Gul Nawaz¹, Muhammad Junaid¹, Adnan Akhunzada², Abdullah Gani^2,*, 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

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 classify the DDoS attack traffic… More >

Laila M. Halman, Mohammed J. F. Alenazi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1469-1483, 2024, DOI:10.32604/cmes.2023.028077

Abstract The healthcare sector holds valuable and sensitive data. The amount of this data and the need to handle, exchange, and protect it, has been increasing at a fast pace. Due to their nature, software-defined networks (SDNs) are widely used in healthcare systems, as they ensure effective resource utilization, safety, great network management, and monitoring. In this sector, due to the value of the data, SDNs face a major challenge posed by a wide range of attacks, such as distributed denial of service (DDoS) and probe attacks. These attacks reduce network performance, causing the degradation of different key performance indicators (KPIs)… More > Graphic Abstract

Shadi Al-Sarawi¹, Mohammed Anbar^1,*, 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

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 attack detection and other relevant… More >

Salem Alhayani^*, Diane R. Murphy

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

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 overwhelm the resources of victims… More >

Muhammad Waqas Nadeem^1,*, 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

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 programmable nature of SDN makes… More >

Ouail Mjahed^1,*, Salah El Hadaj¹, El Mahdi El Guarmah^1,2, Soukaina Mjahed¹

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

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. b) A new technique based… More >

S. Sureshkumar^1,*, 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

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 the purpose of the study… More >

Yousef Sanjalawe^1,2,*, Turke Althobaiti^3,4

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

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 features. Therefore, in this paper,… More >

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

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 network flow features to distinguish… More >

Sajjad Ahmad¹, Imran Raza¹, M. Hasan Jamal¹, Sirojiddin Djuraev², Soojung Hur³, Imran Ashraf^3,*

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

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 the vehicle-to-grid network is investigated… More >