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A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting
Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing
- O. Osanaiye, Haibin Cai, K. Choo, A. Dehghantanha, Zheng Xu, M. Dlodlo
- Computer ScienceEURASIP J. Wirel. Commun. Netw.
- 10 May 2016
An ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection that can effectively reduce the number of features and has a high detection rate and classification accuracy when compared to other classification techniques.
Internet of Things security and forensics: Challenges and opportunities
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning
- Amin Azmoodeh, A. Dehghantanha, Kim-Kwang Raymond Choo
- Computer ScienceIEEE Transactions on Sustainable Computing
This paper transmute OpCodes into a vector space and applies a deep Eigenspace learning approach to classify malicious and benign applications and presents a deep learning based method to detect Internet of Battlefield Things malware via the device’s Operational Code (OpCode) sequence.
A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks
- Hamed Haddad Pajouh, R. Javidan, R. Khayami, A. Dehghantanha, Kim-Kwang Raymond Choo
- Computer ScienceIEEE Transactions on Emerging Topics in Computing
- 1 April 2019
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.
A survey on security and privacy of federated learning
Forensics investigation challenges in cloud computing environments
- Mohsen Damshenas, A. Dehghantanha, R. Mahmod, S. Shamsuddin
- Computer ScienceProceedings Title: International Conference on…
- 26 June 2012
This paper suggests a simple yet very useful solution to conquer the aforementioned issues in forensic investigation of cloud systems by utilizing TPM in hypervisor, implementing multi-factor authentication and updating the cloud service provider policy to provide persistent storage devices.
Investigating the antecedents to the adoption of SCRM technologies by start-up companies
Machine learning aided Android malware classification
Detecting crypto-ransomware in IoT networks based on energy consumption footprint
- Amin Azmoodeh, A. Dehghantanha, M. Conti, Kim-Kwang Raymond Choo
- Computer ScienceJ. Ambient Intell. Humaniz. Comput.
- 1 August 2018
This paper presents a machine learning based approach to detect ransomware attacks by monitoring power consumption of Android devices and demonstrates that the proposed approach outperforms K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest in terms of accuracy rate, recall rate, precision rate and F-measure.