دانلود مقاله انگلیسی رایگان:مشخص کردن بدافزار مبتنی بر لینوکس: یافته ها و روندهای اخیر - 2020
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  • Characterizing Linux-based malware: Findings and recent trends Characterizing Linux-based malware: Findings and recent trends
    Characterizing Linux-based malware: Findings and recent trends

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Characterizing Linux-based malware: Findings and recent trends


    ترجمه فارسی عنوان مقاله:

    مشخص کردن بدافزار مبتنی بر لینوکس: یافته ها و روندهای اخیر


    منبع:

    Sciencedirect - Elsevier - Future Generation Computer Systems, 110 (2020) 267-281. doi:10.1016/j.future.2020.04.031


    نویسنده:

    J. Carrillo-Mondéjar a,∗, J.L. Martínez a, G. Suarez-Tangil b


    چکیده انگلیسی:

    Malware targeting interconnected infrastructures has surged in recent years. A major factor driving this phenomenon is the proliferation of large networks of poorly secured IoT devices. This is exacerbated by the commoditization of the malware development industry, in which tools can be readily obtained in specialized hacking forums or underground markets. However, despite the great interest in targeting this infrastructure, there is little understanding of what the main features of this type of malware are, or the motives of the criminals behind it, apart from the classic denial of service attacks. This is vital to modern malware forensics, where analyses are required to measure the trustworthiness of files collected at large during an investigation, but also to confront challenges posed by tech-savvy criminals (e.g., Trojan Horse Defense). In this paper, we present a comprehensive characterization of Linux-based malware. Our study is tailored to IoT malware and it leverages automated techniques using both static and dynamic analysis to classify malware into related threats. By looking at the most representative dataset of Linux-based malware collected by the community to date, we are able to show that our system can accurately characterize known threats. As a key novelty, we use our system to investigate a number of threats unknown to the community. We do this in two steps. First, we identify known patterns within an unlabeled dataset using a classifier trained with the labeled dataset. Second, we combine our features with a custom distance function to discover new threats by clustering together similar samples. We further study each of the unknown clusters by using state-of-the-art reverse engineering and forensic techniques and our expertise as malware analysts. We provide an in-depth analysis of what the most recent unknown trends are through a number of case studies. Among other findings, we observe that: i) crypto-mining malware is permeating the IoT infrastructure, ii) the level of sophistication is increasing, and iii) there is a rapid proliferation of new variants with minimal investment in infrastructure.
    Keywords: Malware forensics | IoT | Embedded systems | Data analytics | Machine learning | Expert systems


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 15
    حجم فایل: 3907 کیلوبایت

    قیمت: رایگان


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