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 firstname.lastname@example.org
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
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
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