Obfuscation: The Hidden Malware

O’Kane, Philip; Sezer, Sakir; McLaughlin, Kieran

doi:10.1109/msp.2011.98

Cited by 165 publications

(95 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detection techniques proposed earlier were based on static analysis. Static analysis examines the binary code, analyzes all possible execution paths, and identifies malicious code without execution [4]. However, analyzing binary code turns out to be difficult nowadays.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Detect Malware Based on API Call Sequence Analysis

Kim

2015

International Journal of Distributed Sensor Networks

190

View full text Add to dashboard Cite

In the era of ubiquitous sensors and smart devices, detecting malware is becoming an endless battle between ever-evolving malware and antivirus programs that need to process ever-increasing security related data. For malware detection, various approaches have been proposed. Among them, dynamic analysis is known to be effective in terms of providing behavioral information. As malware authors increasingly use obfuscation techniques, it becomes more important to monitor how malware behaves for its detection. In this paper, we propose a novel approach for dynamic analysis of malware. We adopt DNA sequence alignment algorithms and extract common API call sequence patterns of malicious function from malware in different categories. We find that certain malicious functions are commonly included in malware even in different categories. From checking the existence of certain functions or API call sequence patterns matched, we can even detect new unknown malware. The result of our experiment shows high enough -measure and accuracy. API call sequence can be extracted from most of the modern devices; therefore, we believe that our method can detect the malware for all types of the ubiquitous devices.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Detect Malware Based on API Call Sequence Analysis

Kim

2015

International Journal of Distributed Sensor Networks

190

View full text Add to dashboard Cite

show abstract

“…Entropy analysis [5,16,34,36,37] is an effective technique for aiding detection of malware by pointing to the possible presence of deception techniques. Despite polymorphism or obfuscation [19], files with high entropy are more likely to have encrypted sections in them. When a PE file switches between content regimes (i.e.…”

Section: Related Workmentioning

confidence: 99%

Discovering Malware with Time Series Shapelets

Patri¹,

Wojnowicz²,

Wolff³

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

View full text Add to dashboard Cite

show abstract

“…Advances in malware capabilities allow same malware to have different signatures adopting polymorphism or metamorphism techniques [4,5,6]. New signatures are needed in AV software to detect the variant of the existing malware.…”

Section: New Variants Of Malwarementioning

confidence: 99%

Combating Malware with Whitelisting in IoT-based Medical Devices

Chakravartula¹,

Lakshmi²

2017

IJCA

View full text Add to dashboard Cite

With the rapid advancements in the mobile, Internet and wireless technologies, the computing environment is seamlessly getting integrated into the physical world and being connected to the Internet leading to Internet of Things (IoT). In this environment, heterogeneous devices can communicate with one another, leading to innovative applications in healthcare. Malware in IoT environment possesses a great challenge due to interconnected and interoperated systems. Traditional signature based anti-malware solutions will not suffice to healthcare based IoT devices. The paper presents a novel approach of using whitelisting in IoT-based healthcare medical devices and illustrate the performance improvements over traditional solutions. General TermsSecurity, Internet of Things (IoT)

show abstract

Obfuscation: The Hidden Malware

Cited by 165 publications

References 4 publications

A Novel Approach to Detect Malware Based on API Call Sequence Analysis

A Novel Approach to Detect Malware Based on API Call Sequence Analysis

Discovering Malware with Time Series Shapelets

Combating Malware with Whitelisting in IoT-based Medical Devices

Contact Info

Product

Resources

About