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  1. 1
    Implement edge pruning to enhance attack graph generation using the naïve approach algorithm

    Implement edge pruning to enhance attack graph generation using the naïve approach algorithm by Al-Araji, Zaid J., Mutlag, Ammar Awad, Syed Ahmad, Sharifah Sakinah

    Published 2024
    “…The next generation cyber defence concentrating on predictive analysis seems more proactive than existing technologies based on intrusion detection. Recently, many approaches have been proposed to detect and predict attacks; one of these approaches is attack graphs. …”
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  2. 2
    Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA)

    Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) by Nur Khairani, Kamarudin, Ahmad Firdaus, Zainal Abidin, Azlee, Zabidi, Ferda, Ernawan, Syifak, Izhar Hisham, Mohd Faizal, Ab Razak

    Published 2023
    “…Many security practitioners have adopted different approaches to detect malware. However, its attacks continuously evolve and spread, and the number of attacks is still increasing. …”
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  3. 3
    Attack prediction to enhance attack path discovery using improved attack graph

    Attack prediction to enhance attack path discovery using improved attack graph by Al-Araji, Zaid Jasim, Abdullah, Raihana Syahirah, Syed Ahmad, Sharifah Sakinah

    Published 2022
    “…For the attack graph analysis, the random forest algorithm was used to detect, predict, and dynamically ascertain the attack location in the network. …”
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  4. 4
    Bio-inspired for Features Optimization and Malware Detection

    Bio-inspired for Features Optimization and Malware Detection by Mohd Faizal, Ab Razak, Nor Badrul, Anuar, Fazidah, Othman, Ahmad, Firdaus, Firdaus, Afifi, Rosli, Salleh

    Published 2018
    “…Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. …”
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  5. 5
    Bio-inspired for Features Optimization and Malware Detection

    Bio-inspired for Features Optimization and Malware Detection by Razak, Mohd Faizal Ab, Anuar, Nor Badrul, Othman, Fazidah, Firdaus, Ahmad, Afifi, Firdaus, Salleh, Rosli

    Published 2018
    “…Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. …”
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  6. 6
    Attack graph construction for enhancing intrusion prediction based on vulnerabilities metrics

    Attack graph construction for enhancing intrusion prediction based on vulnerabilities metrics by Al-Araji, Zaid Jasim Mohammed

    Published 2023
    “…This study employs use Random Forest algorithm to identify and forecast attacks to dynamically locate the attack location in the network for attack graph analysis. …”
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  7. 7
    Voting algorithms for large scale fault-tolerant systems

    Voting algorithms for large scale fault-tolerant systems by Karimi, Abbas

    Published 2011
    “…To solve this problem and gain benefits of this algorithm, we employed parallel algorithm technique and by using optimal number of processors, we could propose optimal algorithms known as Parallel Average Voting and Parallel Weighted Average Voting which both have optimal time complexity and less calculation cost. …”
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  8. 8
    Detection of DDoS attacks in IoT networks using machine learning algorithms

    Detection of DDoS attacks in IoT networks using machine learning algorithms by Abdulrahman Alwan, Alwan Ahmed, Shah, Asadullah, Abdulrahman Alwan, Alwan Abdullah, Laghari, Shams Ul Arfeen

    Published 2025
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  9. 9
    The need for an enhanced IoT-based malware detection model using artificial intelligence (AI) algorithm: a review

    The need for an enhanced IoT-based malware detection model using artificial intelligence (AI) algorithm: a review by Maidin, Siti Sarah, Yahya, Norzariyah

    Published 2023
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  10. 10
    Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures

    Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures by Koohi, Sina Zangbari

    Published 2020
    “…Any optimization algorithm is suitable for only a specific domain of optimization problems. …”
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  11. 11
    Hybrid intelligent approach for network intrusion detection

    Hybrid intelligent approach for network intrusion detection by Al-Mohammed, Wael Hasan Ali

    Published 2015
    “…Hence, there must be substantial improvement in network intrusion detection techniques and systems. Due to the prevailing limitations of finding novel attacks, high false detection, and accuracy in previous intrusion detection approaches, this study has proposed a hybrid intelligent approach for network intrusion detection based on k-means clustering algorithm and support vector machine classification algorithm. …”
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  12. 12
    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Saleh Ahmed,, Ali Mohammed, Ali Majeed Alhammadi, Nafea, Ahmad Khalaf, Bashar, A. Mostafa, Salama

    Published 2023
    “…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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  13. 13
    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Ahmed, Ali Mohammed Saleh, Majeed Alhammadi, Nafea Ali, Ahmad Khalaf, Bashar, A. Mostafa, Salama

    Published 2023
    “…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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  14. 14
    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Saleh Ahmed, Ali Mohammed, Ali Majeed Alhammadi, Nafea, Ahmad Khalaf, Bashar, A. Mostafa, Salama

    Published 2023
    “…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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  15. 15
    An enhanced android botnet detection approach using feature refinement

    An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

    Published 2019
    “…In order to detect botnet attacks which causes immense chaos and problems to smartphones, first the Android botnet need to be analysed. …”
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  16. 16
    Crytojacking Classification based on Machine Learning Algorithm

    Crytojacking Classification based on Machine Learning Algorithm by Mansor W.N.A.B.W., Ahmad A., Zainudin W.S., Kama M.N., Madihah Mohd Saudi

    Published 2024
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  17. 17
    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Firas Mohammed Aswad, Firas Mohammed Aswad, Ali Mohammed Saleh Ahmed, Ali Mohammed Saleh Ahmed, Nafea Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

    Published 2023
    “…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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  18. 18
    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Ahmed, Ali Mohammed Saleh, Ali Majeed Alhammadi, Nafea, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

    Published 2023
    “…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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  19. 19
    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Firas Mohammed Aswad, Firas Mohammed Aswad, Ali Mohammed Saleh Ahmed, Ali Mohammed Saleh Ahmed, Nafea Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

    Published 2023
    “…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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  20. 20
    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

    Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Firas Mohammed Aswad, Firas Mohammed Aswad, Ali Mohammed Saleh Ahmed, Ali Mohammed Saleh Ahmed, Nafea Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

    Published 2022
    “…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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