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 >

M. Revathi, V. V. Ramalingam^*, B. Amutha

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3023-3036, 2023, DOI:10.32604/csse.2023.033600

Abstract The impact of a Distributed Denial of Service (DDoS) attack on Software Defined Networks (SDN) is briefly analyzed. Many approaches to detecting DDoS attacks exist, varying on the feature being considered and the method used. Still, the methods have a deficiency in the performance of detecting DDoS attacks and mitigating them. To improve the performance of SDN, an efficient Real-time Multi-Constrained Adaptive Replication and Traffic Approximation Model (RMCARTAM) is sketched in this article. The RMCARTAM considers different parameters or constraints in running different controllers responsible for handling incoming packets. The model is designed with multiple controllers to handle network traffic… More >

Jing Wang¹, Xiangyu Lei¹, Qisheng Jiang¹, Osama Alfarraj², Amr Tolba², Gwang-jun Kim^3,*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1727-1742, 2023, DOI:10.32604/csse.2023.030183

Abstract Software-Defined Network (SDN) decouples the control plane of network devices from the data plane. While alleviating the problems presented in traditional network architectures, it also brings potential security risks, particularly network Denial-of-Service (DoS) attacks. While many research efforts have been devoted to identifying new features for DoS attack detection, detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks. To solve this problem, a new method of DoS attack detection based on Deep Factorization Machine (DeepFM) is proposed in SDN. Firstly, we select the Growth Rate of Max Matched Packets… More >

S. Rameshkumar^1,*, R. Ganesan², A. Merline¹

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2379-2394, 2023, DOI:10.32604/csse.2023.027910

Abstract In The Wireless Multimedia Sensor Network (WNSMs) have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets. By utilising portable technologies, it achieves solid and significant results in wireless communication, media transfer, and digital transmission. Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature, moisture content, and other environmental conditions in recent decades. WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appropriate audio and video information. Many video sensor network studies focus on lowering power… More >

Tariq Ahamed Ahanger^1,*, Abdulaziz Aldaej¹, Mohammed Atiquzzaman², Imdad Ullah¹, Mohammed Yousuf Uddin¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3199-3217, 2022, DOI:10.32604/cmc.2022.027212

Abstract DDoS attacks in the Internet of Things (IoT) technology have increased significantly due to its spread adoption in different industrial domains. The purpose of the current research is to propose a novel technique for detecting botnet attacks in user-oriented IoT environments. Conspicuously, an attack identification technique inspired by Recurrent Neural networks and Bidirectional Long Short Term Memory (BLRNN) is presented using a unique Deep Learning (DL) technique. For text identification and translation of attack data segments into tokenized form, word embedding is employed. The performance analysis of the presented technique is performed in comparison to the state-of-the-art DL techniques. Specifically,… More >

S. Hariprasad^1,*, T. Deepa¹, N. Bharathiraja²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1425-1440, 2022, DOI:10.32604/iasc.2022.022856

Abstract The Internet of Things (IoT) is a global information and communication technology which aims to connect any type of device to the internet at any time and in any location. Nowadays billions of IoT devices are connected to the world, this leads to easily cause vulnerability to IoT devices. The increasing of users in different IoT-related applications leads to more data attacks is happening in the IoT networks after the fog layer. To detect and reduce the attacks the deep learning model is used. In this article, a hybrid sample selected recurrent neural network-extreme learning machine (hybrid SSRNN-ELM) algorithm that… More >

D. Yuvaraj¹, S. Shanmuga Priya^2,*, M. Braveen³, S. Navaneetha Krishnan⁴, S. Nachiyappan⁵, Abolfazl Mehbodniya⁶, A. Mohamed Uvaze Ahamed⁷, M. Sivaram⁸

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1505-1522, 2022, DOI:10.32604/iasc.2022.022151

Abstract Wireless sensor networks are extensively utilized as a communication mechanism in the field of the Internet of Things (IoT). Along with these services, numerous IoT based applications need stabilized transmission or delivery over unbalanced wireless connections. To ensure the stability of data packets delivery, prevailing works exploit diverse geographical routing with multi-hop forwarders in WSNs. Furthermore, critical Denial of Service (DoS) attacks frequently has an impact on these techniques, where an enormous amount of invalid data starts replicating and transmitted to receivers to prevent Wireless Sensor Networks (WSN) communication. In this investigation, a novel adaptive endorsement method is designed by… More >

Mohammad Adnan Aladaileh, Mohammed Anbar^*, Iznan H. Hasbullah, Abdullah Ahmed Bahashwan, Shadi Al-Sarawn

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1403-1416, 2022, DOI:10.32604/cmc.2022.029369

Abstract The emergence of a new network architecture, known as Software Defined Networking (SDN), in the last two decades has overcome some drawbacks of traditional networks in terms of performance, scalability, reliability, security, and network management. However, the SDN is vulnerable to security threats that target its controller, such as low-rate Distributed Denial of Service (DDoS) attacks, The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component. Therefore, there is an urgent need to propose a detection approach for this type of attack… More >

Olga Ussatova^1,2, Aidana Zhumabekova^1,*, Yenlik Begimbayeva^2,3, Eric T. Matson⁴, Nikita Ussatov⁵

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 577-594, 2022, DOI:10.32604/cmc.2022.026552

Abstract The fast development of Internet technologies ignited the growth of techniques for information security that protect data, networks, systems, and applications from various threats. There are many types of threats. The dedicated denial of service attack (DDoS) is one of the most serious and widespread attacks on Internet resources. This attack is intended to paralyze the victim's system and cause the service to fail. This work is devoted to the classification of DDoS attacks in the special network environment called Software-Defined Networking (SDN) using machine learning algorithms. The analyzed dataset included instances of two classes: benign and malicious. As the… More >

Shaveta Gupta¹, Dinesh Grover², Ahmad Ali AlZubi^3,*, 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

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 detect the DDoS attacks. However,… More >