MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention

By Andrea Saracino, Daniele Sgandurra, Gianluca Dini, Fabio Martinelli. IEEE Transactions on Dependable and Secure Computing, 2018

384 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e9a706d8c882fbd6e3b50c87ee4be9d760d89074

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Ömer Aslan
2. Refik Samet
3. Kim-Kwang Raymond Choo
4. Prabaharan Poornachandran
5. R. Vinayakumar

In Software

1. Ömer Aslan
2. Refik Samet
3. Kim-Kwang Raymond Choo

In Signal Processing

1. Ömer Aslan
2. Refik Samet
3. Kim-Kwang Raymond Choo
4. Prabaharan Poornachandran
5. R. Vinayakumar

Citations in the corpus

• A blockchain future for internet of things security: a position paper (2017, background): “Applications References Smart phones and Android Security [7],[10],[17] Voice over Internet Protocol (VoIP) [2],[29] Electronic health records [30] Artificial immune system [9], [23],[44] Web server [26],[28] Firewall [46] Unmanned aerial vehicles [52] Cloud [1],[3], [14], [27],[32],[34], [35],[42]”
• Deep Learning Approach for Intelligent Intrusion Detection System (2019, background): “To address this, a typical HIDS operates at host-level by analyzing and monitoring all traffic activities on the system application files, system calls and operating system [73].”
• A Comprehensive Review on Malware Detection Approaches (2020, background): “Host-based malware detection system for Android is presented in [58], [101].”