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Intelligent Digital Forensics for Investigating Botnets in IoT-based Attacks (Advert Ref: SF22/EE/CIS/ASLAM)


   Faculty of Engineering and Environment

  Dr N Aslam  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

With a wide variety of applications, such as home automation, smart grids/cities etc. the IoT systems make compelling targets for cyber-attacks. Network Forensics is the branch of Digital Forensics, where the evidence is network-related and exist in the form of logs, packets and network flows. Popular methods of investigating botnets include Honeypot, Network flow analysis, Intrusion detection systems, Visualization of Network traffic, Deep Packet Analysis etc. Multiple deep learning solutions have been proposed for application in the field of Network Forensics in recent years. Some reported research used stacked auto-encoders in their implementation of a DDoS detection system for software defined networks. The multiple auto-encoders were greedily trained layer-by-layer, with the output of one layer being the input of the next. Then the entire network was fine-tuned as a classifier. Reported accuracy for distinguishing between normal and attack traffic was 99.82%, outperforming other classification methods such as shallow NN, while individual types of DDoS attacks were identified with an accuracy of 95.65%. We will explore using a combination of a one-dimensional CNN and stacked auto-encoders for automatic feature extraction and classification of network traffic, achieving both application identification and traffic characterization in either encrypted or unencrypted traffic. This project will explore the use of Recurrent Neural Network (RNN), Convolutional Neural Networks (CNN), Deep Auto Encoder (DAE), Deep Boltzman Machine (DBM) and Deep Belief Network (DBN), alongside some of the network forensics methods, whereby botnets in IoT can be effectively investigated and mitigated through detection.

For informal enquiries please contact

Eligibility and How to Apply: 

Please note eligibility requirement:  

·               Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement. 

·               Appropriate IELTS score, if required. 

For further details of how to apply, entry requirements and the application form, see 

https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/  

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF22/…) will not be considered. 

Start Date: 1 October 2022 


Funding Notes

Please note this is a self-funded project and does not include tuition fees or stipend.

References

Sami Smadi, N Aslam, L. Zhang, (2018). "Detection of online phishing email using dynamic evolving neural network based on reinforcement learning”, Elsevier Decision Support Systems, Vol 107, pages 88 – 102, March 2018.
M. Aluthaman, N Aslam, L. Zhang and R. Aslem, (2017). "A P2P Botnet Detection Scheme based on Decision Tree and Adaptive Multi-layer Neural Networks”, Journal of Neural Computing and Applications, 2017
S Doswell, D Kendall, N Aslam, G Sexton, (2015). “A longitudinal approach to measuring the impact of mobility on low-latency anonymity networks” International Wireless Communications and Mobile Computing Conference (IWCMC), Croatia, 2015
Stephen Doswell, Nauman Aslam, David Kendall and Graham Sexton, (2013)."Please slow down! The impact on Tor performance from mobility", 3rd Annual ACM CCS Workshop on Security and Privacy in Smartphones and Mobile Devices (SPSM), in conjunction with the 20th ACM Conference on Computer and Communications Security (CCS), Berlin, Germany, November 4-8, 2013
A Moh’d, N Aslam, W Phillips and W Robertson (2013). “A dual-mode energy efficient encryption protocol for wireless sensor networks” Elsevier Ad hoc Networks, Volume 11, Issue 8, November 2013, Pages 2588–2604
Barraclough, P. A., Hossain M. A., Tahir M., and Aslam, N., (2013). “Intelligent phishing detection and protection scheme for online transactions”, Elsevier Science Expert Systems with Applications, 40 (2013) 4697–4706, 2013
Pye, J., Issac, B. Aslam, N. & Rafiq, H. (2020, December). Android Malware Classification Using Machine Learning and Bio-Inspired Optimisation Algorithms, Proceedings of 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020), Guangzhou, China.
Stobbs, J., Issac, B. & Jacob, S. M. (2020, December). Phishing Web Page Detection using Optimised Machine Learning, Proceedings of the 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020), Guangzhou, China.
Gibson, S., Issac, B., Zhang, L. & Jacob, S. M. (2020). Detecting Spam Email with Machine Learning Optimized with Bio-Inspired Metaheuristic Algorithms, IEEE Access, IEEE, ISSN 2169-3536.
Sharma, R., Issac, B. & K., Kalita, H. R. (2019). Intrusion Detection and Response System Inspired by the Defense Mechanism of Plants, IEEE Access, IEEE, ISSN 2169-3536, vol. 7, 52427-52439.
Shah, S. A. R., & Issac, B. (2018). Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System, Future Generation Computer Systems, Elsevier, ISSN 0167-739X, Vol. 80, 157-170.

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