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  • Open Access

    ARTICLE

    Dynamic Threshold-Based Approach to Detect Low-Rate DDoS Attacks on Software-Defined Networking Controller

    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 - 18 May 2022

    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… More >

  • Open Access

    ARTICLE

    Machine Learning with Dimensionality Reduction for DDoS Attack Detection

    Shaveta Gupta1, Dinesh Grover2, Ahmad Ali AlZubi3,*, 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 - 29 March 2022

    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… More >

  • Open Access

    ARTICLE

    Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

    Amani Abdulrahman Albraikan1, Siwar Ben Haj Hassine2, Suliman Mohamed Fati3, Fahd N. Al-Wesabi2,4, Anwer Mustafa Hilal5,*, Abdelwahed Motwakel5, Manar Ahmed Hamza5, Mesfer Al Duhayyim6

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 907-923, 2022, DOI:10.32604/cmc.2022.024488 - 24 February 2022

    Abstract Cyberbullying (CB) is a distressing online behavior that disturbs mental health significantly. Earlier studies have employed statistical and Machine Learning (ML) techniques for CB detection. With this motivation, the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification (ODL-CDC) technique for CB detection in social networks. The proposed ODL-CDC technique involves different processes such as pre-processing, prediction, and hyperparameter optimization. In addition, GloVe approach is employed in the generation of word embedding. Besides, the pre-processed data is fed into Bidirectional Gated Recurrent Neural Network (BiGRNN) model for prediction. Moreover, hyperparameter tuning of BiGRNN More >

  • Open Access

    ARTICLE

    Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

    R. Gopi1, Mahantesh Mathapati2, B. Prasad3, Sultan Ahmad4, Fahd N. Al-Wesabi5, Manal Abdullah Alohali6,*, Anwer Mustafa Hilal7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 141-156, 2022, DOI:10.32604/cmc.2022.023306 - 24 February 2022

    Abstract Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims… More >

  • Open Access

    ARTICLE

    Grey Hole Attack Detection and Prevention Methods in Wireless Sensor Networks

    Gowdham Chinnaraju*, S. Nithyanandam

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 373-386, 2022, DOI:10.32604/csse.2022.020993 - 02 December 2021

    Abstract Wireless Sensor Networks (WSNs) gained wide attention in the past decade, thanks to its attractive features like flexibility, monitoring capability, and scalability. It overcomes the crucial problems experienced in network management and facilitates the development of diverse network architectures. The existence of dynamic and adaptive routing features facilitate the quick formation of such networks. But flexible architecture also makes it highly vulnerable to different sorts of attacks, for instance, Denial of Service (DoS). Grey Hole Attack (GHA) is the most crucial attack types since it creates a heavy impact upon the components of WSN and… More >

  • Open Access

    ARTICLE

    Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

    A. S. Anakath1,*, R. Kannadasan2, Niju P. Joseph3, P. Boominathan4, G. R. Sreekanth5

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 479-492, 2022, DOI:10.32604/csse.2022.019940 - 25 October 2021

    Abstract Cloud computing is a high network infrastructure where users, owners, third users, authorized users, and customers can access and store their information quickly. The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently. This cloud is nowadays highly affected by internal threats of the user. Sensitive applications such as banking, hospital, and business are more likely affected by real user threats. An intruder is presented as a user and set as a member of the network. After becoming an insider… More >

  • Open Access

    ARTICLE

    Industrial Datasets with ICS Testbed and Attack Detection Using Machine Learning Techniques

    Sinil Mubarak1, Mohamed Hadi Habaebi1,*, Md Rafiqul Islam1, Asaad Balla1, Mohammad Tahir2, Elfatih A. A. Elsheikh3, F. M. Suliman3

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1345-1360, 2022, DOI:10.32604/iasc.2022.020801 - 09 October 2021

    Abstract Industrial control systems (ICS) are the backbone for the implementation of cybersecurity solutions. They are susceptible to various attacks, due to openness in connectivity, unauthorized attempts, malicious attacks, use of more commercial off the shelf (COTS) software and hardware, and implementation of Internet protocols (IP) that exposes them to the outside world. Cybersecurity solutions for Information technology (IT) secured with firewalls, intrusion detection/protection systems do nothing much for Operational technology (OT) ICS. An innovative concept of using real operational technology network traffic-based testbed, for cyber-physical system simulation and analysis, is presented. The testbed is equipped… More >

  • Open Access

    ARTICLE

    MNN-XSS: Modular Neural Network Based Approach for XSS Attack Detection

    Ahmed Abdullah Alqarni1, Nizar Alsharif1, Nayeem Ahmad Khan1,*, Lilia Georgieva2, Eric Pardade3, Mohammed Y. Alzahrani1

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4075-4085, 2022, DOI:10.32604/cmc.2022.020389 - 27 September 2021

    Abstract The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing. A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic. In recent years, researchers have used different machine learning methods to detect network attacks without relying on those signatures. The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product. In this study, we propose and implement a new method that relies on a modular deep More >

  • Open Access

    ARTICLE

    SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection

    Wided Ben Daoud1, Sami Mahfoudhi2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2727-2742, 2022, DOI:10.32604/cmc.2022.020141 - 27 September 2021

    Abstract The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require… More >

  • Open Access

    ARTICLE

    Cyber-Attack Detection and Mitigation Using SVM for 5G Network

    Sulaiman Yousef Alshunaifi, Shailendra Mishra*, Mohammed Alshehri

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 13-28, 2022, DOI:10.32604/iasc.2022.019121 - 03 September 2021

    Abstract 5G technology is widely seen as a game-changer for the IT and telecommunications sectors. Benefits expected from 5G include lower latency, higher capacity, and greater levels of bandwidth. 5G also has the potential to provide additional bandwidth in terms of AI support, further increasing the benefits to the IT and telecom sectors. There are many security threats and organizational vulnerabilities that can be exploited by fraudsters to take over or damage corporate data. This research addresses cybersecurity issues and vulnerabilities in 4G(LTE) and 5G technology. The findings in this research were obtained by using primary… More >

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