Comparison of malware detection techniques using machine learning algorithm

Abstract

<p>Currently, the volume of malware grows faster each year and poses a thoughtful global security threat. The number of malware developed increases as computers became interconnected, at an alarming rate in the 1990s. This scenario resulted the increment of malware. It also caused many protections are built to fight the malware. Unfortunately, the current technology is no longer effective to handle more advanced malware. Malware authors have created them to become more difficult to be evaded from anti-virus detection. In the current research, Machine Learning (ML) algorithm techniques became more popular to the researchers to analyze malware detection. In this paper, researchers proposed a defense system which uses three ML algorithm techniques comparison and select them based on the high accuracy malware detection. The result indicates that Decision Tree algorithm is the best detection accuracy compares to others classifier with 99% and 0.021% False Positive Rate (FPR) on a relatively small dataset.</p>

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

“…Also, ContagioDump, VirusTotal, and VirusShare were also employed for malware samples. However, in this SLR study, VirusShare [68], [63], [76], [86], [97], [102], [107], [117], [120], [58], [116] is found as the most popular dataset used in their experiments, followed by DREBIN, [67], [72], [80], [87], [88], [108], [109], [62], [64] Malware Genome Project, [114], [115], [64], [74], [112], [118], Google Play Store, [64], [74], [85], [115], [114] and many more type of datasets as shown in TABLE 14 in Appendix A.…”

“…Meta-Heuristic [30] 81.23% -99.91% NF [87], [92], [93] 69.44% -91% Bayesian [32], [49], [66], [67], [88] 80% -> 97% Gaussian [32], [36], [52], [70], [94] 80% -> 91.1% KNN [28], [29], [37], [48], [51], [55], [60], [69], [71], [72], [73], [74], [85], [96], [99], [100] 80.50% -99.2% N-grams [30], [31], [42], [43], [44], [56], [62], [63], [76], [77], [98] 81.23% -100% Meanwhile, each algorithm's average detection accuracy rate has been obtained, and SVM continues to perform well, with a 90.55% accuracy rate. N-grams have the greatest average detection accuracy rate of 97.80%, followed by KNN 92.72%, DT 92.23%, K-Means 89%, Bayesian 89.08%, Gaussian 87.42%, NB 86.45%, NF 83.48%, and Meta-Heuristic with 81.23%.…”

“…, [56], [57], [60], [61], [64], [66], [67], [69], [70], [71], [76], [81], [83], [84], [85], [87], [88], [90], [94], [96], [100], [101], [102], [105], [109], [113], [119], [121], [125] Signaturebased false positive rate and raising the false negative rate overcomes the limitations of heuristic-based approaches.…”

A Review of Machine Learning Algorithms for Cloud Computing Security

“…Furthermore, they identified five notable trends introduced in the search for security threats and defensive strategies of ML that merit examination. Selamat et al in [24], studied ML algorithms used to solve malware security threats and security in CC. The researchers proposed a barrier framework that uses three ML algorithm examination and selected them based high-accuracy malware discovery.…”

Analisis Static Malware Menggunakan Algoritma Random Forest Machine Learning

“…Analisis malware adalah sebuah metode untuk memeriksa struktur dan perilaku dari malware dengan mengidentifikasi karakteristik yang menunjukan tujuan jahatnya [5]. Umumnya terdapat dua teknik atau metode analisis yaitu analisis static dan dynamic [6] Analisis static atau biasa juga dikenal dengan analisis kode malware, adalah sebuah proses atau metode untuk mempelajari kinerja dari malware dengan mengamati dan analisis kode program dari malware [7]. Dengan menggunakan analisis static fitur PORTABLE EXECUTABLE (PE) bagian header file diektraks menggunakan disassembly tool [8].…”