![]() Parizi, MVFCC: a multi-view fuzzy consensus clustering model for malware threat attribution. Choo, Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. Choo, Detecting crypto-ransomware in IoT networks based on energy consumption footprint. Wikipedia, Supervised Learning – Wikipedia. Law 8(3), 2 (2013)Įxpert.ai Team, ``What is Machine Learning? A definition - Expert System | Expert.ai, Expert System, 2019. Mahmod, Trends in android malware detection. Choo, Machine learning aided android malware classification. Vinod, A machine learning approach for linux malware detection, in Proceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2014, (2014), pp. Hashemi, Graph embedding as a new approach for unknown malware detection. Parizi, AI4SAFE-IoT: an AI-powered secure architecture for edge layer of Internet of things. Darabian et al., Detecting cryptomining malware: a deep learning approach for static and dynamic analysis. Choo, An enhanced stacked LSTM method with no random initialization for malware threat hunting in safety and time-critical systems. ![]() Jahromi et al., An improved two-hidden-layer extreme learning machine for malware hunting. Khayami, Know abnormal, find evil: frequent pattern mining for ransomware threat hunting and intelligence. Homayoun et al., Deep dive into ransomware threat hunting and intelligence at fog layer. Jamshidi, Efficient design and hardware implementation of the OpenFlow v1.3 switch on the Virtex-6 FPGA ML605. Benedetto, A cyber-kill-chain based taxonomy of crypto-ransomware features. Choo, P4-to-blockchain: A secure blockchain-enabled packet parser for software defined networking. Choo, Big data and internet of things security and forensics: Challenges and opportunities, in Handbook of Big Data and IoT Security, (Springer, Cham, 2019), pp. Watson, Internet of things security and forensics: Challenges and opportunities. Dehghantanha, Cyber threat intelligence: challenges and opportunities, in Advances in Information Security, (Springer, Cham, 2018), pp. Javadi, Cyber kill chain-based taxonomy of advanced persistent threat actors: analogy of tactics, techniques, and procedures. Kamluk, Threats to macOS users | Securelist, Securelist by Kaspersky, 2019. Beek et al., Mcafee Labs Threats Report, Technical report (McAfee, St. Dehghantanha, Digital forensics: the missing piece of the Internet of Things promise. McAfee, McAfee Labs Threats Report: April 2017, no. Wikipedia, Usage Share of Operating Systems in Europe, Wikipedia, 2014 (2019). Statscounter, Desktop Operating System Market Share Worldwide, StatCounter Global Stats, 2019. Their false-positive rates are also 1.19% and 11.92%, respectively.īased on the results, we can conclude that macOS users can be better protected from malicious software using the Decision Tree algorithm. The Support Vector Machine algorithm and the Naïve Bayes both have accuracy and false-positive rates of 88.33% and 87.54%, respectively. The Logistic Regression algorithm has an accuracy rate of 89.77% with a 5.15% false-positive rate. ![]() The results from this research show that the Decision Tree algorithm has a 92.78% accuracy with a 3.62% false alarm rate whiles the Stochastic Gradient Descent has an accuracy rate of 91.77% with a 7.09% false-positive rate. They are the Decision Tree (DT), Support Vector Machine (SVM), Gaussian Naïve Bayes (Naïve Bayes), Stochastic Gradient Descent (SGD) and Logistic Regression (LR) algorithms.Īll experiments were tested with a cross-validation technique. We conducted our experiments using five different machine learning algorithms. This research will, therefore, test different machine learning algorithms on the macOS to determine which of the algorithms will produce the best result in detecting malware on the macOS operating system. Machine learning (ML) has been proven to be an excellent method for detecting malicious software on other operating system platforms. As a result of this growth, the number of malware targeting the macOS has also gone up significantly, which has given rise to the need to find a way to protect macOS users from such malicious software. These Apple workstations run on the macOS operating system. ![]() Moreover, this trend is set to continue as Apple makes inroads into the personal computer market previously dominated by the Windows operating system. The number of users choosing Apple desktops and laptops has increased significantly over the past recent years.
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