Browsing by Author "Najeeb, Rahman Athaur"

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    A critical insight into the effectiveness of research methods evolved to secure IoT ecosystem
    (Department of Computer Science, Nasarawa State University Keffi, 2020-03-03) Islam Khan, Burhan UI; Olanrewaju, Rashidah F.; Anwar, Farhat; Roohie, Naaz Mir; Najeeb, Rahman Athaur
    Increasing proliferation of IoT has led to an evolution of various devices for realising the smart features of ubiquitous applications. However, the inclusion of such a massive pool of devices with different computational capabilities, network protocols, hardware configurations, etc. also causes a higher number of security threats. Security professionals, organisations, and researchers are consistently investigating the security problems associated with IoT ecosystem and are coming up with different forms of solution sets. This paper presents a snapshot of the existing research work being carried out towards the security of IoT and assesses their strengths and weaknesses. The paper also explores the current research trend and presents the latest security methods being implemented and outlines the open research issues associated with it. The paper contributes to offering an accurate picture of the effectiveness of the existing security system in IoT.
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    Snort-Based Smart and Swift Intrusion Detection System
    (Department of Computer Science, Nasarawa State University Keffi, 2017-01-01) Olanrewaju, Rashidah F.; Islam Khan, Burhan UI; Najeeb, Rahman Athaur; Zahir, Ku Afiza Ku Nor; Hussain, Sabahat
    Objectives: In this paper, a smart Intrusion Detection System (IDS) has been proposed that detects network attacks in less time after monitoring incoming traffic thus maintaining better performance. Methods/Statistical Analysis: The features are extracted using back-propagation algorithm. Then, only these relevant features are trained with the help of multi-layer perceptron supervised neural network. The simulation is performed using MATLAB. Findings: The proposed system has been verified to have high accuracy rate, high sensitivity as well as a reduction in false positive rate. Besides, the intrusions have been classified into four categories as Denial-of-Service (DoS), User-to-root (U2R), Remote-to-Local (R2L) and Probe attacks; and the alerts are stored and shared via a central log. Thus, the unknown attacks detected by other Intrusion Detection Systems can be sensed by any IDS in the network thereby reducing computational cost as well as enhancing the overall detection rate. Applications/Improvements: The proposed system does not waste time by considering and analysing all the features but takes into consideration only relevant ones for the specific attack and supervised learning neural network is used for intrusion detection. By the application of Snort before backpropagation algorithm, the latter has only one function to perform – detection of unknown attacks. In this way, the time for attack detection is reduced.