Raghavendra Pradyumna Pothukuchi

Hyoungwook Nam, Raghavendra Pradyumna Pothukuchi, Bo Li et al.

Side-channel attacks that use machine learning (ML) for signal analysis have become prominent threats to computer security, as ML models easily find patterns in signals. To address this problem, this paper explores using Adversarial Machine Learning (AML) methods as a defense at the computer architecture layer to obfuscate side channels. We call this approach Defensive ML, and the generator to obfuscate signals, defender. Defensive ML is a workflow to design, implement, train, and deploy defenders for different environments. First, we design a defender architecture given the physical characteristics and hardware constraints of the side-channel. Next, we use our DefenderGAN structure to train the defender. Finally, we apply defensive ML to thwart two side-channel attacks: one based on memory contention and the other on application power. The former uses a hardware defender with ns-level response time that attains a high level of security with half the performance impact of a traditional scheme; the latter uses a software defender with ms-level response time that provides better security than a traditional scheme with only 70% of its power overhead.

Ketaki Joshi, Raghavendra Pradyumna Pothukuchi, Andre Wibisono et al.

Continual learning on sequential data is critical for many machine learning (ML) deployments. Unfortunately, LSTM networks, which are commonly used to learn on sequential data, suffer from catastrophic forgetting and are limited in their ability to learn multiple tasks continually. We discover that catastrophic forgetting in LSTM networks can be overcome in two novel and readily-implementable ways -- separating the LSTM memory either for each task or for each target label. Our approach eschews the need for explicit regularization, hypernetworks, and other complex methods. We quantify the benefits of our approach on recently-proposed LSTM networks for computer memory access prefetching, an important sequential learning problem in ML-based computer system optimization. Compared to state-of-the-art weight regularization methods to mitigate catastrophic forgetting, our approach is simple, effective, and enables faster learning. We also show that our proposal enables the use of small, non-regularized LSTM networks for complex natural language processing in the offline learning scenario, which was previously considered difficult.

Raghavendra Pradyumna Pothukuchi, Sweta Yamini Pothukuchi, Petros Voulgaris et al.

The security of computers is at risk because of information leaking through physical outputs such as power, temperature, or electromagnetic (EM) emissions. Attackers can use advanced signal measurement and analysis to recover sensitive data from these sidechannels. To address this problem, this paper presents Maya, a simple and effective solution against power side-channels. The idea is to re-shape the power dissipated by an application in an application-transparent manner using control theory techniques - preventing attackers from learning any information. With control theory, a controller can reliably keep power close to a desired target value even when runtime conditions change unpredictably. Then, by changing these targets intelligently, power can be made to appear in any desired form, appearing to carry activity information which, in reality, is unrelated to the application. Maya can be implemented in privileged software or in simple hardware. In this paper, we implement Maya on two multiprocessor machines using Operating System (OS) threads, and show its effectiveness and ease of deployment.