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Author: Azeez, Nureni AyofeCOAR Access Rights: open access

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    Article
    Citation - WoS: 24
    Citation - Scopus: 39
    Network Intrusion Detection With a Hashing Based Apriori Algorithm Using Hadoop Mapreduce
    (Mdpi, 2019) Azeez, Nureni Ayofe; Ayemobola, Tolulope Jide; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas
    Ubiquitous nature of Internet services across the globe has undoubtedly expanded the strategies and operational mode being used by cybercriminals to perpetrate their unlawful activities through intrusion on various networks. Network intrusion has led to many global financial loses and privacy problems for Internet users across the globe. In order to safeguard the network and to prevent Internet users from being the regular victims of cyber-criminal activities, new solutions are needed. This research proposes solution for intrusion detection by using the improved hashing-based Apriori algorithm implemented on Hadoop MapReduce framework; capable of using association rules in mining algorithm for identifying and detecting network intrusions. We used the KDD dataset to evaluate the effectiveness and reliability of the solution. Our results obtained show that this approach provides a reliable and effective means of detecting network intrusion.
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    Article
    Citation - WoS: 55
    Citation - Scopus: 105
    Windows Pe Malware Detection Using Ensemble Learning
    (Mdpi, 2021) Azeez, Nureni Ayofe; Odufuwa, Oluwanifise Ebunoluwa; Misra, Sanjay; Oluranti, Jonathan; Damasevicius, Robertas
    In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evaluated or studied before. Machine learning provides an intelligent way to detect malware and comprises two stages: feature extraction and classification. This study suggests an ensemble learning-based method for malware detection. The base stage classification is done by a stacked ensemble of fully-connected and one-dimensional convolutional neural networks (CNNs), whereas the end-stage classification is done by a machine learning algorithm. For a meta-learner, we analyzed and compared 15 machine learning classifiers. For comparison, five machine learning algorithms were used: naive Bayes, decision tree, random forest, gradient boosting, and AdaBoosting. The results of experiments made on the Windows Portable Executable (PE) malware dataset are presented. The best results were obtained by an ensemble of seven neural networks and the ExtraTrees classifier as a final-stage classifier.