Hira Akhtar Butt¹, Khoula Said Al Harthy², Mumtaz Ali Shah³, Mudassar Hussain^2,*, Rashid Amin^4,*, Mujeeb Ur Rehman¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3003-3031, 2024, DOI:10.32604/cmc.2024.057185 - 18 November 2024

Abstract Detecting sophisticated cyberattacks, mainly Distributed Denial of Service (DDoS) attacks, with unexpected patterns remains challenging in modern networks. Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking (SDN) environments. While Machine Learning (ML) models can distinguish between benign and malicious traffic, their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining. In this paper, we propose a novel DDoS detection framework that combines Machine Learning (ML) and Ensemble Learning (EL) techniques to improve DDoS attack detection and mitigation in SDN environments. Our model… More >

Seung-Ju Han, Seong-Su Yoon, Ieck-Chae Euom^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1495-1518, 2024, DOI:10.32604/cmes.2024.053457 - 27 September 2024

Abstract The rapid proliferation of Internet of Things (IoT) technology has facilitated automation across various sectors. Nevertheless, this advancement has also resulted in a notable surge in cyberattacks, notably botnets. As a result, research on network analysis has become vital. Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods. In this paper, we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework. The results indicate that using the More >

Abdulrahman Alzahrani^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2331-2349, 2024, DOI:10.32604/cmc.2024.052804 - 15 August 2024

Abstract The recent development of the Internet of Things (IoTs) resulted in the growth of IoT-based DDoS attacks. The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices. Anomaly detection models evaluate transmission patterns, network traffic, and device behaviour to detect deviations from usual activities. Machine learning (ML) techniques detect patterns signalling botnet activity, namely sudden traffic increase, unusual command and control patterns, or irregular device behaviour. In addition, intrusion detection systems (IDSs) and signature-based techniques are applied to recognize known malware signatures related to botnets.… More >

Xiabin Zhang^1,2, Zhongyi Hu^1,2,*, Lei Xiao^1,2, Hui Huang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2879-2905, 2024, DOI:10.32604/cmc.2024.048725 - 15 May 2024

Abstract Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease (AD). Most studies predominantly employ Convolutional Neural Networks (CNNs), which focus solely on local features, thus encountering difficulties in handling global features. In contrast to natural images, Structural Magnetic Resonance Imaging (sMRI) images exhibit a higher number of channel dimensions. However, during the Position Embedding stage of Multi Head Self Attention (MHSA), the coded information related to the channel dimension is disregarded. To tackle these issues, we propose the RepBoTNet-CESA network, an advanced AD-aided diagnostic model that is capable… More >

Yutao Hu¹, Yuntao Zhao^1,*, Yongxin Feng², Xiangyu Ma¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 509-530, 2024, DOI:10.32604/cmc.2024.042216 - 25 April 2024

Abstract In the face of the increasingly severe Botnet problem on the Internet, how to effectively detect Botnet traffic in real-time has become a critical problem. Although the existing deep Q network (DQN) algorithm in Deep reinforcement learning can solve the problem of real-time updating, its prediction results are always higher than the actual results. In Botnet traffic detection, although it performs well in the training set, the accuracy rate of predicting traffic is as high as%; however, in the test set, its accuracy has declined, and it is impossible to adjust its prediction strategy on… More >

Ashraf S. Mashaleh^1,2,*, Noor Farizah Binti Ibrahim¹, Mohammad Alauthman³, Mohammad Almseidin⁴, Amjad Gawanmeh⁵

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2245-2267, 2024, DOI:10.32604/cmc.2023.047323 - 27 February 2024

Abstract Increasing Internet of Things (IoT) device connectivity makes botnet attacks more dangerous, carrying catastrophic hazards. As IoT botnets evolve, their dynamic and multifaceted nature hampers conventional detection methods. This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization (PSO) to address the risks associated with IoT botnets. Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically. Fuzzy component settings are optimized using PSO to improve accuracy. The methodology allows for more complex thinking by transitioning from binary to continuous assessment. Instead of expert inputs, PSO data-driven tunes rules and membership More >

Mamona Arshad¹, Ahmad Karim¹, Salman Naseer², Shafiq Ahmad³, Mejdal Alqahtani³, Akber Abid Gardezi⁴, Muhammad Shafiq^5,*, Jin-Ghoo Choi⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2123-2135, 2023, DOI:10.32604/cmc.2022.028680 - 29 November 2023

Abstract The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat… More >

Donghyun Kim¹, Seungho Jeon², Jiho Shin³, Jung Taek Seo^4,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2979-2997, 2023, DOI:10.32604/iasc.2023.040019 - 11 September 2023

Abstract The smart city comprises various infrastructures, including healthcare, transportation, manufacturing, and energy. A smart city’s Internet of Things (IoT) environment constitutes a massive IoT environment encompassing numerous devices. As many devices are installed, managing security for the entire IoT device ecosystem becomes challenging, and attack vectors accessible to attackers increase. However, these devices often have low power and specifications, lacking the same security features as general Information Technology (IT) systems, making them susceptible to cyberattacks. This vulnerability is particularly concerning in smart cities, where IoT devices are connected to essential support systems such as healthcare… More >

Yi Qing¹, Xiangyu Liu², Yanhui Du^2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2095-2119, 2023, DOI:10.32604/cmc.2023.039980 - 30 August 2023

Abstract Distributed Denial of Service (DDoS) attacks have always been a major concern in the security field. With the release of malware source codes such as BASHLITE and Mirai, Internet of Things (IoT) devices have become the new source of DDoS attacks against many Internet applications. Although there are many datasets in the field of IoT intrusion detection, such as Bot-IoT, Constrained Application Protocol–Denial of Service (CoAP-DoS), and LATAM-DDoS-IoT (some of the names of DDoS datasets), which mainly focus on DDoS attacks, the datasets describing new IoT DDoS attack scenarios are extremely rare, and only N-BaIoT… 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 - 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 >