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  • Open Access

    ARTICLE

    Swarm Intelligence Based Routing with Black Hole Attack Detection in MANET

    S. A. Arunmozhi*, S. Rajeswari, Y. Venkataramani

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2337-2347, 2023, DOI:10.32604/csse.2023.024340 - 01 August 2022

    Abstract Mobile Ad hoc Network (MANET) possesses unique characteristics which makes it vulnerable to security threats. In MANET, it is highly challenging to protect the nodes from cyberattacks. Power conservation improves both life time of nodes as well as the network. Computational capabilities and memory constraints are critical issues in the implementation of cryptographic techniques. Energy and security are two important factors that need to be considered for improving the performance of MANET. So, the incorporation of an energy efficient secure routing protocol becomes inevitable to ensure appropriate action upon the network. The nodes present in More >

  • Open Access

    ARTICLE

    Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

    Abdullah Alshehri1,*, Nayeem Khan1, Ali Alowayr1, Mohammed Yahya Alghamdi2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1679-1689, 2023, DOI:10.32604/csse.2023.026526 - 15 June 2022

    Abstract This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics. The framework models the user behavior as sequences of events representing the user activities at such a network. The represented sequences are then fitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users. Thus, the model can recognize frequencies of regular behavior to profile the user manner in the network. The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regular or… More >

  • Open Access

    ARTICLE

    Iterative Dichotomiser Posteriori Method Based Service Attack Detection in Cloud Computing

    B. Dhiyanesh1,*, K. Karthick2, R. Radha3, Anita Venaik4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1099-1107, 2023, DOI:10.32604/csse.2023.024691 - 15 June 2022

    Abstract Cloud computing (CC) is an advanced technology that provides access to predictive resources and data sharing. The cloud environment represents the right type regarding cloud usage model ownership, size, and rights to access. It introduces the scope and nature of cloud computing. In recent times, all processes are fed into the system for which consumer data and cache size are required. One of the most security issues in the cloud environment is Distributed Denial of Service (DDoS) attacks, responsible for cloud server overloading. This proposed system ID3 (Iterative Dichotomiser 3) Maximum Multifactor Dimensionality Posteriori Method… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

    Rasha Almarshdi1,2,*, Laila Nassef1, Etimad Fadel1, Nahed Alowidi1

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 297-320, 2023, DOI:10.32604/iasc.2023.026799 - 06 June 2022

    Abstract Internet of Things (IoT) is the most widespread and fastest growing technology today. Due to the increasing of IoT devices connected to the Internet, the IoT is the most technology under security attacks. The IoT devices are not designed with security because they are resource constrained devices. Therefore, having an accurate IoT security system to detect security attacks is challenging. Intrusion Detection Systems (IDSs) using machine learning and deep learning techniques can detect security attacks accurately. This paper develops an IDS architecture based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) deep learning… More >

  • Open Access

    ARTICLE

    Novel DoS Attack Detection Based on Trust Mode Authentication for IoT

    D. Yuvaraj1, S. Shanmuga Priya2,*, 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 - 25 May 2022

    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… More >

  • 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 >

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