H
Hadis Karimipour
Researcher at University of Guelph
Publications - 120
Citations - 3406
Hadis Karimipour is an academic researcher from University of Guelph. The author has contributed to research in topics: Computer science & Smart grid. The author has an hindex of 23, co-authored 99 publications receiving 1654 citations. Previous affiliations of Hadis Karimipour include University of Calgary & University of Alberta.
Papers
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Machine learning based solutions for security of Internet of Things (IoT): A survey
TL;DR: The architecture of IoT is discussed, following a comprehensive literature review on ML approaches the importance of security of IoT in terms of different types of possible attacks, and ML-based potential solutions for IoT security has been presented and future challenges are discussed.
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Cyber intrusion detection by combined feature selection algorithm
TL;DR: This paper proposes an IDS based on feature selection and clustering algorithm using filter and wrapper methods that has a high accuracy and detection rate with a low false positive rate compared to the existing methods in the literature.
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A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids
TL;DR: The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack.
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A survey on internet of things security: Requirements, challenges, and solutions
TL;DR: A taxonomy that taps into the three-layer IoT architecture as a reference to identify security properties and requirements for each layer is built upon, classifying the potential IoT security threat and challenges by an architectural view.
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Fuzzy Pattern Tree for Edge Malware Detection and Categorization in IoT
Enisieh Modiri Dovom,Amin Azmoodeh,Ali Dehghantanha,David Ellis Newton,Reza M. Parizi,Hadis Karimipour +5 more
TL;DR: This study transmute the programs’ OpCodes into a vector space and employ fuzzy and fast fuzzy pattern tree methods for malware detection and categorization, obtaining a high degree of accuracy during reasonable run-times especially for the fast fuzzypattern tree.