Abstract: This research work proposes a novel and innovative idea of application of Machine Learning for malicious software analysis with a case study of malicious URL’s implementations and validatings. Traditionally Data Mining and its associated tools were developed for Malware Detection. Also Data Mining and Machine Learning strategies were used in literature for Cyber security. Deep learning is also used for Malware Analysis. Machine learning techniques for Malware Detection includes: in supervised learning - Hidden Markov Model (HMM), Profile Hidden Markov Model (PHMM), Support Vector Machines (SVM) etc.; in unsupervised learning includes Principal Component Analysis (PCA), K-means etc. Machine learning for Web Mining includes strategies like: Web Structure Mining (Web Crawlers / Indexer/ Ranking – PageRank algorithm), Web Content Mining (Parsing), Natural Language Processing (Information Retrieval models –TF-IDF, Latent Semantic Analysis (LSA), Doc2Vec (word2wec), CBOW model), Post Processing (Latent Dirichlet allocation and Opinion Mining (sentiment analysis) etc.
Keywords: Malware Analysis and Detection, Machine Learning, Malicious URL classification.
Title: Novel Design of Machine Learning for Malicious Software Analysis – Malicious URL Case Study
Author: Dr.G.Anil Kumar, Dr.M.Upendra Kumar, Dr.Sesham Anand, Dr.D.Shravani
International Journal of Interdisciplinary Research and Innovations
ISSN 2348-1218 (print), ISSN 2348-1226 (online)
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