Thursday, May 26, 2022

These articles have been peer-reviewed and accepted for publication in JICT, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the JICT standard. Additionally, titles, authors, abstracts and keywords may change before publication.
1*Kinan Keshkeh, 1Aman Jantan & 2Kamal Alieyan
1School of Computer Sciences, Universiti Sains Malaysia, Gelugor, Pulau Pinang, Malaysia
2Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan;;
Transport Layer Security (TLS) based malware is one of the most hazardous malware types, as it relies on encryption to conceal connections. Due to the complexity of TLS traffic decryption, several anomaly-based detection studies have been conducted to detect TLS-based malware using different features and machine learning (ML) algorithms. However, most of these studies utilized flow features with no feature transformation or relied on inefficient flow feature transformations like frequency-based periodicity analysis and outliers percentage. This paper introduces TLSMalDetect, a TLS-based malware detection approach that integrates periodicity-independent entropy-based flow set (EFS) features generated by a flow feature transformation technique to solve flow feature utilization issues in related research. EFS features effectiveness was evaluated in two ways: (1) by comparing them to the corresponding outliers percentage and flow features using four feature importance methods, and (2) by analyzing classification performance with and without EFS features. Moreover, new Transmission Control Protocol features not explored in literature were incorporated into TLSMalDetect, and their contribution was assessed. This study’s results proved EFS features of the number of packets sent and received were superior to related outliers percentage and flow features and could remarkably increase the performance up to ~42% in the case of Support Vector Machine accuracy. Furthermore, using the basic features, TLSMalDetect achieved the highest accuracy of 93.69% by Naïve Bayes (NB) among the ML algorithms applied. Also, from a comparison view, TLSMalDetect’s Random Forest precision of 98.99% and NB recall of 92.91% exceeded the best relevant findings of previous studies. These comparative results demonstrated the TLSMalDetect’s ability to detect more malware flows out of total malicious flows than existing works. It could also generate more actual alerts from overall alerts than earlier research.
Keywords: Malware detection, Machine Learning, TLS, Entropy, Flow features.

Nurul Aiman Abdul Rahim, Mohd Adili Norasikin & Zulisman Maksom
Pervasive Computing & Educational Technology, Fakulti Teknologi Maklumat dan Komunikasi,
Universiti Teknikal Malaysia Melaka;;
Virtual Reality (VR) technologies create and control different virtual world instead of the actual environment, and this contributes to the feeling of control known as the sense of agency (SoA). The SoA exists from the contrast between the expected sensory consequence of one’s action from efference copy and the real sensory effects. However, the size representation of objects differs between the physical and virtual world due to certain technical limitations, such as the VR application’s virtual hand not reflecting the user’s actual hand size. A limitation that will incur low quality of perception and SoA for digital application. Here, we proposed a proof-of-concept of an interactive e-commerce application that incorporates VR capability and size calibration mechanism. The mechanism uses a calibration method based on the reciprocal scale factor from the virtual object to its real counterpart. The study of the SoA focusing on user perception and interaction was done. The proposed method was tested on twenty two participants − who are also online shopping users. Nearly half of the participants (45%) buy online products frequently, at least one transaction per day. The outcome indicates that our proposed method improves 47% of user perception and interaction compared to the conventional e-commerce application with its static texts and images. Our proposed method is rudimentary yet effective and can be easily implemented in any digital field.
Keywords: virtual environment, sense of agency, virtual hand, online shopping.

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