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2024 International Workshop on Petri Nets and Software engineering, PNSE 2024

作者： Rajput, Akshit Shivhare, Karnika Joshi, Rushikesh K. Department of Computer Science Engineering Indian Institute of Technology Bombay Mumbai India

This paper introduces a browser-based tool for modeling applications with Process Line Diagrams (PLDs)[1]. The tool aims at providing a modular and logical approach for building process architecture models to bridge the gap between high-level process notations, low-level formal process models and implementation programming languages. While high-level process models primarily rely on manual practices for architectural detailing and trace conformance checking, the paper presents a user-assisted trace simulator to visually inspect the behavior of a process. The interface enables the user to obtain pen-paper snapshots from the process through dry run in the visual simulator. The visual modeler implements the syntactic rules of the PLD primitives, including roles, events, choices, and event synchronization. The same are enforced by the simulator for active prototyping through visual traces. © 2024 Copyright for this paper by its authors.

关键词： High level languages

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VULDetect: A Lightweight Ensemble Based Framework for Automated Software System Vulnerability Detection

VULDetect: A Lightweight Ensemble Based Framework for Automa...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Ali, Irshad Subba, Basant Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India

ISBN: (纸本)9798350350821

This paper presents VULDetect: a lightweight and highly accurate ensemble based software system vulnerability detection framework trained on a large C/C++ file corpus comprising thousands of vulnerable and non-vulnerable source code snippets. VULDetect uses a deep learning based ensemble model comprising multiple classifiers to perform a comprehensive vulnerability assessment of the software programs. Experimental results on a proprietary dataset and a benchmark DIVERSEVUL dataset show that VULDetect is capable of detecting wide range of software system vulnerabilities with high accuracy and low false positive rate. In addition, VULDetect is also highly compact and lightweight in comparison to other Large Language Model (LLM) based frameworks proposed in the literature, which makes it highly suitable for deployment on computation constrained edge devices. © 2024 IEEE.

关键词： C++ (programming language)

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Investigation of kinetics, reaction mechanisms, thermodynamics, and synergetic effects in co-pyrolysis of wood sawdust and linear low-density polyethylene using the thermogravimetric approach

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Environmental science and Pollution Research 2024年第44期31卷 56091-56113页

作者： Dhibar, Nayan Chand Busigari, Rajasekhar Reddy Patel, Madhumita Department of Environmental Science and Engineering Indian Institute of Technology [Indian School of Mines Jharkhand Dhanbad826004 India Department of Fuel Mineral and Metallurgical Engineering Indian Institute of Technology [Indian School of Mines Jharkhand Dhanbad826004 India

This study focused on investigating thermal degradation behaviors, kinetics, reaction mechanisms, synergistic effects, and thermodynamic parameters of wood sawdust (WSD), linear low-density polyethylene (LLDPE), and their blends (LW1:3, LW1:1, and LW3:1) during co-pyrolysis in a thermogravimetric analyzer (TGA). Thermal behavior exhibited a LW1:3 blend (25 wt.% LLDPE) showing significant mass loss at lower temperatures (150 to 300 °C) compared to the individual feedstocks, such as 150 to 400 °C and 300 to 520 °C for WSD and LLDPE, respectively. The iso-conversional methods (KAS, FWO, and FM) were used to determine the kinetic parameters (Ea and A), and the activation energy drop was highest for the LW1:3 blend. According to the master plots, the third-order reaction (O3), nucleation (P2/3), and diffusional model (D4) were the predominant reaction mechanisms for the co-pyrolysis of the LW1:3, LW1:1, and LW3:1 blend, respectively. The thermodynamic parameters demonstrate that a small amount of plastic addition into WSD can improve the reactivity of the blend, shorten the reaction time, and cause less energy-intensive reactions. The values of ΔH, ΔG, and ΔS also confirmed the co-pyrolysis process’s spontaneity and endothermic nature. The Fourier transforms infrared spectrometer (FTIR) spectra of raw feedstock, blends, and their biochar revealed some of the peaks were shifted, the intensity was reduced, and disappearance can happen when the temperature was increased. Using the experimental and theoretical/predicted activation energies, the parity chart illustrates the synergistic effects of co-pyrolysis of different blends, and the LW1:3 blend has a favorable synergistic impact. These results could be helpful in process optimization and designing an effective reactor system for co-pyrolysis. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Activation energy

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Energy-Efficient and Mobility-Aware Device Selection for Task Offloading in Blockchain-Enabled IoT

Energy-Efficient and Mobility-Aware Device Selection for Tas...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Sanatan, Kumar Ajay, Shubham Kant Kumar, Satendra Indian Institute of Technology Department of Computer Science and Engineering Patna801106 India

ISBN: (纸本)9798350376715

Blockchain is a decentralized ledger system that securely records transactions across multiple nodes. A key challenge in blockchain networks is forking, where the transaction history diverges due to protocol changes, leading to distinct chains. In Blockchain-enabled IoT networks, forking disrupts consistency and increases latency and energy consumption. To address this issue, we propose a mobility-aware federated learning framework that optimizes the selection of IoT devices and miners to minimize forking events. Specifically, we first employed Markov Predictor to predict the future location of the devices. Further, we select the subset of devices based on their predicted locations and eliminate the devices likely to be involved in the forking event. Finally, the task of eliminating devices is reassigned to underutilized devices. Experimental results show that our Markov-based predictor achieves a 63% prediction accuracy for four to 25 devices. © 2024 IEEE.

关键词： Miners

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Using Recursive Feature Elimination Feature Selection based Machine Learning Classifier for Attack Classification on UNSW-NB 15 dataset 9

Using Recursive Feature Elimination Feature Selection based ...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Albasheer Mohamed, Fawzia Omer Agarwal, Mayank Indian Institute of Technology Patna Department of Computer Science And Engineering Patna India

ISBN: (纸本)9798350394474

In modern computer networks, intrusion detection systems (IDS) play a crucial role by detecting and notifying administrators about malicious activities. Ensuring network security and identifying unauthorized access to network resources is paramount in constructing an effective IDS. Traditionally, intrusion detection research relied heavily on the KDDCUP99 dataset, but its limitations in evaluating network intrusion detection systems (NIDS) compared to the UNSW-NB15 dataset have become apparent. This article introduces a novel approach that incorporates feature selection techniques to enhance the performance of three classification methods: K-Nearest Neighbors (KNN), Decision Trees (DT), and Logistic Regression (LR). By utilizing the recursive feature elimination (RFE) technique, irrelevant features are eliminated, allowing the model to focus on the most discriminative attributes for intrusion detection. This not only improves classification accuracy but also reduces computational complexity. The study compares the performance of KNN, DT, and LR algorithms using various metrics such as accuracy, precision, recall, and F1-score. The findings provide a comprehensive analysis of each algorithm's strengths and weaknesses, offering valuable guidance to practitioners and researchers when selecting appropriate algorithms and feature selection techniques for real-world intrusion detection in network environments. © 2024 IEEE.

关键词： Decision trees

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Automated Diabetic Retinopathy detection and classification using stochastic coordinate descent deep learning architectures

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Materials Today: Proceedings 2023年 80卷 3333-3345页

作者： Rosline Mary, A. Kavitha, P. Department of Computer Science and Engineering Vemana Institute of Technology Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India

A new stochastic coordinate descent deep learning architectures optimization is proposed for Automated Diabetic Retinopathy Detection and Classification from different data sets and convolution networks. Initially, the layer-by-layer comparison of convolution matrix, pooling, transition, and dense of each network along with their matrix order is examined. Loss minimization after the testing data from the prediction analysis is considered as objective function for every stage of convolution networks. Similarity among the networks is identified for optimizing the matrix order of each layer and the minimization is performed for classifying the Diabetic Retinopathy level. The performance of the proposed optimization architectures confirmed through confusion matrix for every data set taking training accuracy and data loss as measures. The result of the proposed optimized schemes shows that for different the datasets different in amount data displays the dominance than existing schemes. The numerical analysis of different training accuracy and data loss is presented for validating the proposed work. © 2021

关键词： Convolution

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TRIESTE: translation based defense for text classifiers

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16385-16396页

作者： Gupta, Anup Kumar Paliwal, Vardhan Rastogi, Aryan Gupta, Puneet Department of Computer Science and Engineering Indian Institute of Technology Indore Madhya Pradesh Indore452020 India Department of Electrical Engineering Indian Institute of Technology Indore Madhya Pradesh Indore452020 India

The field of natural language processing (NLP) has significantly evolved with the advent of state-of-the-art models. The discovery of these models has entirely revolutionised how NLP tasks such as machine translation, sentiment analysis and many others are performed. However, despite their high efficacy and meticulous performance, these models are prone to adversarial attacks. Adversarial attacks involve the introduction of perturbations imperceptible to humans, which can severely impact the model’s learning and prediction accuracy. Current defenses on text data include approaches such as spell-checking and adversarial training, which have their limitations against state-of-the-art adversarial attacks. This paper put forward an effective transformation-based defense, TRIESTE (TRanslatIon basEd defenSe for Text classifiErs). The proposed defense overcomes the shortcomings of existing defenses by translating the input text from the source language to a target language and again back to the source language before providing it to the text classifier. Translation ensures that the sentiment of the translated text is similar to that of the input text by taking the entire text into consideration, which leads to the removal of adversarial perturbations. Rigorous evaluation on publicly available datasets showcases that TRIESTE is successful against state-of-the-art attacks without a significant drop in the classifier accuracy. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Practical Defense Against Adversarial WiFi Sensing 18

Practical Defense Against Adversarial WiFi Sensing

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Shankar, Yamini Chakraborty, Ayon Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391725

WiFi-based sensing, a non-intrusive technology that leverages existing communication infrastructure, has become widely used for environmental monitoring by extracting Channel State Information (CSI). However, the vulnerability of these systems to adversarial attacks, a core challenge within Integrated Sensing and Communication (ISAC), highlights the need for practical and robust defenses. In this work, we introduce a practical black-box defense strategy designed to protect CSI data from adversarial manipulation, significantly reducing an attacker's classification accuracy from 98% to 17% while preserving communication quality. Our approach achieves a minimal median Signal-to-Noise Ratio (SNR) difference of 1 dB, ensuring stable throughput and reliable system performance. This defense represents a crucial step forward in securing WiFi-based sensing systems, offering a resilient, low-impact solution that safeguards both sensing integrity and communication efficacy. © 2024 IEEE.

关键词： Wi-Fi

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PUF-based Lightweight Mutual Authentication Scheme for IoT-based Smart Grids 18

PUF-based Lightweight Mutual Authentication Scheme for IoT-b...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Khatua, Manas Das, Sreeparna Rana, Pratik Manoj Indian Institute of Technology Guwahati Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

Smart grids have paved the way for the amalgamation of electricity and the Internet of Things (IoT). IoT enables communication of important data, such as electricity consumption, between the smart meter and the server. At the same time, it makes the system vulnerable to cyber-attacks as the IoT devices are resource-constrained in nature and not equipped with adequate in-built security. Thus, secure and lightweight authentication schemes are of utmost need. Existing literature on lightweight authentication schemes are either computationally heavy specifically during authentication operations or need high communication and storage requirements. This paper mainly focuses on authentication phase as it is executed many times in the lifespan of an IoT device compared to the device registration phase. To address the above issues, this paper proposes a lightweight mutual authentication scheme that can protect the system from various attacks such as man-in-the-middle (MITM) and replay attacks. The proposed scheme uses physically unclonable function (PUF) primitives of IoT devices to alleviate the long-term storage requirement in the devices and further makes the scheme secure by achieving operational asymmetry. Unlike many existing schemes, our proposed scheme performs the device enrolment phase through the insecure channel as in many real-world applications. We have verified the security of the proposed scheme through formal and informal analysis. Further, we have done a test-bed implementation and analysis using off-the-shelf available hardware to represent IoT devices. In all the analysis, it is observed that the proposed scheme is lightweight and secure compared to the state-of-the-art schemes. © 2024 IEEE.

关键词： Smart power grids

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QoS-Aware Flow Routing with Minimizing Active Links and Rule Capacity Constraints in SDN Networks 22

QoS-Aware Flow Routing with Minimizing Active Links and Rule...

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Kamboj, Priyanka Pal, Sujata Department of Computer Science and Engineering Indian Institute of Technology Punjab Ropar India

ISBN: (纸本)9798331510183

The growing demand for digitization necessitates modifications to current Internet technologies to effectively support Internet of Things (IoT) applications and services. Software-defined networking (SDN) supports network abstraction and dynamic management through a central controller. The ternary content addressable memory (TCAM) memory used in the OpenFlow switches is very costly and limits the flow table size. The limited size of TCAM memory motivates us to think about the flow rule replacement in SDN switches. This paper introduces a routing approach to meet traffic flow requirements in the SDN networks, consisting of two main phases – routing path selection and flow rule replacement. In the first phase, we formulate a minimum cost routing problem to minimize the network’s active links as an integer linear program (ILP) in SDN networks. We then introduce a greedy heuristic-based solution to solve the ILP in polynomial time. In the second phase, we propose a flow rule replacement method that utilizes idle timeout settings to prevent overflow in the flow tables of SDN switches. The simulation results indicate that our proposed approach reduces active links by 15%, 24%, 34% in the Goodnet topology, and by 11%, 23%, 33% in the Sprint topology as compared to benchmark schemes ROSA, LARAC, and SPD, respectively. Additionally, we analyze the impact of idle timeout on flow table entries within both our proposed approach and the benchmark schemes. ©2024 IEEE.

关键词： Polynomial approximation

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