no code implementations • 22 Nov 2019 • Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer
In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation.
no code implementations • 18 May 2017 • Feng Yao, Suleiman Y. Yerima, BooJoong Kang, Sakir Sezer
In order to improve mobile security, an adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication system is proposed in this paper to provide authentication in a continuous and transparent manner. To illustrate the applicability and capability of ANFIS in our implicit authentication system, experiments were conducted on behavioural data collected for up to 12 weeks from different Android users.
no code implementations • 31 Mar 2017 • Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer
Our study shows that several features could be extracted more effectively from the on-device dynamic analysis compared to emulators.
no code implementations • 5 Dec 2016 • BooJoong Kang, Suleiman Y. Yerima, Sakir Sezer, Kieran McLaughlin
Our experiments on a dataset of 2520 samples showed an f-measure of 98% using the n-gram opcode based approach.
no code implementations • 12 Sep 2016 • Feng Yao, Suleiman Y. Yerima, BooJoong Kang, Sakir Sezer
In order to address the increasing compromise of user privacy on mobile devices, a Fuzzy Logic based implicit authentication scheme is proposed in this paper.
Cryptography and Security
no code implementations • 20 Aug 2016 • Suleiman Y. Yerima, Sakir Sezer, Gavin McWilliams
Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide.
no code implementations • 2 Aug 2016 • Suleiman Y. Yerima, Sakir Sezer, Gavin McWilliams, Igor Muttik
Mobile malware has been growing in scale and complexity as smartphone usage continues to rise.
no code implementations • 2 Aug 2016 • Suleiman Y. Yerima, Sakir Sezer, Igor Muttik
With over 50 billion downloads and more than 1. 3 million apps in the Google official market, Android has continued to gain popularity amongst smartphone users worldwide.
no code implementations • 2 Aug 2016 • BooJoong Kang, Suleiman Y. Yerima, Kieran McLaughlin, Sakir Sezer
In this paper, we propose a malware categorization method that models malware behavior in terms of instructions using PageRank.
no code implementations • 27 Jul 2016 • BooJoong Kang, Suleiman Y. Yerima, Kieran McLaughlin, Sakir Sezer
Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets.
no code implementations • 27 Jul 2016 • Suleiman Y. Yerima, Sakir Sezer, Igor Muttik
The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities.
Cryptography and Security
no code implementations • 27 Jul 2016 • Suleiman Y. Yerima, Sakir Sezer, Igor Muttik
Mobile malware has continued to grow at an alarming rate despite on-going efforts towards mitigating the problem.