Anupam Chattopadhyay

Bio: Anupam Chattopadhyay is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Logic synthesis. The author has an hindex of 23, co-authored 282 publications receiving 3002 citations. Previous affiliations of Anupam Chattopadhyay include University of Florida & RWTH Aachen University.

Adversarial Attacks and Defences: A Survey.

Abstract: 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.

Wireless Communication and Security Issues for Cyber–Physical Systems and the Internet-of-Things

Abstract: Wireless sensors and actuators connected by the Internet-of-Things (IoT) are central to the design of advanced cyber–physical systems (CPSs). In such complex, heterogeneous systems, communication links must meet stringent requirements on throughput, latency, and range, while adhering to tight energy budget and providing high levels of security. In this paper, we first summarize wireless communication principles from the perspective of the connectivity needs of IoT and CPS. Based on these principles, we then review the most relevant wireless communication standards before focusing on the key security issues and features of such systems. In particular, the gap between the security features in the communication standards used in CPSs and IoT and their actual vulnerabilities are pointed out with practical examples and recent attacks. We emphasize the need for a more in-depth study of the security issues across all the protocol layers, including both logical layer security and physical layer security.

Multi-valued and Fuzzy Logic Realization using TaOx Memristive Devices

Abstract: Among emerging non-volatile storage technologies, redox-based resistive switching Random Access Memory (ReRAM) is a prominent one. The realization of Boolean logic functionalities using ReRAM adds an extra edge to this technology. Recently, 7-state ReRAM devices were used to realize ternary arithmetic circuits, which opens up the computing space beyond traditional binary values. In this manuscript, we report realization of multi-valued and fuzzy logic operators with a representative application using ReRAM devices. Multi-valued logic (MVL), such as Łukasiewicz logic generalizes Boolean logic by allowing more than two truth values. MVL also permits operations on fuzzy sets, where, in contrast to standard crisp logic, an element is permitted to have a degree of membership to a given set. Fuzzy operations generally model human reasoning better than Boolean logic operations, which is predominant in current computing technologies. When the available information for the modelling of a system is imprecise and incomplete, fuzzy logic provides an excellent framework for the system design. Practical applications of fuzzy logic include, industrial control systems, robotics, and in general, design of expert systems through knowledge-based reasoning. Our experimental results show, for the first time, that it is possible to model fuzzy logic natively using multi-state memristive devices.

The Programmable Logic-in-Memory (PLiM) computer

Abstract: Realization of logic and storage operations in memristive circuits have opened up a promising research direction of in-memory computing. Elementary digital circuits, e.g., Boolean arithmetic circuits, can be economically realized within memristive circuits with a limited performance overhead as compared to the standard computation paradigms. This paper takes a major step along this direction by proposing a fully-programmable in-memory computing system. In particular, we address, for the first time, the question of controlling the in-memory computation, by proposing a lightweight unit managing the operations performed on a memristive array. Assembly-level programming abstraction is achieved by a natively-implemented majority and complement operator. This platform enables diverse sets of applications to be ported with little effort. As a case study, we present a standardized symmetric-key cipher for lightweight security applications. The detailed system design flow and simulation results with accurate device models are reported validating the approach.

A Blockchain Framework for Insurance Processes

Abstract: We design a distributed platform with blockchain as a system service for supporting transaction execution in insurance processes. The insurance industry is heavily dependent on multiple processes between transacting parties for initiating, maintaining and closing diverse kind of policies. Transaction processing time, payment settlement time and security protection of the process execution are major concerns. Blockchain technology, originally conceived as an immutable distributed ledger for detecting double spending of cryptocurrencies, is now increasingly used in different FinTech systems to address productivity and security requirements. The application of blockchain in FinTech processing requires a deep understanding of the underlying business processes. It supports automated interactions between the blockchain and existing transaction systems through the notion of smart contracts. In this paper, we focus on the design of an efficient approach for processing insurance related transactions based on a blockchain-enabled platform. An experimental prototype is developed on Hyperledger fabric, an open source permissioned blockchain design framework. We discuss the main design requirements, corresponding design propositions, and encode various insurance processes as smart contracts. Extensive experiments were conducted to analyze performance of our framework and security of the proposed design.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

[서평]「Applied Cryptography」

Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

When is nearest neighbor meaningful

Abstract: We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

In-memory computing with resistive switching devices

Abstract: Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which data can be transferred between the processing unit and the memory unit represents a fundamental limitation of modern computers, known as the memory wall. In-memory computing is an approach that attempts to address this issue by designing systems that compute within the memory, thus eliminating the energy-intensive and time-consuming data movement that plagues current designs. Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital, analogue, and stochastic computing schemes that have been proposed, and explore the microscopic physical mechanisms involved. Finally, we discuss the challenges in-memory computing faces, including the required scaling characteristics, in delivering next-generation computing. This Review Article examines the development of in-memory computing using resistive switching devices.