Countering malware evolution using cloud-based learning

2013 ◽  
Author(s):  
Jacob Ouellette ◽  
Avi Pfeffer ◽  
Arun Lakhotia
Keyword(s):  
Malware Evolution

scholarly journals The impact of malware evolution on the analysis methods and infrastructure

2017 ◽  
Author(s):  
Krzysztof Cabaj ◽  
Piotr Gawkowski ◽  
Konrad Grochowski ◽  
Alexis Nowikowski ◽  
Piotr Żórawski
Keyword(s):  
Analysis Methods ◽  
Malware Evolution ◽  
The Impact

Malware

2008 ◽  
pp. 4014-4037
Author(s):  
Steven Furnell ◽  
Jeremy Ward
Keyword(s):  
Network Security ◽  
Malware Detection ◽  
Appropriate Use ◽  
Antivirus Software ◽  
Security Technologies ◽  
It Systems ◽  
Malware Evolution

In the two decades since its first significant appearance, malware has become the most prominent and costly threat to modern IT systems. This chapter examines the nature of malware evolution. It highlights that, as well as the more obvious development of propagation techniques, the nature of payload activities (and the related motivations of the malware creators) is also significantly changing, as is the ability of the malware to defeat defences. Having established the various facets of the threat, the discussion proceeds to consider appropriate strategies for malware detection and prevention, considering the role of modern antivirus software, and its use alongside other network security technologies to give more comprehensive protection. It is concluded that although malware is likely to remain a significant and ever-present threat, the risk and resultant impacts can be substantially mitigated by appropriate use of such safeguards.

scholarly journals A Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 551-604
Author(s):  
Damien Warren Fernando ◽  
Nikos Komninos ◽  
Thomas Chen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Learning Techniques ◽  
New Directions ◽  
Encryption Schemes ◽  
Malware Evolution ◽  
Number Of Citations ◽  
Research Studies

This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The increase in the use of artificial intelligence also coincides with this boom in ransomware. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. In this survey, we review prominent research studies which all showcase a machine learning or deep learning approach when detecting ransomware malware. These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution. We also explored the new directions of ransomware and how we expect it to evolve in the coming years, such as expansion into IoT (Internet of Things), with IoT being integrated more into infrastructures and into homes.

scholarly journals The Recent Trends in Malware Evolution, Detection and Analysis for Android Devices

2020 ◽  
Vol 13 (4) ◽  
pp. 240-248
Author(s):  
Kakelli Anil Kumar ◽  
◽  
A. Raman ◽  
C. Gupta ◽  
R.R. Pillai
Keyword(s):  
Recent Trends ◽  
Malware Evolution

scholarly journals Analysis of Mobile Malware: A Systematic Review of Evolution and Infection Strategies

2021 ◽  
Vol 4 (2) ◽  
pp. 1-29
Author(s):  
Moses Ashawa ◽  
Sarah Morris
Keyword(s):  
Systematic Review ◽  
Mobile Devices ◽  
Critical Analysis ◽  
Security Threats ◽  
Infection Rates ◽  
Survey Papers ◽  
Security Challenges ◽  
Mobile Malware ◽  
Survey Results ◽  
Malware Evolution

The open-source and popularity of Android attracts hackers and has multiplied security concerns targeting devices. As such, malware attacks on Android are one of the security challenges facing society. This paper presents an analysis of mobile malware evolution between 2000-2020. The paper presents mobile malware types and in-depth infection strategies malware deploys to infect mobile devices. Accordingly, factors that restricted the fast spread of early malware and those that enhance the fast propagation of recent malware are identified. Moreover, the paper discusses and classifies mobile malware based on privilege escalation and attack goals. Based on the reviewed survey papers, our research presents recommendations in the form of measures to cope with emerging security threats posed by malware and thus decrease threats and malware infection rates. Finally, we identify the need for a critical analysis of mobile malware frameworks to identify their weaknesses and strengths to develop a more robust, accurate, and scalable tool from an Android detection standpoint. The survey results facilitate the understanding of mobile malware evolution and the infection trend. They also help mobile malware analysts to understand the current evasion techniques mobile malware deploys

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