Browsing by Author "Morris, S"
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Item Open Access 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 SOur 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.Item Open Access Smurf : A reliable method for contextualising social media artefacts(2020-02) David, Anne; Morris, S; Appleby-Thomas, Gareth J.This research aims to evaluate whether artefacts other than the content of user com munication on social media can be used to attribute actions or relationships to a user. Social Media has enhanced the way users communicate on the Internet; providing the means for users to share content in real-time, and to establish connections and social relationships with like-minded individuals. However, as with all technology, social media can be leveraged for disagreeable and/or unlawful activities such as cyber bullying, trolling, grooming, or luring. There are reported cases where evidence from social media was used to secure convictions; for example, the tragic cases of Ashleigh Hall in 2009 and Kayleigh Haywood in 2015. The social media evidence e.g. the messages sent to the victim to arrange a meet up was used to link the suspect to the victim, and attribute actions to the suspect; in addition to other physical evidence presented as part of the case. Investigations with elements of social media is growing within digital forensics. This reinforces the need for a technique that can be used to make inferences about user actions and relationships, especially during a live triage investigation where the information needs to be obtained as quickly as possible. This research evaluates the use of live triage in the investigation of social media interactions, in order to determine the reliability of such a technique as a means of contextualising user activity, and attributing relationships or actions to a user. This research also evaluates the reliability of artefacts other than the actual content exchanged on social media; in the event that the content of communication is not immediately accessible/available to the investigator. To achieve this, it was important to break down the events that occur before, during and after user activity on social media; followed by the determination of what constitutes communication content in the context of this research. This research makes the following contributions: establishes a method for the cat egorisation of social media artefacts based on perceived user activity; communication content was characterised, thus highlighting evidential data of interest from user social media activity; the criteria for assessing the reliability of social media artefacts in a live triage investigation was proposed; a novel framework for social media investigation was developed with a Proof of Concept (PoC) to test its viability. The PoC demonstrates that it is possible to attribute actions or relationships to a user, using artefacts other than the actual content exchanged on social media.