Browsing by Author "Ashawa, Moses Aprofin"

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    Analysis of Android malware detection techniques: a systematic review
    (Society of Digital Information and Wireless Communications, 2019-09-30) Ashawa, Moses Aprofin; Morris, Sarah
    The emergence and rapid development in complexity and popularity of Android mobile phones has created proportionate destructive effects from the world of cyber-attack. Android based device platform is experiencing great threats from different attack angles such as DoS, Botnets, phishing, social engineering, malware and others. Among these threats, malware attacks on android phones has become a daily occurrence. This is due to the fact that Android has millions of user, high computational abilities, popularity, and other essential attributes. These factors influence cybercriminals (especially malware writers) to focus on Android for financial gain, political interest, and revenge. This calls for effective techniques that could detect these malicious applications on android devices. The aim of this paper is to provide a systematic review of the malware detection techniques used for android devices. The results show that most detection techniques are not very effective to detect zero-day malware and other variants that deploy obfuscation to evade detection. The critical appraisal of the study identified some of the limitations in the detection techniques that need improvement for better detection.
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    Design and implementation of Linux based workflow for digital forensics investigation
    (Foundation of Computer Science, 2019-04-30) Ashawa, Moses Aprofin; Ntonja, Morris
    Window based digital forensic workflow has been the traditional investigation model for digital evidence. Investigating using Linux based platform tends challenging since there is no specific investigation workflow for Linux platform. This study designed and implemented a Linux forensic based-workflow for digital investigation. The workflow was divided into different investigation phases. The digital investigations processes in all the phases were performed using Linux riggings. The work-flow was tested and evidence such as (E01) Image was accurately acquired. This paper is presented in the following sections. Section one and two provided introduction and literature on existing forensic workflow using windows-based workflow respectively. Section three provided the approach to window workflow. The experimental design and tools used were presented in section four. The rest of the sections considered the research analysis, discussion and conclusion respectively. The implication of the test conducted, tools used with their corresponding weakness and strengths were highlighted in the appendix.
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    The detection and prevention of Malware attacks on android mobile through the application of artificial intelligence techniques
    (2021-09) Ashawa, Moses Aprofin; Morris, S; Sastry, V V S S
    Our everyday lives are integrated with the use of mobile devices which store sensitive data. Sensitive data stored on smartphones attract different threats including malware. Among mobile platforms, Android is the most popular OS with malware targeting sensitive information and other mobile services. If malware infects a digital device, then it has control over the device's functionality and data. This can impact your finances, your privacy, and your access to your data. Malware is a threat not only to individuals but also to corporate organisations and financial institutions as well. This could lead to communication traffic of an infected network, hardware failure of the physical device, data theft, and loss of critical business data, among others. There are existing detection techniques for identifying Android malware. However, these techniques are limited in detecting evolving and sophisticated malware which use permission features as attack vectors in a smart fashion to infect Android mobile devices. To improve malware detection accuracy based on the related problem, we developed techniques for identifying Android-based malicious applications. To achieve this, the author presents a thorough review of the mobile malware evolution and infection strategies. The second part of the survey covers Android mobile malware detection, classification, and analysis techniques where the author identifies their efficacy in detecting evolving malware and their limitations. The author identifies through the review research gaps which open unto the development of different and novel solutions for Android malware classification and analysis. We leveraged the existing strengths of the previous methods to develop a robust novel automated framework to classify and analyse Android malware based on permission features. Classification accuracy of 97% was achieved with our framework with a False Positive Rate of 3%. Our techniques identified privileges that malware exploits as attack vectors to infect Android-based devices. The results demonstrate that our framework has high feature diversity capabilities for Android malware classification. We identified that there are permissions with similar attributes that are correlated and can trigger the installation of similar permissions with the same threat level especially. However, these prevention techniques are not tested on other mobile platforms' data and do not focus on mitigating pileup susceptibilities. Finally, we believe that as the results of this research are being made public and cited by organizations and individuals, the outcome of this will influence the security and social policies that mobile companies will implement based on some of the recommendations by our findings.