A
Ali Dehghantanha
Researcher at University of Guelph
Publications - 262
Citations - 10955
Ali Dehghantanha is an academic researcher from University of Guelph. The author has contributed to research in topics: Malware & Cloud computing. The author has an hindex of 41, co-authored 241 publications receiving 6323 citations. Previous affiliations of Ali Dehghantanha include Pacific Institute & University of Salford.
Papers
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Journal ArticleDOI
A survey on security and privacy of federated learning
Viraaji Mothukuri,Reza M. Parizi,Seyedamin Pouriyeh,Yan Huang,Ali Dehghantanha,Gautam Srivastava,Gautam Srivastava +6 more
TL;DR: This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible.
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Internet of Things security and forensics: Challenges and opportunities
TL;DR: This paper first introduces existing major security and forensics challenges within IoT domain and then briefly discusses about papers published in this special issue targeting identified challenges.
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A systematic literature review of blockchain cyber security
TL;DR: It is shown that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII).
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A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks
TL;DR: A novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks is presented.
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A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting
Hamed HaddadPajouh,Ali Dehghantanha,Raouf Khayami,Kim-Kwang Raymond Choo,Kim-Kwang Raymond Choo +4 more
TL;DR: The potential of using Recurrent Neural Network (RNN) deep learning in detecting IoT malware by using RNN to analyze ARM-based IoT applications’ execution operation codes (OpCodes) is explored.