Kevin Choi

Soumya Banerjee, Sandip Roy, Sayyed Farid Ahamed et al.

The membership inference attack (MIA) is a popular paradigm for compromising the privacy of a machine learning (ML) model. MIA exploits the natural inclination of ML models to overfit upon the training data. MIAs are trained to distinguish between training and testing prediction confidence to infer membership information. Federated Learning (FL) is a privacy-preserving ML paradigm that enables multiple clients to train a unified model without disclosing their private data. In this paper, we propose an enhanced Membership Inference Attack with the Batch-wise generated Attack Dataset (MIA-BAD), a modification to the MIA approach. We investigate that the MIA is more accurate when the attack dataset is generated batch-wise. This quantitatively decreases the attack dataset while qualitatively improving it. We show how training an ML model through FL, has some distinct advantages and investigate how the threat introduced with the proposed MIA-BAD approach can be mitigated with FL approaches. Finally, we demonstrate the qualitative effects of the proposed MIA-BAD methodology by conducting extensive experiments with various target datasets, variable numbers of federated clients, and training batch sizes.

Deepak Kushwaha, Dhruv Nandakumar, Akshay Kakkar et al.

Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until they reach the ultimate target of their attack. Lateral Movement intrusions have become more intricate with the increasing complexity and interconnected nature of enterprise networks, and require equally sophisticated detection mechanisms to proactively detect such threats in near real-time at enterprise scale. In this paper, the authors propose a novel, lightweight method for Lateral Movement detection using user behavioral analysis and machine learning. Specifically, this paper introduces a novel methodology for cyber domain-specific feature engineering that identifies Lateral Movement behavior on a per-user basis. Furthermore, the engineered features have also been used to develop two supervised machine learning models for Lateral Movement identification that have demonstrably outperformed models previously seen in literature while maintaining robust performance on datasets with high class imbalance. The models and methodology introduced in this paper have also been designed in collaboration with security operators to be relevant and interpretable in order to maximize impact and minimize time to value as a cyber threat detection toolkit. The underlying goal of the paper is to provide a computationally efficient, domain-specific approach to near real-time Lateral Movement detection that is interpretable and robust to enterprise-scale data volumes and class imbalance.

Marc Vucovich, Devin Quinn, Kevin Choi et al.

Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. The main goal of a federated learning architecture is to protect the privacy of each client while still contributing to the training of the global model. However, the main advantage of privacy in federated learning is also the easiest aspect to exploit. Without being able to see the clients' data, it is difficult to determine the quality of the data. By utilizing data poisoning methods, such as backdoor or label-flipping attacks, or by sending manipulated information about their data back to the server, malicious clients are able to corrupt the global model and degrade performance across all clients within a federation. Our novel aggregation method, FedBayes, mitigates the effect of a malicious client by calculating the probabilities of a client's model weights given to the prior model's weights using Bayesian statistics. Our results show that this approach negates the effects of malicious clients and protects the overall federation.

Padmaksha Roy, Tyler Cody, Himanshu Singhal et al.

Zero-day anomaly detection is critical in industrial applications where novel, unforeseen threats can compromise system integrity and safety. Traditional detection systems often fail to identify these unseen anomalies due to their reliance on in-distribution data. Domain generalization addresses this gap by leveraging knowledge from multiple known domains to detect out-of-distribution events. In this work, we introduce a multi-task representation learning technique that fuses information across related domains into a unified latent space. By jointly optimizing classification, reconstruction, and mutual information regularization losses, our method learns a minimal(bottleneck), domain-invariant representation that discards spurious correlations. This latent space decorrelation enhances generalization, enabling the detection of anomalies in unseen domains. Our experimental results demonstrate significant improvements in zero-day or novel anomaly detection across diverse anomaly detection datasets.