Conference Papers

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    ARTINALI: Dynamic invariant detection for Cyber-Physical System security
    (Association for Computing Machinery acmhelp@acm.org, 2017) Aliabadi, M.R.; Kamath, A.A.; Gascon-Samson, J.; Pattabiraman, K.
    Cyber-Physical Systems (CPSes) are being widely deployed in security- critical scenarios such as smart homes and medical devices. Unfortunately, the connectedness of these systems and their relative lack of security measures makes them ripe targets for attacks. Specification-based Intrusion Detection Systems (IDS) have been shown to be effective for securing CPSs. Unfortunately, deriving invariants for capturing the specifications of CPS systems is a tedious and error-prone process. Therefore, it is important to dynamically monitor the CPS system to learn its common behaviors and formulate invariants for detecting security attacks. Existing techniques for invariant mining only incorporate data and events, but not time. However, time is central to most CPS systems, and hence incorporating time in addition to data and events, is essential for achieving low false positives and false negatives. This paper proposes ARTINALI, which mines dynamic system properties by incorporating time as a first-class property of the system. We build ARTINALI-based Intrusion Detection Systems (IDSes) for two CPSes, namely smart meters and smart medical devices, and measure their efficacy. We find that the ARTINALIbased IDSes significantly reduce the ratio of false positives and false negatives by 16 to 48% (average 30.75%) and 89 to 95% (average 93.4%) respectively over other dynamic invariant detection tools. © 2017 Association for Computing Machinery.
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    Teaching EARS to undergrads in the pandemic - Industry academia experience
    (Institute of Electrical and Electronics Engineers Inc., 2020) Nair, G.V.; Jeppu, Y.; Tahiliani, M.P.
    The COVID-19 pandemic is rampant in India and this has changed the way the students and teachers interact with each other during a course. An added complexity is the introduction of the Industry Academia participation in terms of Adjunct Faculties. Teaching formal methods to undergraduate students has been difficult and these are well captured in the academic community. The necessity of good requirements writing which can be validated using formal methods is a need of the hour for the industry. Requirements error contribute to 70% of the errors in safety critical projects. A course on Formal Methods is offered at the National Institute of Technology Karnataka, Surathkal as an undergraduate elective. This has 54 students registered and the course is offered online by an adjunct faculty from the industry. The experiences of capturing and writing good requirements using the EARS (Easy Approach to Requirements Syntax) is highlighted in this paper. A survey of before and after the class and an exercise on EARS notations are brought out. The lessons learnt and the efficacy of the teaching is brought out as a three perspective: student, academia and industry. © 2020 IEEE.
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    NeuralDoc-Automating Code Translation Using Machine Learning
    (Springer Science and Business Media Deutschland GmbH, 2022) Sree Harsha, S.; Sohoni, A.C.; Chandrasekaran, K.
    Source code documentation is the process of writing concise, natural language descriptions of how the source code behaves during run time. In this work, we propose a novel approach called NeuralDoc, for automating source code documentation using machine learning techniques. We model automatic code documentation as a language translation task, where the source code serves as the input sequence, which is translated by the machine learning model to natural language sentences depicting the functionality of the program. The machine learning model that we use is the Transformer, which leverages the self-attention and multi-headed attention features to effectively capture long-range dependencies and has been shown to perform well on a range of natural language processing tasks. We integrate the copy attention mechanism and incorporate the use of BERT, which is a pre-training technique into the basic Transformer architecture to create a novel approach for automating code documentation. We build an intuitive interface for users to interact with our models and deploy our system as a web application. We carry out experiments on two datasets consisting of Java and Python source programs and their documentation, to demonstrate the effectiveness of our proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.