Faculty Publications
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Publications by NITK Faculty
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Item 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.Item 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.Item 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.Item Estimating and prediction of turn around time for incidents in application service maintenance projects(Academy Publisher, 2008) Basavaraj, M.J.; Shet, K.C.Application Service Maintenance Projects normally deals with Incidents as First Level support function. Incidents in majority directly link with Production Environment, so Turn around Time for Incidents is a significant factor. Many Companies are having Service Level Agreements with Customer for Turn around Time for Incidents. There is a need to focus on Estimating and Predicting Turn around Time for Incidents. Improvement in Turn around Time helps in improving the Service Level Agreements earlier agreed with the Customer. Saved time can be diverted to other Project Activities like Enhancements or for new requests. This will also helps as one of the paths for Companies to get new business with the Customer. We have used Capability Maturity Model Integration(CMMI)V1.2 Quantitative Project Management(QPM) methodology for Application Service Maintenance(ASM) Projects for estimating and predicting turn around time for incidents. By implementing this best practice in SEI CMMI Level 5 Company we have achieved a significant improvement of approximately 50 percent reduction in Average Turn around Time for incidents. © 2008 Academy Publisher.Item Heuristics based server consolidation with residual resource defragmentation in cloud data centers(Elsevier, 2015) Sunil Rao, K.; Santhi Thilagam, P.Server Consolidation is one of the foremost concerns associated with the effective management of a Cloud Data Center as it has the potential to accomplish significant reduction in the overall cost and energy consumption. Most of the existing works on Server Consolidation have focused only on reducing the number of active physical servers (PMs) using Virtual Machine (VM) Live Migration. But, along with reducing the number of active PMs, if a consolidation approach reduces residual resource fragmentation, the residual resources can be efficiently used for new VM allocations, or VM reallocations, and some future migrations can also be reduced. None of the existing works have explicitly focused on reducing residual resource fragmentation along with reducing the number of active PMs to the best of our knowledge. We propose RFAware Server Consolidation, a heuristics based server consolidation approach which performs residual resource defragmentation along with reducing the number of active PMs in cloud data centers. © 2014 Elsevier B.V. All rights reserved.Item A tree based representation for effective pattern discovery from multimedia documents(Elsevier B.V., 2017) Pushpalatha, K.; Ananthanarayana, A.The growing amount of multimedia documents demanded the efficient knowledge discovery systems. The efficacy of the knowledge discovery systems is influenced by the representation of multimedia documents. The suitable multimedia document representation acts as a platform for multimedia mining tasks. In this paper, a Multimedia Suffix Tree Document model (MSTD) is presented to represent the multimedia documents in a tree based structure. The MSTD model discovers the useful patterns embedded in the multimedia documents and reduces the search time thereby aiding the multimedia mining methods. It provides the complete information of the multimedia documents in one structure. In order to evaluate the proficiency of the proposed MSTD model, the MSTD model based mining methods are proposed. The experiments are conducted with three multimodal multimedia document datasets. The experimental analysis of the proposed methods reveal the significance of MSTD representation for multimedia documents in achieving the significant performance of multimedia mining tasks. © 2016 Elsevier B.V.Item Straddling the crevasse: A review of microservice software architecture foundations and recent advancements(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Joseph, C.T.; Chandrasekaran, K.Microservice architecture style has been gaining wide impetus in the software engineering industry. Researchers and practitioners have adopted the microservices concepts into several application domains such as the internet of things, cloud computing, service computing, and healthcare. Applications developed in alignment with the microservices principles require an underlying platform with management capabilities to coordinate the different microservice units and ensure that the application functionalities are delivered to the user. A multitude of approaches has been proposed for the various tasks in microservices-based systems. However, since the field is relatively young, there is a need to organize the different research works. In this study, we present a comprehensive review of the research approaches directed toward microservice architectures and propose a multilevel taxonomy to categorize the existing research. The study also discusses the different distributed computing paradigms employing microservices and identifies the open research challenges in the domain. © 2019 John Wiley & Sons, Ltd.Item CoCoA++: Delay gradient based congestion control for Internet of Things(Elsevier B.V., 2019) Rathod, V.; Jeppu, N.; Sastry, S.; Singala, S.; Tahiliani, M.P.In this paper, we propose a new congestion control algorithm called CoCoA++ to address the issue of network congestion in Internet of Things (IoT). Unlike the existing congestion control mechanisms that operate on instantaneous Round Trip Time (RTT) measurements in IoT, we use delay gradients to get a better measure of network congestion, and implement a probabilistic backoff to deal with congestion. We integrate the delay gradients and the probability backoff factor with Constrained Application Protocol (CoAP). The proposed algorithm is implemented and evaluated using the Cooja network simulator provided by Contiki OS. Subsequently, it is deployed and evaluated in a real testbed by using the FIT/IoT-LAB. We observe that delay gradients give a more accurate measure of congestion and the Retransmission Time Out (RTO) is reduced significantly, thereby leading to less delays and high packet sending rates. CoCoA++ being a minor improvement over the existing algorithm is easy to deploy. © 2019 Elsevier B.V.Item Dialect Identification using Chroma-Spectral Shape Features with Ensemble Technique(Academic Press, 2021) Chittaragi, N.B.; Koolagudi, S.G.The present work proposes a text-independent dialect identification system. Generally, dialects of a language exhibit varying pronunciation styles followed in a specific geographical region. In this paper, chroma features familiar with music-related systems are employed for identification of dialects. In addition, eight significant spectral shape related features from short term spectra are computed and combined along with chroma features and named as chroma-spectral shape features. Chroma features try to aggregate spectral information and attempt to encapsulate the evidential variations, concerning timbre, correlated melody, rhythmic, and intonation patterns found prominently among dialects of few languages. The effectiveness of the proposed features and approach is evaluated on five prominent Kannada dialects spoken in Karnataka, India and internationally known standard Intonation Variation in English (IViE) dataset with nine British English dialects. Discriminative models such as, single classifier based Support Vector Machine (SVM) and ensemble based support vector machines (ESVM) are employed for classification. The proposed features have shown better performance over state-of-the-art i-vector features on both datasets. The highest recognition performance of 95.6% and 97.52% are achieved in the cases of Kannada and IViE dialect datasets respectively using ESVM. Proposed features have also demonstrated robust performance with small sized (limited data) audio clips even in noisy conditions. © 2021 Elsevier LtdItem End-to-end latent fingerprint enhancement using multi-scale Generative Adversarial Network(Elsevier B.V., 2024) Pramukha, R.N.; Akhila, P.; Koolagudi, S.G.Latent fingerprint enhancement is paramount as it dramatically influences matching accuracy. This process is often challenging due to varying structured noise and background patterns. The prints may be of arbitrary sizes and scales with a high degree of occlusion. There is a need for creating an end-to-end system that handles different conditions reliably to streamline this often lengthy and tricky process. In this work, we propose a Generative Adversarial Network (GAN) based architecture that effectively captures multi-scale context using Atrous Spatial Pyramid Pooling (ASPP). We have trained the network on a synthetically generated dataset, carefully designed to represent real-world latent prints. By avoiding the reconstruction of spurious ridges and only enhancing valid ridges, we avoid the generation of false minutiae, leading to better matching performance. We obtained state-of-the-art results in Sensor to Latent matching using the IIITD MOLF and Latent to Latent Matching using IIITD Latent datasets. © 2024 Elsevier B.V.