arXiv subjects

Debdeep Mukhopadhyay

Publications and source records attributed to Debdeep Mukhopadhyay.

23 records · Page 2Linked to original sources

arXiv2018

How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?

Deep Learning algorithms have recently become the de-facto paradigm for various prediction problems, which include many privacy-preserving applications like online medical image analysis. Presumably, the privacy of data in a deep learning system is a serious concern. There have been several efforts to analyze and exploit the information leakages from deep learning architectures to compromise data privacy. In this paper, however, we attempt to provide an evaluation strategy for such information leakages through deep neural network architectures by considering a case study on Convolutional Neural Network (CNN) based image classifier. The approach takes the aid of low-level hardware information, provided by Hardware Performance Counters (HPCs), during the execution of a CNN classifier and a simple hypothesis testing in order to produce an alarm if there exists any information leakage on the actual input.

cs.LG↗

arXiv2018

Testability Analysis of PUFs Leveraging Correlation-Spectra in Boolean Functions

Testability of digital ICs rely on the principle of controllability and observability. Adopting conventional techniques like scan-chains open up avenues for attacks, and hence cannot be adopted in a straight-forward manner for security chips. Furthermore, testing becomes incredibly challenging for the promising class of hardware security primitives, called PUFs, which offer unique properties like unclonability, unpredictibility, uniformity, uniqueness, and yet easily computable. However, the definition of PUF itself poses a challenge on test engineers, simply because it has no golden response for a given input, often called challenge. In this paper, we develop a novel test strategy considering that the fabrication of a batch of $N>1$ PUFs is equivalent to drawing random instances of Boolean mappings. We hence model the PUFs as black-box Boolean functions of dimension $m\times1$, and show combinatorially that random designs of such functions exhibit correlation-spectra which can be used to characterize random and thus {\em good} designs of PUFs. We first develop theoretical results to quantize the correlation values, and subsequently the expected number of pairs of such Boolean functions which should belong to a given spectra. In addition to this, we show through extensive experimental results that a randomly chosen sample of such PUFs also resemble the correlation-spectra property of the overall PUF population. Interestingly, we show through experimental results on $50$ FPGAs that when the PUFs are infected by faults the usual randomness tests for the PUF outputs such as uniformity, fail to detect any aberration. However, the spectral-pattern is clearly shown to get affected, which we demonstrate by standard statistical tools. We finally propose a systematic testing framework for the evaluation of PUFs by observing the correlation-spectra of the PUF instances under test.

cs.CR↗

arXiv2018

Adversarial Attacks and Defences: A Survey

Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number of tasks. As a consequence, deep learning is being extensively used in most of the recent day-to-day applications. However, security of deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify the output. In recent times, different types of adversaries based on their threat model leverage these vulnerabilities to compromise a deep learning system where adversaries have high incentives. Hence, it is extremely important to provide robustness to deep learning algorithms against these adversaries. However, there are only a few strong countermeasures which can be used in all types of attack scenarios to design a robust deep learning system. In this paper, we attempt to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate the efficiency and challenges of recent countermeasures against them.

cs.LG↗

arXiv2018

Cryptographically Secure Multi-Tenant Provisioning of FPGAs

FPGAs (Field Programmable Gate arrays) have gained massive popularity today as accelerators for a variety of workloads, including big data analytics, and parallel and distributed computing. This has fueled the study of mechanisms to provision FPGAs among multiple tenants as general purpose computing resources on the cloud. Such mechanisms offer new challenges, such as ensuring IP protection and bitstream confidentiality for mutually distrusting clients sharing the same FPGA. A direct adoption of existing IP protection techniques from the single tenancy setting do not completely address these challenges, and are also not scalable enough for practical deployment. In this paper, we propose a dedicated and scalable framework for secure multi-tenant FPGA provisioning that can be easily integrated into existing cloud-based infrastructures such as OpenStack. Our technique has constant resource/memory overhead irrespective of the number of tenants sharing a given FPGA, and is provably secure under well-studied cryptographic assumptions. A prototype implementation of our proposition on Xilinx Virtex-7 UltraScale FPGAs is presented to validate its overheads and scalability when supporting multiple tenants and workloads. To the best of our knowledge, this is the first FPGA provisioning framework to be prototyped that achieves a desirable balance between security and scalability in the multi-tenancy setting.

cs.CR↗

arXiv2018

RAPPER: Ransomware Prevention via Performance Counters

Ransomware can produce direct and controllable economic loss, which makes it one of the most prominent threats in cyber security. As per the latest statistics, more than half of malwares reported in Q1 of 2017 are ransomware and there is a potent threat of a novice cybercriminals accessing rasomware-as-a-service. The concept of public-key based data kidnapping and subsequent extortion was introduced in 1996. Since then, variants of ransomware emerged with different cryptosystems and larger key sizes though, the underlying techniques remained same. Though there are works in literature which proposes a generic framework to detect the crypto ransomwares, we present a two step unsupervised detection tool which when suspects a process activity to be malicious, issues an alarm for further analysis to be carried in the second step and detects it with minimal traces. The two step detection framework- RAPPER uses Artificial Neural Network and Fast Fourier Transformation to develop a highly accurate, fast and reliable solution to ransomware detection using minimal trace points.

cs.CR↗