A Survey on Malware and Malware Detection Systems

Abstract

Over the last decades, there were lots of studies made on malware and their countermeasures. The most recent reports emphasize that the invention of malicious software is rapidly increasing. Moreover, the intensive use of networks and Internet increases the ability of the spreading and the effectiveness of this kind of software. On the other hand, researchers and manufacturers making great efforts to produce anti-malware systems with effective detection methods for better protection on computers. In this paper, a detailed review has been conducted on the current situation of malware infection and the work done to improve anti-malware or malware detection systems. Thus, it provides an up-to-date comparative reference for developers of malware detection systems.

Internet of Things Malware : A Survey

“…Traditional computing environments are mainly based on X86 architecture, there exists a wide array of studies on malware propagation and analysis in X86 and/or X64 without consideration of device interaction with other architectures hence homogeneity based on specific architecture and operating system. Examples of such studies includes: on malware taxonomies [38,61,62], on malware detection [63][64][65][66][67], malware propagation [68] and malware analysis [69]. Windows variants of operating systems in both X86 and X64 architectures are most vulnerable to malware infections.…”

Privacy Concerns in Online Social Networks: A Users’ Perspective

“…The antivirus industry has been quite a mature database to handle issues of viruses, malware, Spywares and Spams having already detected signatures. Different Malware and their detection approaches are described in [100].…”

Comparative Analysis of Feature Extraction Methods of Malware Detection

“…For attaining efficiency and robustness, the system adheres to the best feature type which explores a meaningful corpus of malwares. In static analysis, the expected behavior of program is determined over the observations in its binary code or internal structure of files instead of actually executing it [6]. The static feature uniquely identifies the signature of malware or malware families.…”

“…Researchers have adopted supervised machine learning approach which uses classifiers Decision trees, Support vector machine, Nave bayes, Bayesian network, KNN algorithm, etc. are mentioned in [6,7,1]. The best classifier is chosen which gives the clear margin, and reduces interference and misclassification between maliciousness and benignancy of executables.…”