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Aminollah Khormali
Researcher at University of Central Florida
Publications - 29
Citations - 569
Aminollah Khormali is an academic researcher from University of Central Florida. The author has contributed to research in topics: Malware & Adversarial machine learning. The author has an hindex of 9, co-authored 27 publications receiving 314 citations. Previous affiliations of Aminollah Khormali include K.N.Toosi University of Technology.
Papers
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Journal ArticleDOI
Analyzing and Detecting Emerging Internet of Things Malware: A Graph-Based Approach
Hisham Alasmary,Aminollah Khormali,Afsah Anwar,Jeman Park,Jinchun Choi,Ahmed Abusnaina,Amro Awad,DaeHun Nyang,Aziz Mohaisen +8 more
TL;DR: This paper builds a detection mechanism of IoT malware utilizing control flow graphs (CFGs), and shows that IoT malware samples have a large number of edges despite a smaller number of nodes, which demonstrate a richer flow structure and higher complexity.
Journal ArticleDOI
Control chart pattern recognition using RBF neural network with new training algorithm and practical features.
TL;DR: A new method based on optimized radial basis function neural network (RBFNN) is proposed for control chart patterns (CCPs) recognition and the results showed that the proposed method has a very good performance.
Proceedings ArticleDOI
Adversarial Learning Attacks on Graph-based IoT Malware Detection Systems
TL;DR: The findings of this work highlight the essential need for more robust detection tools against adversarial learning, including features that are not easy to manipulate, unlike CFG-based features.
Journal ArticleDOI
A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine.
Aminollah Khormali,Jalil Addeh +1 more
TL;DR: A multiclass SVM (SVM) based classifier is proposed because of the promising generalization capability of support vector machines, and cuckoo optimization algorithm (COA) is proposed for selecting appropriate parameters of the classifier.
Proceedings ArticleDOI
DFD: Adversarial Learning-based Approach to Defend Against Website Fingerprinting
TL;DR: This paper proposes a novel defense mechanism using a per-burst injection technique, called Deep Fingerprinting Defender (DFD), against deep learning-based WF attacks, and demonstrates that DFD outperforms the state-of-the-art alternatives by requiring lower bandwidth overhead.