Visible to the public Biblio

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2023-02-03
Saha, Akashdeep, Chatterjee, Urbi, Mukhopadhyay, Debdeep, Chakraborty, Rajat Subhra.  2022.  DIP Learning on CAS-Lock: Using Distinguishing Input Patterns for Attacking Logic Locking. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). :688–693.
The globalization of the integrated circuit (IC) manufacturing industry has lured the adversary to come up with numerous malicious activities in the IC supply chain. Logic locking has risen to prominence as a proactive defense strategy against such threats. CAS-Lock (proposed in CHES'20), is an advanced logic locking technique that harnesses the concept of single-point function in providing SAT-attack resiliency. It is claimed to be powerful and efficient enough in mitigating existing state-of-the-art attacks against logic locking techniques. Despite the security robustness of CAS-Lock as claimed by the authors, we expose a serious vulnerability and by exploiting the same we devise a novel attack algorithm against CAS-Lock. The proposed attack can not only reveal the correct key but also the exact AND/OR structure of the implemented CAS-Lock design along with all the key gates utilized in both the blocks of CAS-Lock. It simply relies on the externally observable Distinguishing Input Patterns (DIPs) pertaining to a carefully chosen key simulation of the locked design without the requirement of structural analysis of any kind of the locked netlist. Our attack is successful against various AND/OR cascaded-chain configurations of CAS-Lock and reports 100% success rate in recovering the correct key. It has an attack complexity of \$\textbackslashmathcalO(m)\$, where \$m\$ denotes the number of DIPs obtained for an incorrect key simulation.
ISSN: 1558-1101
2020-09-14
Chatterjee, Urbi, Govindan, Vidya, Sadhukhan, Rajat, Mukhopadhyay, Debdeep, Chakraborty, Rajat Subhra, Mahata, Debashis, Prabhu, Mukesh M..  2019.  Building PUF Based Authentication and Key Exchange Protocol for IoT Without Explicit CRPs in Verifier Database. IEEE Transactions on Dependable and Secure Computing. 16:424–437.
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.
2020-09-04
Chatterjee, Urbi, Santikellur, Pranesh, Sadhukhan, Rajat, Govindan, Vidya, Mukhopadhyay, Debdeep, Chakraborty, Rajat Subhra.  2019.  United We Stand: A Threshold Signature Scheme for Identifying Outliers in PLCs. 2019 56th ACM/IEEE Design Automation Conference (DAC). :1—2.

This work proposes a scheme to detect, isolate and mitigate malicious disruption of electro-mechanical processes in legacy PLCs where each PLC works as a finite state machine (FSM) and goes through predefined states depending on the control flow of the programs and input-output mechanism. The scheme generates a group-signature for a particular state combining the signature shares from each of these PLCs using \$(k,\textbackslashtextbackslash l)\$-threshold signature scheme.If some of them are affected by the malicious code, signature can be verified by k out of l uncorrupted PLCs and can be used to detect the corrupted PLCs and the compromised state. We use OpenPLC software to simulate Legacy PLC system on Raspberry Pi and show İ/O\$ pin configuration attack on digital and pulse width modulation (PWM) pins. We describe the protocol using a small prototype of five instances of legacy PLCs simultaneously running on OpenPLC software. We show that when our proposed protocol is deployed, the aforementioned attacks get successfully detected and the controller takes corrective measures. This work has been developed as a part of the problem statement given in the Cyber Security Awareness Week-2017 competition.

2020-04-24
Balijabudda, Venkata Sreekanth, Thapar, Dhruv, Santikellur, Pranesh, Chakraborty, Rajat Subhra, Chakrabarti, Indrajit.  2019.  Design of a Chaotic Oscillator based Model Building Attack Resistant Arbiter PUF. 2019 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). :1—6.

Physical Unclonable Functions (PUFs) are vulnerable to various modelling attacks. The chaotic behaviour of oscillating systems can be leveraged to improve their security against these attacks. We have integrated an Arbiter PUF implemented on a FPGA with Chua's oscillator circuit to obtain robust final responses. These responses are tested against conventional Machine Learning and Deep Learning attacks for verifying security of the design. It has been found that such a design is robust with prediction accuracy of nearly 50%. Moreover, the quality of the PUF architecture is evaluated for uniformity and uniqueness metrics and Monte Carlo analysis at varying temperatures is performed for determining reliability.