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

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 and IoT-23 datasets used IoT… More >

Xingyu Kan^1,*, Yiwei Wang¹, Jiale Yan², Renfang Huang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.2, pp. 1-2, 2023, DOI:10.32604/icces.2023.09937

Abstract Nonlocal differential operators have become an increasingly important tool in the field of numerical modeling and computational science. In recent years, two specific types of nonlocal differential operators have emerged as particularly useful in simulations of material and structural failures, such as fracture and crack propagations in solids. In this paper, the first type of nonlocal operator is based on the nonlocal operator theory in peridynamic theory, which is called PDOs [1,2]. The second type of nonlocal operator is derived from the Taylor series expansion of nonlocal interpolation, which is called NDOs [3-5]. NDOs are usually used to discretize the… More >

Jiale Cheng¹, Zilong Jin^1,2,*

Journal on Internet of Things, Vol.4, No.3, pp. 183-195, 2022, DOI:10.32604/jiot.2022.042054

Abstract A novel Federated learning classifier is proposed using the Dempster-Shafer (DS) theory for the set-valued classification of industrial IoT Distributed Denial of Service (DDoS) attack traffic. The proposed classifier, referred to as the evidence-based federated learning classifier, employs convolution and pooling layers to extract high-dimensional features of Distributed Denial of Service (DDoS) traffic from the local data of private industrial clients. The characteristics obtained from the various participants are transformed into mass functions and amalgamated utilizing Dempster’s rule within the DS layer, situated on the federated server. Lastly, the set value classification task of attack mode is executed in the… More >

Yea-Sul Kim¹, Ye-Eun Kim¹, Hwankuk Kim^2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1125-1147, 2023, DOI:10.32604/csse.2023.039550

Abstract With the commercialization of 5th-generation mobile communications (5G) networks, a large-scale internet of things (IoT) environment is being built. Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service (DDoS) attacks across vast IoT devices. Recently, research on automated intrusion detection using machine learning (ML) for 5G environments has been actively conducted. However, 5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data. If this data is used to train an ML model, it will likely suffer from generalization errors due to… More >

Jieren Cheng^1,3, Xiulai Li^1,2,3,4,*, Xinbing Xu^2,3, Xiangyan Tang^1,3, Victor S. Sheng⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 975-992, 2023, DOI:10.32604/csse.2023.039280

Abstract With the rapid development of blockchain technology, the number of distributed applications continues to increase, so ensuring the security of the network has become particularly important. However, due to its decentralized, decentralized nature, blockchain networks are vulnerable to distributed denial-of-service (DDoS) attacks, which can lead to service stops, causing serious economic losses and social impacts. The research questions in this paper mainly include two aspects: first, the classification of DDoS, which refers to detecting whether blockchain nodes are suffering DDoS attacks, that is, detecting the data of nodes in parallel; The second is the problem of DDoS segmentation, that is,… 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 >

Adnan Hasan Bdair Aighuraibawi^1,2, Selvakumar Manickam^1,*, Rosni Abdullah³, Zaid Abdi Alkareem Alyasseri^4,5, Ayman Khallel⁶, Dilovan Asaad Zebari⁹, Hussam Mohammed Jasim⁷, Mazin Mohammed Abed⁸, Zainb Hussein Arif⁷

Computer Systems Science and Engineering, Vol.47, No.1, pp. 553-574, 2023, DOI:10.32604/csse.2023.037948

Abstract Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system… 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 >

Zisheng Yang¹, Wendong Li¹, Xuelei Cheng^1,2,*, Ran Hai¹, Shunqun Li³

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.8, pp. 2119-2130, 2023, DOI:10.32604/fdmp.2023.025444

Abstract Sixteen groups of comprehensive tests have been conducted to investigate the modifications in the physical properties of a weak expansive soil due to the addition of a cement jute fiber. The tests have been conducted to analyze the liquid plastic limit, the particle distribution and the free expansion rate. The results show that: (1) With an increase in the cement-jute fiber content, the free expansion rate of the modified expansive soil gradually decreases, however, such a rate rebounds when the fiber content exceeds 0.5% and the cement content exceeds 6%. (2) With an increase in the cement percentage, the particle… 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 >