A Survey on Metamorphic Malware Detection based on Hidden Markov Model
A Survey on Metamorphic Malware Detection based on Hidden Markov Model
复制标题
基于隐马尔可夫模型的变态恶意软件检测综述
DOI：
10.1109/icacci.2018.8554803
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Ciza Thomas
中科院分区：
文献类型：
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作者：
Satheesh Kumar Sasidharan;Ciza Thomas
信息安全威胁的现象每天都在增加。反病毒公司的统计报告显示，攻击者使用恶意应用程序作为渗透和破坏计算机或移动的系统的主要工具之一。为了保护信息免受恶意软件攻击，研究人员正在确定和提出许多不同的技术。恶意软件检测和分类是一个具有挑战性的研究领域，因为每天都有大量新的恶意软件变种被引入。变形恶意软件会带来另一个挑战，因为它在结构上会随着每次新感染而变化。常用的基于签名的恶意软件检测在大多数情况下无法检测变形恶意软件。研究表明，行为或启发式方法是更有效的检测变形恶意软件。这项工作是一个全面的调查基于隐马尔可夫模型，恶意软件分析的启发式技术的恶意软件检测。这种随机建模方法的优点是，它有助于检测变形恶意软件，逃避正常的检测方法。该调查涵盖了该领域的主要文献，并得出结论，HMM是一种高效和有效的变形恶意软件检测和分类技术。
The phenomenon of information security threats increases every day. The statistical reports from antivirus companies show that attackers use malicious applications as one of the major tools to infiltrate and damage the computer or mobile system. To protect and secure information from malware attacks, many different techniques are being identified and proposed by researchers. Malware detection and classification is a challenging area of research as large number of new malware variants are introduced day by day. Metamorphic malware causes another challenge as it varies structurally with every new infection. The commonly used signature based malware detection fails in detecting metamorphic malware most of the times. The studies reveal that behavioral or heuristic approach is more effective for detection of metamorphic malware. This work is a comprehensive survey on malware detection based on Hidden Markov Model, a heuristic technique for malware analysis. The advantage of this stochastic modeling method is that it helps to detect metamorphic malware, which evade the normal detection methods. The survey covers major literatures in the field and concludes that HMM is an efficient and effective technique for metamorphic malware detection and classification.