Evaluation of countermeasure against future malware evolution with deterministic modeling

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.

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Wireless relay networks: deterministic modeling and capacity approximation

SciVee ◽

10.4016/6695.01 ◽

2008 ◽

Keyword(s):

Relay Networks ◽

Wireless Relay Networks ◽

Deterministic Modeling ◽

Wireless Relay

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Analysis of the fractional corona virus pandemic via deterministic modeling

Mathematical Methods in the Applied Sciences ◽

10.1002/mma.6814 ◽

2020 ◽

Vol 44 (1) ◽

pp. 1086-1102

Author(s):

Nguyen Huy Tuan ◽

Vo Viet Tri ◽

Dumitru Baleanu

Keyword(s):

Deterministic Modeling ◽

Corona Virus

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

IoT ◽

10.3390/iot1020030 ◽

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.

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