基于机器学习的恶意软件检测与2019年KISA数据挑战数据集

Soonhong Kwon, HeeDong Yang, Manhee Lee, Jong‐Hyouk Lee
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引用次数: 0

摘要

随着第四次工业时代的到来,人工智能、自动驾驶等信息通信技术迅速发展。然而,与这些积极方面不同的是,恶意黑客利用病毒、蠕虫和特洛伊木马等恶意软件攻击我们周围的物联网设备,窃取机密信息或阻止物联网设备正常运行。此外,恶意黑客正在开发和使用智能和高级恶意软件,因此恶意软件不容易被发现。近年来,利用机器学习和深度学习技术进行恶意软件检测技术的研究/开发,以检测恶意软件的智能和高级变体。本文基于KISA数据挑战数据集,进行了基于基本机器学习的恶意软件检测,并分析了已经出现的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning based Malware Detection with the 2019 KISA Data Challenge Dataset
With the advent of the 4th industrial era, ICT technologies such as artificial intelligence and autonomous driving are rapidly developing. However, unlike these positive aspects, malicious hackers target IoT devices around us using malwares such as viruses, worms, and Trojan horses to steal confidential information or prevent IoT devices from operating normally. In addition, malicious hackers are developing and using intelligent and advanced malwares so that malware cannot be easily detected. In recent years, studied/development of malware detection technology using machine learning and deep learning technologies has been conducted to detect intelligent and advanced variants of malwares. In this paper, based on the KISA Data Challenge Dataset, basic machine learning based malware detection is performed and the limitations that have occurred are analyzed.
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