Search Results for author: Sakir Sezer

Found 12 papers, 0 papers with code

DL-Droid: Deep learning based android malware detection using real devices

no code implementations22 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.

Android Malware Detection Malware Detection

Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System

no code implementations18 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.

Mobile Security

EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning

no code implementations31 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.

Android Malware Detection BIG-bench Machine Learning +1

N-gram Opcode Analysis for Android Malware Detection

no code implementations5 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.

Android Malware Detection feature selection +1

Fuzzy Logic-based Implicit Authentication for Mobile Access Control

no code implementations12 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

High Accuracy Android Malware Detection Using Ensemble Learning

no code implementations2 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.

Android Malware Detection BIG-bench Machine Learning +3

PageRank in Malware Categorization

no code implementations2 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.

BIG-bench Machine Learning Malware Analysis

N-opcode Analysis for Android Malware Classification and Categorization

no code implementations27 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.

Classification General Classification +1

Android Malware Detection: an Eigenspace Analysis Approach

no code implementations27 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

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