Poster

Shukla, Manish; Mondal, Sutapa; Lodha, Sachin

doi:10.1145/2976749.2989051

Cited by 27 publications

(4 citation statements)

References 4 publications

(12 reference statements)

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“…The anomaly detection approach utilizes benign to build normal profile. In the local environment, client-server approach used to build file tracker which works as dynamic ransomware [30]. In case of suspicious features notified, the server and user were given the signal of ransomware attack.…”

Section: Anomaly Detection Approachmentioning

confidence: 99%

Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review

Hussain,

Razak,

Firdaus

2023

JCSANDM

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Globally extensive digital revolutions involved with every process related to human progress can easily create the critical issues in security aspects. This is promoted due to the important factors like financial crises and geographical connectivity in worse condition of the nations. By this fact, the authors are well motivated to present a precise literature on malware detection with deep learning approach. In this literature, the basic overview includes the nature of nature of malware detection i.e., static, dynamic, and hybrid approach. Another major component of this articles is the investigation of the backgrounds from recently published and highly cited state-of-the-arts on malware detection, prevention and prediction with deep learning frameworks. The technologies engaged in providing solutions are utilized from AI based frameworks like machine learning, deep learning, and hybrid frameworks. The main motivations to produce this article is to portrait clear pictures of the option challenging issues and corresponding solution for developing robust malware-free devices. In the lack of a robust malware-free devices, highly growing geographical and financial disputes at wide globes can be extensively provoked by malicious groups. Therefore, exceptionally high demand of the malware detection devices requires a very strong recommendation to ensure the security of a nation. In terms preventing and recovery, Zero-day threats can be handled by recent methodology used in deep learning. In the conclusion, we also explored and investigated the future patterns of malware and how deals with in upcoming years. Such review may extend towards the development of IoT based applications used many fields such as medical devices, home appliances, academic systems.

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Section: Anomaly Detection Approachmentioning

confidence: 99%

Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review

Hussain,

Razak,

Firdaus

2023

JCSANDM

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“…First, they impose a management burden coming from their installation and frequent update tasks when a large set of hosts is involved (Berrueta et al, 2019;Morato et al, 2018). Second, locally installed software that analyses system calls consumes CPU cycles at the user host, having a potential impact on computer responsiveness (Continella et al, 2016;Kharraz et al, 2016;Mehnaz et al, 2018;Shaukat & Ribeiro, 2018;Shukla et al, 2016). Third, malware capable of escalating privileges could deactivate the detection tool (Loman, 2019).…”

Section: Background and Related Workmentioning

confidence: 99%

Crypto-ransomware detection using machine learning models in file-sharing network scenarios with encrypted traffic

Berrueta

Morató

Magaña

et al. 2022

Expert Systems with Applications

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“…• The read and write frequency is commonly used by these tools to detect the malware [29], [30]. Every ransomware must read and write the files to encrypt them, which they can do slowly to avoid being detected by these tools.…”

Section: History and Classification Of Ransomwarementioning

confidence: 99%

Open Repository for the Evaluation of Ransomware Detection Tools

Berrueta

Morató²,

Magaña

et al. 2020

IEEE Access

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Crypto-ransomware is a type of malware that encrypts user files, deletes the original data, and asks for ransom to recover the hijacked documents. Several articles have presented detection techniques for this type of malware; these techniques are applied before the ransomware encrypts files or during its action in an infected host. The evaluation of these proposals has always been accomplished using sets of ransomware samples that are prepared locally for the research article, without making the data available. Different studies use different sets of samples and different evaluation metrics, resulting in insufficient comparability. In this paper, we describe a public data repository containing the file access operations of more than 70 ransomware samples during the encryption of a large network shared directory. These data have already been used successfully in the evaluation of a network-based ransomware detection algorithm. Now, we are making these data available to the community and describing their details, how they were captured, and how they can be used in the evaluation and comparison of the results of most ransomware detection techniques. INDEX TERMSRansomware, open repository, traffic analysis. EDUARDO BERRUETA was graduated in telecommunication engineering from the Public University of Navarre (UPNA), Spain, in 2016. He received the M.Sc. degree in telecommunication engineering from UPNA, in 2018, where he is currently pursuing the Ph.D. degree with the Telecommunications, Networks and Services Research Group. Previously, he attended the University of Turin for completing his thesis on software defined networking. In 2016, he held a Scholarship at the Automatics and Computing Department. In 2017 and 2018, he was a Research Assistant with the Telecommunications, Networks and Services Research Group, UPNA. DANIEL MORATO received the M.Sc. degree in telecommunication engineering and the Ph.D. degree from the Public University of Navarre, Spain. In 2002, he was a Visiting Postdoctoral Fellow with the Electrical Engineering and Computer Sciences Department, University of California at Berkeley, Berkeley. Since 2006, he has been working at the Public University of Navarre. He is currently an Associate Professor with the Department of Electrical, Electronic and Communications Engineering. In 2014, he became a member of the Institute of Smart Cities. His research interests include high-speed networks, the performance and traffic analysis of Internet services, and network monitoring. EDUARDO MAGAÑA received the M.Sc. and Ph.D. degrees in telecommunications engineering from the

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Poster

Cited by 27 publications

References 4 publications

Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review

Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review

Crypto-ransomware detection using machine learning models in file-sharing network scenarios with encrypted traffic

Open Repository for the Evaluation of Ransomware Detection Tools

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