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  • Open Access


    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

    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 irregular behavior. The importance of… More >

  • Open Access


    User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

    Rahmat Budiarto1,*, Ahmad A. Alqarni1, Mohammed Y. Alzahrani1, Muhammad Fermi Pasha2, Mohamed Fazil Mohamed Firdhous3, Deris Stiawan4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2679-2698, 2022, DOI:10.32604/cmc.2022.019847

    Abstract As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with… More >

  • Open Access


    Sales Prediction and Product Recommendation Model Through User Behavior Analytics

    Xian Zhao, Pantea Keikhosrokiani*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3855-3874, 2022, DOI:10.32604/cmc.2022.019750

    Abstract The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for… More >

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