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  • Open Access

    ARTICLE

    Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

    Manal Abdullah Alohali1, Muna Elsadig1, Fahd N. Al-Wesabi2, Mesfer Al Duhayyim3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3087-3102, 2023, DOI:10.32604/csse.2023.036802

    Abstract With the advent of the Internet of Things (IoT), several devices like sensors nowadays can interact and easily share information. But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable. In such scenarios, security concern is the most prominent. Different models were intended to address these security problems; still, several emergent variants of botnet attacks like Bashlite, Mirai, and Persirai use security breaches. The malware classification and detection in the IoT model is still a problem, as the adversary reliably generates a new variant of IoT malware and actively… More >

  • Open Access

    ARTICLE

    An Asset-Based Approach to Mitigate Zero-Day Ransomware Attacks

    Farag Azzedin*, Husam Suwad, Md Mahfuzur Rahman

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3003-3020, 2022, DOI:10.32604/cmc.2022.028646

    Abstract This article presents an asset-based security system where security practitioners build their systems based on information they own and not solicited by observing attackers’ behavior. Current security solutions rely on information coming from attackers. Examples are current monitoring and detection security solutions such as intrusion prevention/detection systems and firewalls. This article envisions creating an imbalance between attackers and defenders in favor of defenders. As such, we are proposing to flip the security game such that it will be led by defenders and not attackers. We are proposing a security system that does not observe the behavior of the attack. On… More >

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