ضرایب رگرسیون به عنوان مقیاس سه گانه برای تشخیص بدافزار
ترجمه نشده

ضرایب رگرسیون به عنوان مقیاس سه گانه برای تشخیص بدافزار

عنوان فارسی مقاله: ضرایب رگرسیون به عنوان مقیاس سه گانه برای تشخیص بدافزار
عنوان انگلیسی مقاله: Regression coefficients as triad scale for malware detection
مجله/کنفرانس: کامپیوتر و مهندسی برق - Computers and Electrical Engineering
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی نرم افزار، امنیت اطلاعات
کلمات کلیدی فارسی: شناسایی بدافزار، توالی فراخوانی، نمودارهای جریان کنترل، مقیاس سه گانه، تست تی، قابل اجرا، فراخوانی API
کلمات کلیدی انگلیسی: Malware detection - Call sequences - Control flow graphs - Triad scale - T-test - Portable executable - API-call
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.compeleceng.2020.106886
دانشگاه: Department of Information Systems, College of Computer and Information Systems, Umm Al Qura University, Makkah, Saudi Arabia
صفحات مقاله انگلیسی: 14
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2021
ایمپکت فاکتور: 4.071 در سال 2020
شاخص H_index: 55 در سال 2021
شاخص SJR: 0.579 در سال 2020
شناسه ISSN: 0045-7906
شاخص Quartile (چارک): Q2 در سال 2020
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E15277
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (ترجمه)

خلاصه

کلید واژه ها

1. مقدمه

2. تحقیقات مرتبط

3. روش ها و مواد

4. مطالعه تجربی

5. نتیجه گیری

بیانیه نویسنده

اعلامیه منافع رقابتی

منابع

فهرست مطالب (انگلیسی)

Abstract

Keywords

1. Introduction

2. Related research

3. Methods and materials

4. Experimental study

5. Conclusion

Author statement

Declaration of Competing Interest

References

بخشی از مقاله (انگلیسی)

Abstract

The malware detection methods are classified into two categories, namely, dynamic analysis (active analysis) and static analysis (passive analysis). These methods undergo unusual obstruction, and challenges that are process complexity, limitation over detection accuracy. The static method serves to discover malicious applications using various parameters like permission analysis, signature verification. It can be regularly obfuscated. Dynamic techniques entail investigating the performance of an application by administering it in a restricted environment. The complex version of a portable executable often emerges with an intervention by hardening the dynamic analysis centric malware detection methods. The various constraints of these dynamic and static models contribute to this manuscript represents a Multi-Level Malware detection using Triad Scale (MLMTS) built on regression coefficients. The proposed method MLMTS spans into three levels, such that the first and second level performs static analysis, and the third level performs the dynamic analysis. The second and third levels of the hierarchy invoke upon the ambiguous decision of their respective predecessor level. The proposed work is based on the Machine Learning (ML) model that determines the triad scale by applying linear regression for each level of malware detection. The call sequences of the portable executable, arguments passed to these call sequences and their fallouts (resultant values) in respective order of three levels of the MLMTS method. The experimental study manifests the significance of the proposal compared to the other recent malware detection methods.

1. Introduction

The malicious software that is often termed as malware intends to infiltrate, infect, or intrude the cryptographic verification of the owner in the computer system. According to contemporary statistics [1], an average of 400 million malware models is recognized per annum. Currently, the malware family has boosted through the software modules engineered by incredible software skills [2]. The attacks can anonymize the source of the attack [2] and considerably succeeds in hacking the potential industrial structures known as the Stuxnet [3]. Anonymized sources are significant challenges to contemporary malware detection strategies. Extensive utilization of computer-networks is vulnerable to potential malware attacks. The dynamic network connectivity exposes the vulnerabilities of the corresponding network, which entertains the attackers to exploit these vulnerabilities to inject the malware into the respective system. The Intel organization has estimated the impact of malware in terms of loss of revenue as above 400 billion (USD 400 * 109 ) dollars worldwide per annum [1]. These statistics concreting the need for potential malware detect and defense mechanisms.