Systematic literature review for malware visualization techniques

Analyzing the activities or the behaviors of malicious scripts highly depends on extracted features. It is also significant to know which features are more effective for certain visualization types. Similarly, selecting an appropriate visualization technique plays a key role for analytical descriptive, diagnostic, predictive and prescriptive. Thus, the visualization technique should provide understandable information about the malicious code activities. This paper followed systematic literature review method in order to review the extracted features that are used to identify the malware, different types of visualization techniques and guidelines to select the right visualization techniques. An advanced search has been performed in most relevant digital libraries to obtain potentially relevant articles. The results demonstrate significant resources and types of features that are important to analyze malware activities and common visualization techniques that are currently used and methods to choose the right visualization technique in order to analyze the security events effectively

Analysis of Feature Categories for Malware Visualization

It is important to know which features are more effective for certain visualization types. Furthermore, selecting an appropriate visualization tool plays a key role in descriptive, diagnostic, predictive and prescriptive analytics. Moreover, analyzing the activities of malicious scripts or codes is dependent on the extracted features. In this paper, the authors focused on reviewing and classifying the most common extracted features that have been used for malware visualization based on specified categories. This study examines the features categories and its usefulness for effective malware visualization. Additionally, it focuses on the common extracted features that have been used in the malware visualization domain. Therefore, the conducted literature review finding revealed that the features could be categorized into four main categories, namely, static, dynamic, hybrid, and application metadata. The contribution of this research paper is about feature selection for illustrating which features are effective with which visualization tools for malware visualization

Semantic graph knowledge representation for Al-Quran verses based on word dependencies

Semantic approaches present an efficient, detailed and easily understandable representation of knowledge from documents. Al-Quran contains a vast amount of knowledge that needs appropriate knowledge extraction. A semantic based approach can help in designing an efficient and explainable knowledge representation model for Al-Quran. This research aims to propose a semantic-graph knowledge representation model for verses of Al-Quran based on word dependencies. These features are used in the proposed knowledge representation model allowing the semantic graph matching to improve Al-Quran search applications' accuracy. The proposed knowledge representation model is essentially a formalism for generating a semantic graph representation of Quranic verses, which can be applied for knowledge base construction for other applications such as information retrieval system. A set of rules called Semantic Dependency Triple Rules are defined to be mapped into the semantic graph representing the verse's logic. The rules translate word dependencies and other NLP metadata into a triple form that holds logical information. The proposed model has been tested with English translation of Al-Quran on a document retrieval prototype The basic system has been enhanced with anaphoric pronouns correction, which has shown improvement in retrieval performance. The results have been compared with a closely related system and evaluated on the accuracy of the document retrieval in Precision, Recall and F-score measurements. The proposed model has achieved 65%, 60% and 62.4% for the measurements, respectively. It has also improved the overall accuracy of previous system by 43.8%

Perception on Cyber Terrorism: A Focus Group Discussion Approach

Focus group discussion is an exploratory research technique used to collect data through group interaction. This technique provides the opportunity to observe interaction among participants on a topic under this study. This paper contributes to an understanding on the cyber terrorism conceptual framework through the analysis of focus group discussion. The proposed cyber terrorism conceptual framework which was obtained during the qualitative study by the authors has been used as a basis for discussion in the focus group discussion. Thirty (30) participants took part in the focus group discussion. The overall results suggest that the proposed cyber terrorism framework is acceptable by the participants. The present study supports our initial research that the cyber terrorism conceptual framework constitutes the following components: target, motivation, tools of attack, domain, methods of attack and impact

Cyber Security Situational Awareness among Students: A Case Study in Malaysia

This paper explores the need for a national baseline study on understanding the level of cyber security situational awareness among primary and secondary school students in Malaysia. The online survey method was deployed to administer the data collection exercise. The target groups were divided into three categories: Group 1 (primary school aged 7-9 years old), Group 2 (primary school aged 10-12 years old), and Group 3 (secondary school aged 13-17 years old). A different questionnaire set was designed for each group. The survey topics/areas included Internet and digital citizenship knowledge. Respondents were randomly selected from rural and urban areas throughout all 14 states in Malaysia. A total of 9,158 respondents participated in the survey, with most states meeting the minimum sample size requirement to represent the country’s demographics. The findings and recommendations from this baseline study are fundamental to develop teaching modules required for children to understand the security risks and threats associated with the Internet throughout their years in school. Early exposure and education will help ensure healthy cyber habits among millennials in Malaysia

Analysis of feature categories for malware visualization

It is important to know which features are more effective for certain visualization types. Furthermore, selecting an appropriate visualization tool plays a key role in descriptive,diagnostic, predictive and prescriptive analytics. Moreover,analyzing the activities of malicious scripts or codes is dependent on the extracted features. In this paper, the authors focused on reviewing and classifying the most common extracted features that have been used for malware visualization based on specified categories. This study examines the features categories and its usefulness for effective malware visualization. Additionally, it focuses on the common extracted features that have been used in the malware visualization domain. Therefore, the conducted literature review finding revealed that the features could be categorized into four main categories, namely, static, dynamic,hybrid, and application metadata. The contribution of this research paper is about feature selection for illustrating which features are effective with which visualization tools for malware visualization