17 citations · 31 across the 8 of their papers we have counts for
8 papers
MVAM: Multi-variant Attacks on Memory for IoT Trust Computing
Arup Kumar Sarker, Md Khairul Islam, Yuan Tian
With the significant development of the Internet of Things and low-cost cloud services, the sensory and data processing requirements of IoT systems are continually going up. TrustZ…
ML-FEED: Machine Learning Framework for Efficient Exploit Detection
Tanujay Saha, Tamjid Al-Rahat, Najwa Aaraj +2
Machine learning (ML)-based methods have recently become attractive for detecting security vulnerability exploits. Unfortunately, state-of-the-art ML models like long short-term me…
One Bad Apple Spoils the Barrel: Understanding the Security Risks Introduced by Third-Party Components in IoT Firmware
Binbin Zhao, Shouling Ji, Jiacheng Xu +8
Currently, the development of IoT firmware heavily depends on third-party components (TPCs) to improve development efficiency. Nevertheless, TPCs are not secure, and the vulnerabil…
Conditional Supervised Contrastive Learning for Fair Text Classification
Jianfeng Chi, William Shand, Yaodong Yu +3
Contrastive representation learning has gained much attention due to its superior performance in learning representations from both image and sequential data. However, the learned…
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies
Md Rizwan Parvez, Jianfeng Chi, Wasi Uddin Ahmad +2
Prior studies in privacy policies frame the question answering (QA) task as identifying the most relevant text segment or a list of sentences from a policy document given a user qu…
A Coding Framework and Benchmark towards Low-Bitrate Video Understanding
Yuan Tian, Guo Lu, Yichao Yan +3
Video compression is indispensable to most video analysis systems. Despite saving transportation bandwidth, it also deteriorates downstream video understanding tasks, especially at…
Model-Targeted Poisoning Attacks with Provable Convergence
Fnu Suya, Saeed Mahloujifar, Anshuman Suri +2
In a poisoning attack, an adversary with control over a small fraction of the training data attempts to select that data in a way that induces a corrupted model that misbehaves in…
Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phase
Jianfeng Chi, Emmanuel Owusu, Xuwang Yin +4
We present a practical method for protecting data during the inference phase of deep learning based on bipartite topology threat modeling and an interactive adversarial deep networ…