| This page reports the list of student projects with the type (long or short), the contact person for each project ("@" is replaced by "=>"), the status (available or assigned) the warning level (low, medium or high; where high means that is strongly suggested to submit the project proposal head of time to not incur in delays). New projects will be added at the end. All other information related with the projects are available on the course pages on Canvas. |
| Number and Type | Title and Abstract | Supervisor | Status | Warning |
| 2 - short or long | AutoML for Side-Channel Analysis Description: Side-channel analysis (SCA) is an important aspect of modern hardware security and cryptography. It consists of acquiring physical measurements and analyzing them using machine learning. Any machine-learning processing pipeline can quickly get very large and complex: as a result, the hardware hacker may not be able to pick the optimal attack parameters. To that end, we have developed a framework that automates ML in the context of hardware security (called MetaHive), using heuristics like particle swarm optimization (PSO). Goals: -learn the basics of SCA and PSO -implement PSO in an existing python framework for automated ML -potential extensions: measure the effectiveness of PSO in optimizing the the parameters of a side-channel attack |
Kostas Papagiannopoulos k.papagiannopoulos=>uva.nl | available | low |
| 3 - long | Investigating Parsing Differentials in micro services Environments that use micro services often have a wide variety of programming languages and frameworks. Therefore, we suspect that parser differentials vulnerabilities are common in micro service architectures. For example how two libraries parse (malformed) JSON, HTTP requests etc. This could lead to interesting vulnerabilities that are hard to find. The goal of this project is to find such parser differentials in commonly used libraries (first period) and see if this could lead to real vulnerabilities (second period). Reference: https://www.sonarsource.com/blog/security-implications-of-url-parsing-differentials/ |
Matthijs Melissen
mmelissen=>computest.nl and Daan Keuper
dkeuper=>computest.nl |
available | low |
| 4 - short | Detecting DDoS DDoS attacks are still a large problem for internet-connected applications. In DDoS attacks, an attacker often abuses software of third parties without these third parties awareness. The goal of this project is to be able to test whether an organisation's internet-exposed network can be exploited to cause a DDoS. To do so, the student will first find out which amplification factor is in use for common software. Then the student will build a tool to verify if this software is running within the organisaton. Reference: https://www.shadowserver.org/news/over-18-8-million-ips-vulnerable-to-middlebox-tcp-reflection-ddos-attacks/ |
Matthijs Melissen mmelissen=>computest.nl and Daan Keuper dkeuper=>computest.nl | available | low |
| 6 - short | Interpreting automated tool output for cloud security testing There exist a lot of automated security tools for cloud environments (Azure, AWS and Google Cloud). An example of such a tool is Defender for Cloud. We notice that the results of these tools are often hard to interpret. During this project, the student will look for a tool that is able to map the technical findings originating from the automated tool to the customer's risk assessment. |
Matthijs Melissen mmelissen=>computest.nl and Daan Keuper dkeuper=>computest.nl | available | low |
| 7 - short | Hardware dropper In redteaming, we often try to get access to network ports at the customer location. These network ports might be in use, so in this case we would like to introduce a piece of hardware that acts as a transparent proxy for the existing user, and also allows the security tester to access the network. The device probably should have wifi and/or 5G, so that data can be exfiltrated invisible to the tested organisation. During this project, the student will create such a hardware dropper (perhaps using existing hardware), and implements the correct software to use the device. |
Matthijs Melissen mmelissen=>computest.nl and Daan Keuper dkeuper=>computest.nl | available | low |
| 11 - short or long | (CSA CAIQ or European Cybersecurity Compliance framework) into the DevSecOps pipeline for security improvement and risk assessment of applications Analise the structure of the selected (one of) compliance frameworks and map it to the typical (cloud-based) application architecture, suggest architecture design patterns. Identify what security controls can be used and how they can be applied to different CI/CD stages. |
Yuri Demchenko Y.Demschenko=>uva.nl | available | low |
| 13 - short | Malicious G-Code Detection in Additive Manufacturing Abstract: After how G-code can be abused by malicious users, a detection tool needs to be developed to prevent attackers from downloading/executing malicious G-code on a protected additive manufacturing machine/ 3d printer. Ideally, the detection needs to be efficient and robust. |
Chenglu Jin Chenglu.Jin=>cwi.nl | available | low |
| XX - short | Title Abstract |
Supervisor | available | low |
Below the Presentation Schedule (Final)
Room: B1.23
BBB: ask if you need
Username: ask if you need
Password: ask if you need
Monday 5 February 2024 |
||
| Time Slot | RP Number | Title |
| 9:00-9:25 | 40 | Improving Fingerprinting Protections in Web Browsers |
| 9:25-9:50 | 39 | Implementing Attention For Autoencoders In Side-Channel Attacks |
| 9:50-10:15 | 38 | A Broad Survey and Replication of ML-Based Side-Channel Attack Research |
| 10:15-10:40 | 26 - long | Side channel attacks on remote FPGAs |
| Short Break | ||
| 11:00-11:25 | 28 - long | Empowering Cybersecurity: Modifiable Environment for Analyzing and Monitoring Ransomware Behavior |
| 11:25-11:50 | 10 - long | Honeypots in the cloud |
| 11:50-12:15 | 5 - long | Browser-Powered Desync Attack: Obfuscating Content-Length in HTTP Protocol |
| Lunch Break | ||
| 13:00-13:25 | 37 - long | Evaluating vulnerabilities in TCP stack and developing a white box fuzzer for the common vulnerabilities in the TCP stack of operating systems |
| 13:25-13:50 | 42 | Blockchain technologies comparison and selection for decentralised application development |
| 13:50-14:15 | 9 | Automatic evidence processing and analysis in the cloud |
| 14:15-14:40 | 1 | Pipeline of Mass Disclosure |
| Short Break | ||
| 15:00-15:25 | 33 | Feature Selection on a Differentially Private Dataset |
| 15:25-15:50 | 32 | Breaching the isolation layer through Inter-Partition Communication in Hyper-V |
|
15:50-16:15 |
14 - RP2 |
Autoencoder for Detecting Malicious Model Updates in Differentially Private Federated Learning |
| 16:15-16:40 | 35 - RP2 | Energy Cost of
PETs - Differential Privacy |
| 16:40-17:05 | 36 - RP2 | Introducing Cryptographic Agility in Mobile Banking Application |
Tuesday 6 February 2024 |
||
| Time Slot | RP Number | Title |
| 9:00-9:25 | 34 | Implementing and Testing SCION in the FABRIC Infrastructure |
| 9:25-9:50 | 31 | Mitigating layer 7-based DDoS attacks in the SURF network |
| 9:50-10:15 | 30 | Analysis in Deepfake |
| 10:15-10:40 | 16 | Local Differential Privacy for Data Clustering |
| Short Break | ||
| 11:00-11:25 | 29 | Payload tracking |
| 11:25-11:50 | 27 | XDP-based DNS hot cache |
| 11:50-12:15 | 23 | P2P VPN solution for eduVPN using WireGuard |
| Lunch Break | ||
| 13:00-13:25 | 22 | Comparing approaches to Multi-Party Computation and Synthetic data in terms of energy consumption and privacy |
| 13:25-13:50 | 19 | Secret sharing based electronic voting for IoT |
| 13:50-14:15 | 18 | Using ZIP files to smuggle malware through scanners undetected |
| 14:15-14:40 | 15 | Secure Collaborative Data Sharing for Enhanced Machine Learning Insights |
| Short Break | ||
| 15:00-15:25 | 25 | Classifying Web-Based Attacks |
| 15:25-15:50 | 24 | State of the art
of log collection methods for security and application
monitoring purposes |
| 15:50-16:15 | 12 | Malicious
G-Code characterisation in Additive Manufacturing |
| 16:15-16:40 | 8 | Static Code Analysis using Large Language Models |
Date to be defined
|
||
| Time Slot | RP Number | Title |
| TO BE DEFINED | 41 - RP2 | Creating, Detecting and Preventing Malicious IaC Packages |
| TO BE DEFINED | 17 | Evaluating Fairness in k-Anonymized Datasets |