| Intelligent mobile malware detection using permission requests and API calls M Alazab, M Alazab, A Shalaginov, A Mesleh, A Awajan Future Generation Computer Systems 107, 509-521, 2020 | 185 | 2020 |
| Machine learning aided static malware analysis: A survey and tutorial A Shalaginov, S Banin, A Dehghantanha, K Franke Cyber threat intelligence, 7-45, 2018 | 119 | 2018 |
| Deep graph neural network-based spammer detection under the perspective of heterogeneous cyberspace Z Guo, L Tang, T Guo, K Yu, M Alazab, A Shalaginov Future generation computer systems 117, 205-218, 2021 | 105 | 2021 |
| Decentralized self-enforcing trust management system for social Internet of Things MA Azad, S Bag, F Hao, A Shalaginov IEEE Internet of Things Journal 7 (4), 2690-2703, 2020 | 67 | 2020 |
| BCFL logging: An approach to acquire and preserve admissible digital forensics evidence in cloud ecosystem K Awuson-David, T Al-Hadhrami, M Alazab, N Shah, A Shalaginov Future Generation Computer Systems 122, 1-13, 2021 | 33 | 2021 |
| Cyber crime investigations in the era of big data A Shalaginov, JW Johnsen, K Franke 2017 IEEE International Conference on Big Data (Big Data), 3672-3676, 2017 | 30 | 2017 |
| A new method for an optimal som size determination in neuro-fuzzy for the digital forensics applications A Shalaginov, K Franke International Work-Conference on Artificial Neural Networks, 549-563, 2015 | 30 | 2015 |
| Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness A Shalaginov, K Franke Applied Soft Computing 52, 359-375, 2017 | 25 | 2017 |
| Understanding Neuro-Fuzzy on a class of multinomial malware detection problems A Shalaginov, LS Grini, K Franke International Joint Conference on Neural Networks (IJCNN) 2016, 684-691, 2016 | 24 | 2016 |
| MEML: Resource-aware MQTT-based machine learning for network attacks detection on IoT edge devices A Shalaginov, O Semeniuta, M Alazab Proceedings of the 12th IEEE/ACM International Conference on Utility and …, 2019 | 20 | 2019 |
| Malware Beaconing Detection by Mining Large-scale DNS Logs for Targeted Attack Identification A Shalaginov, K Franke, X Huang 18th International Conference on Computational Intelligence in Security …, 2016 | 20 | 2016 |
| Iot digital forensics readiness in the edge: A roadmap for acquiring digital evidences from intelligent smart applications A Shalaginov, A Iqbal, J Olegård Edge Computing–EDGE 2020: 4th International Conference, Held as Part of the …, 2020 | 19 | 2020 |
| Malware Analysis Using Artificial Intelligence and Deep Learning M Stamp, M Alazab, A Shalaginov Springer Nature, 2020 | 19 | 2020 |
| A new method of fuzzy patches construction in Neuro-Fuzzy for malware detection A Shalaginov, K Franke IFSA-EUSFLAT, 2015 | 18 | 2015 |
| Predicting likelihood of legitimate data loss in email DLP MF Faiz, J Arshad, M Alazab, A Shalaginov Future Generation Computer Systems 110, 744-757, 2020 | 17 | 2020 |
| Memory access patterns for malware detection S Banin, A Shalaginov, K Franke NISK, 2016 | 17 | 2016 |
| Study of Soft Computing methods for large-scale multinomial malware types and families detection LS Grini, A Shalaginov, K Franke The 6th World Conference on Soft Computing, 2016 | 17 | 2016 |
| Automated intelligent multinomial classification of malware species using dynamic behavioural analysis A Shalaginov, K Franke IEEE Privacy, Security and Trust 2016, 2016 | 16 | 2016 |
| Automated Generation of Fuzzy Rules from Large-scale Network Traffic Analysis in Digital Forensics Investigations A Shalaginov, K Franke 7th International Conference on Soft Computing and Pattern Recognition …, 2015 | 15 | 2015 |
| Automatic rule-mining for malware detection employing neuro-fuzzy approach A Shalaginov, K Franke Norsk informasjonssikkerhetskonferanse (NISK) 2013, 2013 | 15 | 2013 |
Katrin FrankeNTNU - Norwegian University of Science and TechnologyVerified email at ntnu.no
