Deep Android Malware Detection

By Niall McLaughlin, Jesús Martínez del Rincón, Boojoong Kang, Suleiman Y. Yerima, Paul C. Miller, Sakir Sezer, Yeganeh Safaei, Erik Trickel, Ziming Zhao, Adam Doupé and 1 more. Conference on Data and Application Security and Privacy, 2017

480 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/95707d8e38241fddb375caf5354313c034e28e19

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Citations in the corpus

Evaluation and Design of Robust Neural Network Defenses (2018, background): “[80] Android 21,00 Static Yes Wang et al.”
A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges (2020, methodology): “In [140], CNN is applied for classifying remote sensing data for ensuring security in wireless sensor networks.”
Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions (2022, methodology): “Using a CNN-based deep learning model for intrusion detection, such as denial-of-service (DoS) attacks [134], to detect malware [150], android malware detection [92].”
DL-Droid: Deep learning based android malware detection using real devices (2019, methodology): “Deep learning (DL) has gained increasing attention in the machine learning community and is re-emerging as a popular method of AI being applied in many ﬁelds ( Hou et al., 2016; 2017; Karbab et al., 2017; LeCun et al., 2015; McLaughlin et al., 2017; Yuan et al., 2014; 2016 ).”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “The main advantage of using DL algorithms, such as CNNs, is that the network is trained to learn suitable features and execute classification conjointly, eliminating the extraction process required in traditional ML and consequently providing an end-to-end model [207].”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “CNNs [189,190] Malware attacks -CNN is best suited for highly efficient and fast feature extraction from raw data.”
A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “The raw opcode sequences are processed by 126:6 J. Qiu et al. using the sequence data classification techniques of DL so the high-level semantic feature representation can be automatically learned by the deep neural networks [48].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “CNN has also been used in [335], where the authors draw inspiration from NLP and take the disassembled byte-code of an app as a text for analysis.”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “[82] also used the opcodes from malware files to build a detector that does not require any feature selection or engineering.”
Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection (2020, background): “use is still under-researched in cybersecurity generally, the application of CNNs has advanced the state-of-the-art in certain specific scenarios such as malware detection [14]–[17],”
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “words based detection method, and an open-sourced opcode sequence-based detection method [30].”