Kaspersky Lab: from Russia with anti-virus

Kshetri, Nir

doi:10.1108/20450621111180954

Cited by 8 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, providing a name to address mapping services for the chain of Internet connectivity makes the approach vulnerable network protocols for today's internet traffic [1,2]. Cyber-attacks are considered as a new remote weapon [3,4] targeting critical infrastructures such as a presidential campaign [5], a nuclear program [6], government personnel data [7], and software providers [8]. It is vital to distinguish harmful and normal traffics while using the internet network efficiently.…”

Section: Introductionmentioning

confidence: 99%

Detecting Malicious DNS over HTTPS Traffic in Domain Name System using Machine Learning Classifiers

Banadaki¹

2020

JCSA

View full text Add to dashboard Cite

This paper presents a systematic two-layer approach for detecting DNS over HTTPS (DoH) traffic and distinguishing Benign-DoH traffic from Malicious-DoH traffic using six machine learning algorithms. The capability of machine learning classifiers is evaluated considering their accuracy, precision, recall, and F-score, confusion matrices, ROC curves, and feature importance. The results show that LGBM and XGBoost algorithms outperform the other algorithms in almost all the classification metrics reaching the maximum accuracy of 100% in the classification tasks of layers 1 and 2.LGBM algorithms only misclassified one DoH traffic test as non-DoH out of 4000 test datasets. It has also found that out of 34 features extracted from the CIRA-CIC-DoHBrw-2020 dataset, SourceIP is the critical feature for classifying DoH traffic from non-DoH traffic in layer one followed by DestinationIP feature. However, only DestinationIP is an important feature for LGBM and gradient boosting algorithms when classifying Benign-DoH from Malicious-DoH traffic in layer 2.

show abstract

Section: Introductionmentioning

confidence: 99%

Detecting Malicious DNS over HTTPS Traffic in Domain Name System using Machine Learning Classifiers

Banadaki¹

2020

JCSA

View full text Add to dashboard Cite

show abstract

“…For instance, in 2010, Moscow-based KL was the world's fourth biggest IT security company. Some other former second world economies also have top IT security companies such as the Czech Republic's AVG Technologies, Romania's BitDefender and the Slovak Republic's ESET (Kshetri, 2011b). Likewise, the Belarusian firm VirusBlokAda was the first company to identify the Stuxnet code in June 2010 (Borland, 2010).…”

Section: Discussionmentioning

confidence: 96%

Cybercrime and Cybersecurity in the Global South

Kshetri

2013

Self Cite

View full text Add to dashboard Cite

DLLMiner: structural mining for malware detection

Narouei

Ahmadi

Giacinto

et al. 2015

Security Comm Networks

View full text Add to dashboard Cite

Existing anti-malware products usually use signature-based techniques as their main detection engine. Although these methods are very fast, they are unable to provide effective protection against newly discovered malware or mutated variant of old malware. Heuristic approaches are the next generation of detection techniques to mitigate the problem. These approaches aim to improve the detection rate by extracting more behavioral characteristics of malware. Although these approaches cover the disadvantages of signature-based techniques, they usually have a high false positive, and evasion is still possible from these approaches. In this paper, we propose an effective and efficient heuristic technique based on static analysis that not only detect malware with a very high accuracy, but also is robust against common evasion techniques such as junk injection and packing. Our proposed system is able to extract behavioral features from a unique structure in portable executable, which is called dynamic-link library dependency tree, without actually executing the application

show abstract

Kaspersky Lab: from Russia with anti-virus

Cited by 8 publications

References 2 publications

Detecting Malicious DNS over HTTPS Traffic in Domain Name System using Machine Learning Classifiers

Detecting Malicious DNS over HTTPS Traffic in Domain Name System using Machine Learning Classifiers

Cybercrime and Cybersecurity in the Global South

DLLMiner: structural mining for malware detection

Contact Info

Product

Resources

About