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Found 11 papers, 3 papers with code
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
Existing defenses against poisoning attacks in FL have several limitations, such as relying on specific assumptions about attack types and strategies or data distributions or not sufficiently robust against advanced injection techniques and strategies and simultaneously maintaining the utility of the aggregated model.
DEMASQ: Unmasking the ChatGPT Wordsmith
The potential misuse of ChatGPT and other Large Language Models (LLMs) has raised concerns regarding the dissemination of false information, plagiarism, academic dishonesty, and fraudulent activities.
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks
Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning model without having them share their private data.
To ChatGPT, or not to ChatGPT: That is the question!
The dataset serves as a reference to assess the performance of various techniques in detecting ChatGPT-generated content.
ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks
ARGUS monitors the contextual setting based on the state and actions of IoT devices in the environment.
BayBFed: Bayesian Backdoor Defense for Federated Learning
However, as these approaches directly operate on client updates, their effectiveness depends on factors such as clients' data distribution or the adversary's attack strategies.
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning
When extended to new vulnerability types, ESCORT yields an average F1-score of 93%.
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS
Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates.
Peek-a-Boo: I see your smart home activities, even encrypted!
It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying particular types of IoT devices, their actions, states, and ongoing user activities in a cascading style by only observing passively the wireless traffic from smart home devices.
Cryptography and Security
Breaking Fitness Records without Moving: Reverse Engineering and Spoofing Fitbit
In this paper, we provide an in-depth security analysis of the operation of fitness trackers commercialized by Fitbit, the wearables market leader.
Cryptography and Security
Android Code Protection via Obfuscation Techniques: Past, Present and Future Directions
We believe that, there is a need to investigate efficiency of the defense techniques used for code protection.
Cryptography and Security
