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Naeem Firdous Syed
Person information
- affiliation: Deakin University, Centre for Cyber Security Research and Innovation, Geelong, Australia
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2020 – today
- 2024
- [j11]Merwa Mehmood, Zubair A. Baig, Naeem Syed, Sherali Zeadally:
The Role of Rule Mining in Generating Synthetic Cyber-Physical System Attack Samples. IEEE Internet Things Mag. 7(6): 14-19 (2024) - [c10]Merwa Mehmood, Zubair A. Baig, Naeem Syed:
Securing Industrial Control Systems (ICS) Through Attack Modelling and Rule-Based Learning. COMSNETS 2024: 598-602 - [c9]Sri Harsha Mekala, Zubair A. Baig, Adnan Anwar, Naeem Syed:
Evaluation and Analysis of a Digital Forensic Readiness Framework for the IIoT. ISDFS 2024: 1-6 - 2023
- [j10]Naeem Firdous Syed
, Mengmeng Ge, Zubair A. Baig
Fog-cloud based intrusion detection system using Recurrent Neural Networks and feature selection for IoT networks. Comput. Networks 225: 109662 (2023) - [c8]Sri Harsha Mekala
DoS Attacks, Human Factors, and Evidence Extraction for the Industrial Internet of Things (IIoT) Paradigm. ASEW 2023: 32-39 - 2022
- [j9]Naeem Firdous Syed
Zero Trust Architecture (ZTA): A Comprehensive Survey. IEEE Access 10: 57143-57179 (2022) - [j8]Naeem Firdous Syed
Traceability in supply chains: A Cyber security analysis. Comput. Secur. 112: 102536 (2022) - [j7]Zubair A. Baig
Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning. Future Internet 14(7): 205 (2022) - [c7]Naeem Syed
Unsupervised Machine Learning for Drone Forensics through Flight Path Analysis. ISDFS 2022: 1-6 - 2021
- [j6]Mengmeng Ge
Towards a deep learning-driven intrusion detection approach for Internet of Things. Comput. Networks 186: 107784 (2021) - [j5]Syed W. Shah, Naeem Firdous Syed
LCDA: Lightweight Continuous Device-to-Device Authentication for a Zero Trust Architecture (ZTA). Comput. Secur. 108: 102351 (2021) - 2020
- [j4]Zubair A. Baig
Averaged dependence estimators for DoS attack detection in IoT networks. Future Gener. Comput. Syst. 102: 198-209 (2020) - [j3]Naeem Firdous Syed
Denial of service attack detection through machine learning for the IoT. J. Inf. Telecommun. 4(4): 482-503 (2020) - [c6]Syed W. Shah, Naeem Firdous Syed
Towards a Lightweight Continuous Authentication Protocol for Device-to-Device Communication. TrustCom 2020: 1119-1126 - [i2]Mengmeng Ge, Naeem Firdous Syed, Xiping Fu, Zubair A. Baig, Antonio Robles-Kelly:
Toward a Deep Learning-Driven Intrusion Detection Approach for Internet of Things. CoRR abs/2007.09342 (2020) - [i1]Syed W. Shah, Naeem Firdous Syed, Arash Shaghaghi, Adnan Anwar, Zubair A. Baig, Robin Doss:
Towards a Lightweight Continuous Authentication Protocol for Device-to-Device Communication. CoRR abs/2010.05144 (2020)
2010 – 2019
- 2019
- [c5]Mengmeng Ge, Xiping Fu, Naeem Firdous Syed
Deep Learning-Based Intrusion Detection for IoT Networks. PRDC 2019: 256-265 - 2017
- [j2]Zubair A. Baig
Future challenges for smart cities: Cyber-security and digital forensics. Digit. Investig. 22: 3-13 (2017) - [c4]Syed Naeem Firdous
Modelling and Evaluation of Malicious Attacks against the IoT MQTT Protocol. iThings/GreenCom/CPSCom/SmartData 2017: 748-755 - 2013
- [j1]Mohammed H. Sqalli, Syed Naeem Firdous
Classifying malicious activities in Honeynets using entropy and volume-based thresholds. Secur. Commun. Networks 6(5): 567-583 (2013) - 2011
- [c3]Mayez A. Al-Mouhamed, Irfan Ali Khan, Syed Naeem Firdous
A reliable peer-to-peer protocol for mobile Ad-Hoc wireless networks. AICCSA 2011: 32-37 - [c2]Mohammed H. Sqalli, Syed Naeem Firdous
Identifying network traffic features suitable for honeynet data analysis. CCECE 2011: 1044-1048 - [c1]Mohammed H. Sqalli, Syed Naeem Firdous
An Entropy and Volume-Based Approach for Identifying Malicious Activities in Honeynet Traffic. CW 2011: 23-30
Coauthor Index
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