Adjusting SVMs for Large Data Sets using Balanced Decision Trees

Vatamanu, Cristina; Gavriluţ, Dragoş Teodor; Popoiu, George

doi:10.1109/synasc.2018.00043

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is essentially a manual comparison of some systems' typical actions. Lowering the , [55], [58], [59], [62], [63], [65], [68], [72], [73], [74], [75], [78], [79], [80], [82], [86], [91], [92], [93], [95], [97], [103], [104], [106], [107], [108], [110], [111], [112], [114], [115], [116], [117], [118], [120], [122], [123], [126] Behaviorbased…”

Section: D: Specification-basedmentioning

confidence: 99%

“…Furthermore, the outdated dataset used in the experiment, which offers little or no utility as a benchmark for the performance of malware detection systems on a modern network [119], also contributed to this issue. [53], [58], [59], [65], [68], [78], [79], [82], [86], [91], [95], [97], [103], [104], [106], [108], [111], [112], [115], [118], [120], [122], [123] Dynamic 2 6 5 9 2 2 1 1 2 0 2 [55], [62], [63], [72], [74], [75], [92], [93], [107], [110], [113], [114], [116], [117], [126] Hybrid 2 4 3 4 1 0 0 3 1 1 3 [54], [56], [57], [60], [61], [64], [66], [67], [69], [70]...…”

Section: A Dataset Usedmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2023

View full text Add to dashboard Cite

Malware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies. As a result, malware detection has always been a major worry and a difficult issue, owing to shortcomings in performance accuracy, analysis type, and malware detection approaches that fail to identify unexpected malware attacks. This paper seeks to conduct a thorough literature review and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen research works. The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. Furthermore, the taxonomy was used to evaluate the most recent machine learning algorithm and analysis. The paper also examines the obstacles and associated concerns encountered in malware detection and potential remedies. Finally, to address the related issues that would motivate researchers in their future work, an empirical study was utilized to assess the performance of several machine learning algorithms.INDEX TERMS Malware detection, machine learning algorithms, state-of-the-art.

show abstract