Support vector machine integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware

Zolotukhin, Mikhail; Hämäläinen, Timo D.

doi:10.1109/glocomw.2013.6824988

Cited by 11 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We found some insights from our review which are as follows: First we observed that systems using opcode and PE features adhere to low FPR and high accuracy i.e. above 95% with some fluctuations [11,14,17,18]. They were unable to cope with packed executables, while disassembly of executables is not always feasible.…”

Section: Performance Evaluationmentioning

confidence: 95%

Comparative Analysis of Feature Extraction Methods of Malware Detection

Ranveer¹,

Hiray²

2015

IJCA

View full text Add to dashboard Cite

Recent years have encountered massive growth in malwares which poses a severe threat to modern computers and internet security. Existing malware detection systems are confronting with unknown malware variants. Recently developed malware detection systems investigated that the diverse forms of malware exhibit similar patterns in their structure with minor variations. Hence, it is required to discriminate the types of features extracted for detecting malwares. So that potential of malware detection system can be leveraged to combat with unfamiliar malwares. We mainly focus on the categorization of features based on malware analysis. This paper highlights general framework of malware detection system and pinpoints strengths and weaknesses of each method. Finally we presented overview of performance of present malware detection systems based on features.

show abstract

Section: Performance Evaluationmentioning

confidence: 95%

Comparative Analysis of Feature Extraction Methods of Malware Detection

Ranveer¹,

Hiray²

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…Zolotukhin and Hämäläinen [19] argue that signature-based malware detection and classification is flawed, because it requires systems to be compromised before new signatures can be detected. Their alternative to this approach is to first analyze the bytecode of known malicious samples and look for patterns that are later used to detect new malware exhibiting similar behaviour but possibly carrying a different signature.…”

Section: Malware Detectionmentioning

confidence: 99%

Game-Theoretic Malware Detection

MacQueen¹,

Bombardieri²,

Wright³

et al. 2020

Preprint

View full text Add to dashboard Cite

Large software platforms (e.g., mobile app stores, social media, email service providers) must ensure that files on their platform do not contain malicious code. Platform hosts use security tools to analyze those files for potential malware. However, given the expensive runtimes of tools coupled with the large number of exchanged files, platforms are not able to run all tools on every incoming file. Moreover, malicious parties look to find gaps in the coverage of the analysis tools, and exchange files containing malware that exploits these vulnerabilities.To address this problem, we present a novel approach that models the relationship between malicious parties and the security analyst as a leader-follower Stackelberg security game. To estimate the parameters of our model, we have combined the information from the VirusTotal dataset with the more detailed reports from the National Vulnerability Database. Compared to a set of natural baselines, we show that our model computes an optimal randomization over sets of available security analysis tools.

show abstract

“…Others, like [77], rely on graph related techniques to classify malware. Authors in [78] introduce an immune-based system for malware detection in smartphones, while Zolotukhin et al combine in [79] SVM, genetic and game-based techniques for detection and classification.…”

Section: Classificationmentioning

confidence: 99%

Networkmetrics: multivariate big data analysis in the context of the internet

Camacho¹,

Magán-Carrión²,

García-Teodoro³

et al. 2016

Journal of Chemometrics

View full text Add to dashboard Cite

Multivariate problems are found in all areas of knowledge. In chemistry and related disciplines. The chemometric community was developed in a joint effort to understand and solve problems mainly from a multivariate and exploratory perspective. This perspective is, indeed, of broader applicability, even in areas of knowledge far from chemistry. In this paper, we focus on the Internet: the net of devices that allow an interconnected world where all types of data can be shared and unprecedented communication services can be provided. Problems in the Internet, or, in general, in networking, are not very different from chemometric problems. Yet, they also have their specificities, mainly their typical Big Data nature and the presence of unstructured data. Exabytes of data in the format of text, video, audio, etc. are daily generated in the Internet. Part of these data need to be analyzed for disparate purposes, ranging from the optimization of the communications to cybersecurity and cyberwalfare. We argue that exploratory multivariate analysis is, indeed, useful to tackle these problems and has a role to play in the Big Data arena, currently limited to data mining techniques and visualization analytics. In absence of a better name, we call Networkmetrics the approach of treating Internet problems from the multivariate and exploratory perspective.

show abstract

Support vector machine integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware

Cited by 11 publications

References 16 publications

Comparative Analysis of Feature Extraction Methods of Malware Detection

Comparative Analysis of Feature Extraction Methods of Malware Detection

Game-Theoretic Malware Detection

Networkmetrics: multivariate big data analysis in the context of the internet

Contact Info

Product

Resources

About