Student Projects Completed in 2021-2022

Fall 2021 Student Projects

  • Students: Rohit Bhat

    Faculty Mentor: Matthew Green

    Abstract: We examine Nano, a digital cash blockchain protocol that eliminates mining by validating transactions asynchronously. We evaluate the consensus algorithms of Bitcoin and Nano, focusing on value transfer to observe emergent network effects regarding security, decentralization, and scalability. These properties are measured in their appropriate contexts: security regarding transaction finality, decentralization regarding the Nakamoto Coefficient, and scalability regarding transactions per second.

  • Students: Connor Gephart

    Faculty Mentor: Reuben Johnston

    Abstract: Previous studies have looked to adapt frameworks to specific uses or demonstrate particular implementations, but none have practically compared frameworks to determine their best use cases. This research analyzes the NIST Cybersecurity Framework and the MITRE Engage framework using Red Canary’s top 10 MITRE ATT&CK techniques to determine which framework better addresses common threats. Each framework was applied to an individual threat to determine what steps would be necessary to mitigate or prevent the threat. The ATT&CK techniques were tested on a Windows 10 system to model real-world end-user systems. The MITRE Engage framework is interesting in that it is designed to enhance the understanding of an adversary in addition to mitigating their threat. The NIST CSF is distinct because it deals with more general business practices than the MITRE Engage Framework. Both the NIST CSF and the MITRE Engage framework mitigate all ten threats, however, neither gives precise enough recommendations to use either framework alone to make active defensive decisions.

  • Students: Tyler Ramdass, Brittany Stewart, Eric Santorelli

    Faculty Mentor: Tim Leschke

    Abstract: Digital forensics is an ever-expanding field of research as computers, criminals, and investigators trying to catch criminals continue to employ more advanced techniques. As the research space for non-volatile memory is increasing exponentially, volatile memory is lacking. Tools such as Autopsy provide an examiner, novice or expert, the opportunity to complete an exam on a computer’s non-volatile memory successfully. However, loads of information, such as passwords and temporary files, are lost to the ignorance of those who do not understand the vitality of volatile memory. The research provided in this paper is meant to act as a springboard for future research into the executable integration of Volatility, an open-source volatile memory tool, in the Autopsy framework. This paper will cover the background needed to understand memory, a literature review, which includes the current state-of-the-art, a look into our attempt to integrate the software, a usability test to be used once the deliverable is completed, and, finally, our hope for future research and a reflection into what could have gone better.

  • Students: Xueming Feng

    Faculty Mentor: Xiangyang Li

    Abstract: This research first identifies the current network environment is hostile for small businesses and startups to host their service on the Internet. Then reviews Cloudflare’s past and current technologies against flooding attacks. From their technologies, select the following techniques to do further performance evaluation: iptables, BPF, and XDP. This paper analyzed the pros and cons of the above techniques. Then in a custom build environment that includes an victim, an adversary, and a normal user. We write our own tools that utilize the above techniques to mitigate incoming UDP floods. Along with a packet generator that is able to simulate UDP flood, we put our tools in place to try to stop the flood. We gathered data and benchmarked the tools we created against huge amount of traffic and under different CPU frequencies. Then we evaluated the performance of there techniques from the data sets we gathered. Finally, we provided the suggestion of the best techniques to mitigate the denial of service attack.

  • Students: Qi Cheng

    Faculty Mentor: Xiangyang Li

    External Mentor: Dr. Leah Ding (American University)

    Research Assistant: Anyi Xu (American University)

    Abstract: Machine learning-based spam detection models learn from a set of labeled training data and detect spam emails after the training phase. We study a class of vulnerabilities of such detection models, where the attack can manipulate a trained model to misclassify maliciously crafted spam emails at the detection phase. However, very often feature extraction methods make it very difficult to translate the change in the feature space to that in the textual email space. This paper proposes a new attack method of making guided changes to text data by taking advantage of findings of generated adversarial examples that purposely modify the features representing an email. We study different feature extraction methods using various Natural Language Processing (NLP) techniques. We develop effective methods to translate adversarial perturbations in the feature space back to a set of “magic words”, or malicious words, in the text space, which can cause desirable misclassifications from the attacker’s perspective. We show that our attacks are effective across different datasets and various machine learning methods in white-box, gray-box, and black-box attack settings. In addition, we also got preliminary result for our methodology on deep learning based-spam filters. Finally, we discuss preliminary exploration to counter such attacks. We hope our findings and analysis will allow future work to perform additional studies of defensive solutions against this new class of attacks.

  • Students: Aaron Wu, Hyoeun Choi

    Faculty Mentor: Xiangyang Li

    Abstract: In this project, we Implement a full stack video sharing social platform integrated into bitcoin(BTC) mainnet. – platform. The tech stack consists of serverless AWS lambda, React, Node.js AWS S3, Azure function. We keep high code quality and follow the best security practices (avoiding address reuse) to protect investor’s digital assets. We also launched a congestion attack on the lighting node. As a result,  a force close to the channel. Finally, we evaluated the performance between Lightning Network and BTC mainnet and discussed future work for the video sharing social platform.

JHU Information Security Institute