Visible to the public A Malware Detection Method Based on Improved Fireworks Algorithm and Support Vector Machine

TitleA Malware Detection Method Based on Improved Fireworks Algorithm and Support Vector Machine
Publication TypeConference Paper
Year of Publication2020
AuthorsDong, D., Ye, Z., Su, J., Xie, S., Cao, Y., Kochan, R.
Conference Name2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)
Keywordsclassification performance, composability, computer systems, Decision trees, differential evolution, Evolutionary algorithms, Fireworks algorithm, genetic algorithms, improved fireworks algorithm, invasive software, kernel function parameter, learning (artificial intelligence), Levy Flights, machine learning methods, malware detection, Malware detection method, optimal parameter combination, particle swarm optimisation, particle swarm optimization, pattern classification, penalty factor, Predictive Metrics, pubcrawl, Resiliency, signature-based anti-virus systems, support vector machine, Support vector machines, SVM
AbstractThe increasing of malwares has presented a serious threat to the security of computer systems in recent years. Traditional signature-based anti-virus systems are not able to detect metamorphic and previously unseen malwares and it inspires people to use machine learning methods such as Naive Bayes and Decision Tree to identity malicious executables. Among these methods, detecting malwares by using Support Vector Machine (SVM) is one of the most effective approaches. However, the parameters of SVM have serious impacts on its classification performance. In order to find the optimal parameter combination and avoid the problem of falling into local optimal solution, many methods based on evolutionary algorithms are proposed, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and others. But these algorithms still face the problem of being trapped into local solution spaces in different degree. In this paper, an improved fireworks algorithm is presented and applied to search parameters of SVM: penalty factor c and kernel function parameter g. To research the performance of the proposed algorithm, numeric experiments are made and compared with some typical algorithms, the experimental results demonstrate it outperforms other algorithms.
Citation Keydong_malware_2020