Tools and Techniques for Malware Detection and Analysis

Talukder, Sajedul

doi:10.48550/arxiv.2002.06819

Cited by 9 publications

(16 citation statements)

References 31 publications

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“…Machine learning has been used in solving social network problems [4][5][6][7] to cybersecurity issues [8,9], building automated digital systems [10] to e-governance solution [11]. However, there have been very limited attempts to apply this technique to tackle the mental illness issue.…”

Section: Related Workmentioning

confidence: 99%

AIPSYCH: A Mobile Application-based Artificial Psychiatrist for Predicting Mental Illness and Recovery Suggestions among Students

Hossen¹,

Talukder²,

Fahad³

2022

IJAIA

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COVID-19’s outbreak affected and compelled people from all walks of life to self-quarantine in their houses in order to prevent the virus from spreading. As a result of adhering to the exceedingly strict guideline, many people developed mental illnesses. Because the educational institution was closed at the time, students remained at home and practiced self-quarantine. As a result, it is necessary to identify the students who developed mental illnesses at that time. To develop AiPsych, a mobile application-based artificial psychiatrist, we train supervised and deep learning algorithms to predict the mental illness of students during the COVID-19 situation. Our experiment reveals that supervised learning outperforms deep learning, with a 97% accuracy of the Support Vector Machine (SVM) for mental illness prediction. Random Forest (RF) achieves the best accuracy of 91% for the recovery suggestion prediction. Our android application can be used by parents, educational institutes, or the government to get the predicted result of a student’s mental illness status and take proper measures to overcome the situation.

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Section: Related Workmentioning

confidence: 99%

AIPSYCH: A Mobile Application-based Artificial Psychiatrist for Predicting Mental Illness and Recovery Suggestions among Students

Hossen¹,

Talukder²,

Fahad³

2022

IJAIA

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“…It collects data about a user's online browsing history to target advertisements. The most common motivation for Adware is to collect data about the user to earn advertising revenue [5,[7][8][9][10][11][12][13][14][15].…”

Section: Adwarementioning

confidence: 99%

“…It keeps track of a user's login credentials, such as usernames and passwords, or collects sensitive data. Keyloggers are common spyware that records keystrokes on the keyboard and captures the login credentials [7][8][9][10].…”

Section: Spywarementioning

confidence: 99%

A Survey on Visualization-Based Malware Detection

Moawad¹,

Ebada²,

Al-Zoghby³

2022

Journal of Cyber Security

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In computer security, the number of malware threats is increasing and causing damage to systems for individuals or organizations, necessitating a new detection technique capable of detecting a new variant of malware more efficiently than traditional anti-malware methods. Traditional antimalware software cannot detect new malware variants, and conventional techniques such as static analysis, dynamic analysis, and hybrid analysis are time-consuming and rely on domain experts. Visualization-based malware detection has recently gained popularity due to its accuracy, independence from domain experts, and faster detection time. Visualization-based malware detection uses the image representation of the malware binary and applies image processing techniques to the image. This paper aims to provide readers with a comprehensive understanding of malware detection and focuses on visualization-based malware detection.

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“…A comprehensive review of malware analysis tools that detect and analyze malware executables is given in [95]. Except for reverse engineering tools as well as memory forensics, packet analysis, detection tools, online scanners and sandboxes were elaborated.…”

Section: Other Surveysmentioning

confidence: 99%

Data Transformation Schemes for CNN-Based Network Traffic Analysis: A Survey

2021

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The enormous growth of services and data transmitted over the internet, the bloodstream of modern civilization, has caused a remarkable increase in cyber attack threats. This fact has forced the development of methods of preventing attacks. Among them, an important and constantly growing role is that of machine learning (ML) approaches. Convolutional neural networks (CNN) belong to the hottest ML techniques that have gained popularity, thanks to the rapid growth of computing power available. Thus, it is no wonder that these techniques have started to also be applied in the network traffic classification domain. This has resulted in a constant increase in the number of scientific papers describing various approaches to CNN-based traffic analysis. This paper is a survey of them, prepared with particular emphasis on a crucial but often disregarded aspect of this topic—the data transformation schemes. Their importance is a consequence of the fact that network traffic data and machine learning data have totally different structures. The former is a time series of values—consecutive bytes of the datastream. The latter, in turn, are one-, two- or even three-dimensional data samples of fixed lengths/sizes. In this paper, we introduce a taxonomy of data transformation schemes. Next, we use this categorization to describe various CNN-based analytical approaches found in the literature.

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Tools and Techniques for Malware Detection and Analysis

Cited by 9 publications

References 31 publications

AIPSYCH: A Mobile Application-based Artificial Psychiatrist for Predicting Mental Illness and Recovery Suggestions among Students

AIPSYCH: A Mobile Application-based Artificial Psychiatrist for Predicting Mental Illness and Recovery Suggestions among Students

A Survey on Visualization-Based Malware Detection

Data Transformation Schemes for CNN-Based Network Traffic Analysis: A Survey

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