Visible to the public Prevent Data Exfiltration on Smart Phones Using Audio Distortion and Machine Learning

TitlePrevent Data Exfiltration on Smart Phones Using Audio Distortion and Machine Learning
Publication TypeConference Paper
Year of Publication2021
AuthorsMoonamaldeniya, Menaka, Priyashantha, V.R.S.C., Gunathilake, M.B.N.B., Ransinghe, Y.M.P.B., Ratnayake, A.L.S.D., Abeygunawardhana, Pradeep K.W.
Conference Name2021 Moratuwa Engineering Research Conference (MERCon)
Keywordsandroid, android encryption, Audio Distortion, Audio Jacking, Clipboard Jacking, codes, Encryption, Human Behavior, machine learning, Malware, Metrics, mobile applications, mobile security, Operating systems, permissions, pubcrawl, resilience, Resiliency, Scalability, Servers, Side-channel attack, side-channel attacks, Switches
AbstractAttacks on mobile devices have gained a significant amount of attention lately. This is because more and more individuals are switching to smartphones from traditional non-smartphones. Therefore, attackers or cybercriminals are now getting on the bandwagon to have an opportunity at obtaining information stored on smartphones. In this paper, we present an Android mobile application that will aid to minimize data exfiltration from attacks, such as, Acoustic Side-Channel Attack, Clipboard Jacking, Permission Misuse and Malicious Apps. This paper will commence its inception with an introduction explaining the current issues in general and how attacks such as side-channel attacks and clipboard jacking paved the way for data exfiltration. We will also discuss a few already existing solutions that try to mitigate these problems. Moving on to the methodology we will emphasize how we came about the solution and what methods we followed to achieve the end goal of securing the smartphone. In the final section, we will discuss the outcomes of the project and conclude what needs to be done in the future to enhance this project so that this mobile application will continue to keep the user's data safe from the criminals' grasps.
DOI10.1109/MERCon52712.2021.9525639
Citation Keymoonamaldeniya_prevent_2021