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A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

Eva Rodriguez, Beatriz Otero, Norma Gutierrez, Ramon Canal
2021 IEEE Communications Surveys and Tutorials  
This paper presents a comprehensive survey of recent cybersecurity works that use DL in mobile and wireless networks.  ...  First, we provide a detailed overview of DL techniques applied, or with potential applications, to cybersecurity. Then, we review cybersecurity works based on DL.  ...  ACKNOWLEDGMENTS This work is supported by the Generalitat de Catalunya under grant 2017SGR962 and the DRAC project (001-P-001723).  ... 
doi:10.1109/comst.2021.3086296 fatcat:2svylj3y7vfijnynpnoksdl6oa

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Luca Caviglione, Michal Choras, Igino Corona, Artur Janicki, Wojciech Mazurczyk, Marek Pawlicki, Katarzyna Wasielewska
2020 IEEE Access  
On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding.  ...  One of the major components that leads to the successful compromising of the targeted system is malicious software.  ...  While the behavior-based malware detection needs to run a sample of malware, the heuristic approach examines their features, such as: API calls, byte N-grams, operational codes (OpCodes), control flow  ... 
doi:10.1109/access.2020.3048319 fatcat:tatdk6pzczgp3aylvbxoxabuta

Vehicle Security: A Survey of Security Issues and Vulnerabilities, Malware Attacks and Defenses

Abdulrahman Abu Elkhail, Rafi Ud Daula Refat, Ricardo Habre, Azeem Hafeez, Anys Bacha, Hafiz Malik
2021 IEEE Access  
This work also presents a detailed survey of available defenses against such attacks including: signature, behavior, heuristic, cloud, and machine learning-based detection measures.  ...  This trend is exacerbated by the fact that many of these ECUs rely on wireless communication for interacting with the outside world.  ...  Other work by Marhusin et al [157] proposed a malware n-grams-based detection method based on extraction of API sequences.  ... 
doi:10.1109/access.2021.3130495 fatcat:6tivatn72fflnbpz6obekjk7oi

A Survey on Adversarial Attacks for Malware Analysis [article]

Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam
2022 arXiv   pre-print
Work will provide a taxonomy of adversarial evasion attacks on the basis of attack domain and adversarial generation techniques.  ...  The paper will introduce various machine learning techniques used to generate adversarial and explain the structure of target files.  ...  Among numerous available features, n-Grams [107] , [108] for byte sequence analysis and Opcode [109] , used to analyze the frequency of 'Operation Code' appearance are the most widely used ones.  ... 
arXiv:2111.08223v2 fatcat:fiw3pgunsvb2vo7uv72mp6b65a

Detecting Neural Network Functions in Binaries Based on Syntactic Features

Georg Aschl, Martina Lindorfer, Jakob Bleier
2020
We use reference implementations of these functions and compute fuzzy hashes of basic blocks belonging to loops using a sliding window.  ...  With on-device NN comes the need to also deploy the models to the device. These models are part of the intellectual property of the app's vendor and thus carry value.  ...  This thesis would not have been possible without her invaluable input as well as prompt and continuous feedback. Her motivating and reassuring words meant more to me than she could have imagined.  ... 
doi:10.34726/hss.2020.66352 fatcat:62arghwozzepthf25cu6gvkt5u