Author

# Debdeep Mukhopadhyay

Other affiliations: Indian Institutes of Technology, Indian Institute of Technology Madras

Bio: Debdeep Mukhopadhyay is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Block cipher & Side channel attack. The author has an hindex of 37, co-authored 361 publications receiving 5845 citations. Previous affiliations of Debdeep Mukhopadhyay include Indian Institutes of Technology & Indian Institute of Technology Madras.

##### Papers published on a yearly basis

##### Papers

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01 Jan 2015

TL;DR: This book includes the following chapters: Introduction to Modern Symmetric-Key Ciphers, Mathematics of Cryptography, and Message Integrity and Message Authentication, and Security at the Network Layer: IPSec.

Abstract: This book includes the following chapters : Introduction; Mathematics of Cryptography; Traditional Symmetric-Key Ciphers; Mathematics of Cryptography; Introduction to Modern Symmetric-Key Ciphers; Data Encryption Standard (DES); Advanced Encryption Standard (AES); Encipherment Using Modern Symmetric-Key Ciphers; Mathematics of Cryptography; Asymmetric-Key Cryptography; Message Integrity and Message Authentication; Cryptographic Hash Functions; Digital Signature; Entity Authentication; Key Management; Security at the Application Layer: PGP and S/MIME; Security at the Transport Layer: SSL and TLS; and Security at the Network Layer: IPSec.

854 citations

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TL;DR: This paper attempts 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.

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.

455 citations

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01 Jun 2011TL;DR: In this paper, the AES key can be deduced using a single random byte fault at the input of the eighth round using a two-stage algorithm, with a statistical expectation of reducing the possible key hypotheses to 232 and a mere 28.

Abstract: In this paper we present a differential fault attack that can be applied to the AES using a single fault. We demonstrate that when a single random byte fault is induced at the input of the eighth round, the AES key can be deduced using a two stage algorithm. The first step has a statistical expectation of reducing the possible key hypotheses to 232, and the second step to a mere 28.

274 citations

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TL;DR: A differential fault attack that can be applied to the AES using a single fault, which demonstrates that when a single random byte fault is induced at the input of the eighth round, the AES key can be deduced using a two stage algorithm.

Abstract: In this paper we present a differential fault attack that can be applied to the AES using a single fault. We demonstrate that when a single random byte fault is induced at the input of the eighth round, the AES key can be deduced using a two stage algorithm. The first step has a statistical expectation of reducing the possible key hypotheses to 2, and the second step to a mere 2. Furthermore, we show that, with certain faults, this can be reduced to two key hypothesis.

273 citations

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TL;DR: This paper develops an authentication and key exchange protocol by combining the ideas of Identity based Encryption, PUFs and Key-ed Hash Function to show that this combination can help to do away with the requirement to store the secret challenge-response pair explicitly at the verifier end.

Abstract: Physically Unclonable Functions (PUFs) promise to be a critical hardware primitive to provide unique identities to billions of connected devices in Internet of Things (IoTs). In traditional authentication protocols a user presents a set of credentials with an accompanying proof such as password or digital certificate. However, IoTs need more evolved methods as these classical techniques suffer from the pressing problems of password dependency and inability to bind access requests to the “things” from which they originate. Additionally, the protocols need to be lightweight and heterogeneous. Although PUFs seem promising to develop such mechanism, it puts forward an open problem of how to develop such mechanism without needing to store the secret challenge-response pair (CRP) explicitly at the verifier end. In this paper, we develop an authentication and key exchange protocol by combining the ideas of Identity based Encryption (IBE), PUFs and Key-ed Hash Function to show that this combination can help to do away with this requirement. The security of the protocol is proved formally under the Session Key Security and the Universal Composability Framework. A prototype of the protocol has been implemented to realize a secured video surveillance camera using a combination of an Intel Edison board, with a Digilent Nexys-4 FPGA board consisting of an Artix-7 FPGA, together serving as the IoT node. We show, though the stand-alone video camera can be subjected to man-in-the-middle attack via IP-spoofing using standard network penetration tools, the camera augmented with the proposed protocol resists such attacks and it suits aptly in an IoT infrastructure making the protocol deployable for the industry.

179 citations

##### Cited by

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[...]

TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.

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 …

33,785 citations

Microsoft

^{1}TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

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2,687 citations

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2,618 citations

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Google

^{1}, University of Pennsylvania^{2}, Graz University of Technology^{3}, Cryptography Research^{4}, University of Adelaide^{5}TL;DR: Spectre as mentioned in this paper is a side channel attack that can leak the victim's confidential information via side channel to the adversary. And it can read arbitrary memory from a victim's process.

Abstract: Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try to guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access the victim's memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victim's confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victim's process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, including operating system process separation, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing and side-channel attacks. These attacks represent a serious threat to actual systems since vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. While makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak.

1,317 citations