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20162025
most citedDL-Droid: Deep learning based android malware detection using real devices

444 citations · 536 across the 6 of their papers we have counts for

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Showing cs.CRShow all

7 papers · 1 filter

cs.CR2025

A Systematic Review of Security Vulnerabilities in Smart Home Devices and Mitigation Techniques

Mohammed K. Alzaylaee

Smart homes that integrate Internet of Things (IoT) devices face increasing cybersecurity risks, posing significant challenges to these environments. The study explores security th…

cs.CR2020★ 2 cited

Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks

Suleiman Y. Yerima, Mohammed K. Alzaylaee

Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form pa…

cs.CR2020

High Accuracy Phishing Detection Based on Convolutional Neural Networks

Suleiman Y. Yerima, Mohammed K. Alzaylaee

The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attac…

cs.CR2019★ 444 cited

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

Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer

The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The soph…

cs.CR2017★ 7 cited

Improving Dynamic Analysis of Android Apps Using Hybrid Test Input Generation

Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer

The Android OS has become the most popular mobile operating system leading to a significant increase in the spread of Android malware. Consequently, several static and dynamic anal…

cs.CR2017★ 66 cited

EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning

Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer

The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detec…

cs.CR2016★ 17 cited

DynaLog: An automated dynamic analysis framework for characterizing Android applications

Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become t…