed Rihan2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 491-515, 2023, DOI:10.32604/cmc.2023.041108 - 31 October 2023

    Abstract The Internet of Things (IoT) consists of interconnected smart devices communicating and collecting data. The Routing Protocol for Low-Power and Lossy Networks (RPL) is the standard protocol for Internet Protocol Version 6 (IPv6) in the IoT. However, RPL is vulnerable to various attacks, including the sinkhole attack, which disrupts the network by manipulating routing information. This paper proposes the Unweighted Voting Method (UVM) for sinkhole node identification, utilizing three key behavioral indicators: DODAG Information Object (DIO) Transaction Frequency, Rank Harmony, and Power Consumption. These indicators have been carefully selected based on their contribution to sinkhole… More >

  • Open Access

    ARTICLE

    A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

    Salem Alhayani*, Diane R. Murphy

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2235-2259, 2023, DOI:10.32604/cmc.2023.038003 - 30 August 2023

    Abstract The Internet service provider (ISP) is the heart of any country’s Internet infrastructure and plays an important role in connecting to the World Wide Web. Internet exchange point (IXP) allows the interconnection of two or more separate network infrastructures. All Internet traffic entering a country should pass through its IXP. Thus, it is an ideal location for performing malicious traffic analysis. Distributed denial of service (DDoS) attacks are becoming a more serious daily threat. Malicious actors in DDoS attacks control numerous infected machines known as botnets. Botnets are used to send numerous fake requests to… More >

  • Open Access

    ARTICLE

    Toward Secure Software-Defined Networks Using Machine Learning: A Review, Research Challenges, and Future Directions

    Muhammad Waqas Nadeem1,*, Hock Guan Goh1, Yichiet Aun1, Vasaki Ponnusamy2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2201-2217, 2023, DOI:10.32604/csse.2023.039893 - 28 July 2023

    Abstract Over the past few years, rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems. As a result, greater intelligence is necessary to effectively manage, optimize, and maintain these systems. Due to their distributed nature, machine learning models are challenging to deploy in traditional networks. However, Software-Defined Networking (SDN) presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes. SDN provides a centralized network view and allows for dynamic updates of flow rules and software-based traffic analysis. While the… More >

  • Open Access

    ARTICLE

    New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

    Ouail Mjahed1,*, Salah El Hadaj1, El Mahdi El Guarmah1,2, Soukaina Mjahed1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.039111 - 26 May 2023

    Abstract Denial of Service (DoS/DDoS) intrusions are damaging cyber-attacks, and their identification is of great interest to the Intrusion Detection System (IDS). Existing IDS are mainly based on Machine Learning (ML) methods including Deep Neural Networks (DNN), but which are rarely hybridized with other techniques. The intrusion data used are generally imbalanced and contain multiple features. Thus, the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017, CSE-CICIDS2018 and CICDDoS 2019 datasets, according to the following key points. a) Three imbalanced CICIDS2017-2018-2019 datasets, including Benign and DoS/DDoS attack classes, are used.… More >

  • Open Access

    ARTICLE

    Adaptive Butterfly Optimization Algorithm (ABOA) Based Feature Selection and Deep Neural Network (DNN) for Detection of Distributed Denial-of-Service (DDoS) Attacks in Cloud

    S. Sureshkumar1,*, G .K. D. Prasanna Venkatesan2, R. Santhosh3

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1109-1123, 2023, DOI:10.32604/csse.2023.036267 - 26 May 2023

    Abstract Cloud computing technology provides flexible, on-demand, and completely controlled computing resources and services are highly desirable. Despite this, with its distributed and dynamic nature and shortcomings in virtualization deployment, the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties. The Intrusion Detection System (IDS) is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources. DDoS attacks are becoming more frequent and powerful, and their attack pathways are continually changing, which requiring the development of new detection methods. Here… More >

  • Open Access

    ARTICLE

    DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

    Yousef Sanjalawe1,2,*, Turke Althobaiti3,4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3571-3588, 2023, DOI:10.32604/cmc.2023.037386 - 31 March 2023

    Abstract Intrusion Detection System (IDS) in the cloud Computing (CC) environment has received paramount interest over the last few years. Among the latest approaches, Deep Learning (DL)-based IDS methods allow the discovery of attacks with the highest performance. In the CC environment, Distributed Denial of Service (DDoS) attacks are widespread. The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic, resulting in financial losses. Although various researchers have proposed many detection techniques, there are possible obstacles in terms of detection performance due to the use of insignificant traffic… More >

  • Open Access

    ARTICLE

    A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

    Anitha Thangasamy*, Bose Sundan, Logeswari Govindaraj

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2553-2567, 2023, DOI:10.32604/csse.2023.032078 - 21 December 2022

    Abstract The recent development of cloud computing offers various services on demand for organization and individual users, such as storage, shared computing space, networking, etc. Although Cloud Computing provides various advantages for users, it remains vulnerable to many types of attacks that attract cyber criminals. Distributed Denial of Service (DDoS) is the most common type of attack on cloud computing. Consequently, Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks. Since DDoS attacks have become increasingly widespread, it becomes difficult for some DDoS attack methods based on individual… More >

  • Open Access

    ARTICLE

    Central Aggregator Intrusion Detection System for Denial of Service Attacks

    Sajjad Ahmad1, Imran Raza1, M. Hasan Jamal1, Sirojiddin Djuraev2, Soojung Hur3, Imran Ashraf3,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2363-2377, 2023, DOI:10.32604/cmc.2023.032694 - 31 October 2022

    Abstract Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles (EVs) to be used by the smart grid through the central aggregator. Since the central aggregator is connected to the smart grid through a wireless network, it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection system. However, existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid network. In this paper, the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of… More >

  • Open Access

    ARTICLE

    Resilient Service Authentication for Smart City Application Using IoT

    Gokulakannan Elamparithi*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 145-152, 2023, DOI:10.32604/iasc.2023.032036 - 29 September 2022

    Abstract Internet of Things (IoT) support for smart city systems improves service scales by ignoring various user congestion. People are looking for different security features for reliable and robust applications. Here, the Permanent Denial of Service (PDoS) problem arises from improper user identification. This article introduces the Service-Reliant Application Authentication (SRAA) to prevent PDoS attacks in a smart area of the city. In this authentication method, the security of the application is ensured through the distribution of guarded access. The supervised access distribution uses user interface features and sync with the user device. Abnormality in linking More >

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