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Feedback-based trust module for IoT networks using machine learning

International Journal of Wireless and Mobile Computing 2024年第1期27卷 78-91页

作者： Iqbal, Sania Qureshi, Shaima Department of Computer Science and Engineering National Institute of Technology Jammu & Kashmir Srinagar India

The increasing emergence of IoT in healthcare, industrial automation, manufacturing, infrastructure, business and the home undoubtedly provides more conveniences in different aspects of human life. Any IoT security and trust solution must consider the heterogeneity and mobility of IoT devices, and the features of other devices connected to the network. Traditional security measures face computation, storage and communication challenges in securing IoT, and the increasing reliance on IoT services and applications has exacerbated this issue. It is crucial to identify a security management model to handle these constraints. Trust as a security regime in the IoT has far-reaching implications. This paper proposes a novel trust-based security mechanism by combining machine learning and traditional summations to determine a node’s trustworthiness in an IoT environment in milliseconds. Based on this, a computational trust management model is designed by aggregating direct and feedback-based trust information. The model parameters like the trust breach window and trust re-initiation point are selected based on extensive experimentation to dynamically calculate each node’s trust value to reach the proposed model’s peak performance. The model’s effectiveness is verified through simulation. In 500 iterations, the proposed model achieved 97% accuracy with a run time of 0.37 seconds. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Network security

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An Experimental Study on Random Projection Based Biometric Security

An Experimental Study on Random Projection Based Biometric S...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Shukla, Rishabh Kaur, Harkeerat Indian Institute of Technology Jammu Computer Science and Engineering Jammu India

ISBN: (纸本)9798350324457

The safeguarding of biometric data is a crucial aspect of biometric authentication systems, and biometric template protection serves as a means to ensure the confidentiality of such information. In recent times, the utilization of random projection has surfaced as a viable technique for safeguarding biometric templates. This article delves deeper into the security of random projection-based techniques utilized for safeguarding biometric templates. The demonstration showcases that the security of biometric systems is compromised by non-invertibility attacks, as the protected template can be utilized by an attacker to accurately replicate the initial biometric template. A viable approach for executing non-invertibility attacks on systems, based on random projection is additionally furnished by us. The study conducted an experiment on two publicly accessible biometric datasets to demonstrate the vulnerability of random projection-based biometric template security methods. The proposed assault was utilized to recreate the original templates, highlighting the ease with which this could be accomplished. The results of our study emphasize the necessity for innovative methods in safeguarding biometric templates that are impervious to inversion. © 2023 IEEE.

关键词： Biometrics Cyber Security Privacy Random Projection

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An Integrated Approach to Point Labeling Problem 14

An Integrated Approach to Point Labeling Problem

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Simranjeet Kumar, Varun Bhattacharjee, Subhasis Indian Institute of Technology Jammu Computer Science & Engineering Jammu India

ISBN: (纸本)9798350335095

The point feature labeling problem is a very wellknown problem where a set P = {p1, p2, •••, pn} of point sites are given along with their labels. The objective is to maximize the number of labels for points on the map so that no two placed labels are mutually overlapping. Here, we investigate a special class of the point site labeling problem where the label of a point pi touches the point either at one of its four corners (Corner model) or at the middle of one of its 4 edges (Middle Edge model). We assumed that only the height (but not the length) of the labels of all the points are same. This maximum point site labeling problem is known to be NP-hard. We first provided an O(n√ n) heuristic solution for point labeling in the Middle Edge model. On analysing the benchmarks, we felt the need of applying labeling solution simultaneously in Corner model and Middle Edge model for any problem instance. We thus provided an integrated approach for the point labeling problem in two models together. Experimental result show significant improvement (14%) in the number of points being labeled in our integrated approach compared to the existing Corner model of labeling. Our solution for Middle Edge model always take less time than the Corner model, and it also provides a nice trade-off between the solution quality and the runtime across two models. © 2023 IEEE.

关键词： Integrated control

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Additive manufacturing of cordierite materials by digital light processing

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Ceramics International 2025年第16期51卷 22683-22690页

作者： Tsai, Jung-Ting Gupta, Anant Kumar Muhtadin, Muhtadin Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Department of Materials Engineering Indian Institute of Technology Jammu Jammu and Kashmir 181221 India

Cordierite ceramic material is exceptionally resistant to chemical corrosion, low thermal expansion, and thermal shock, which would be a suitable candidate for thermal applications. Cordierite materials ((Mg, Fe)₂Al₄Si₅O₁₈) were additive manufactured by digital light processing (DLP). The proposed ceramic UV-curable method vastly decreases the traditional fabrication time steps, providing a more complex design with a more efficient and cost-effective solution. The commercial-made ready cordierite UV-curable resin shows a ceramic yield of 72 %, with linear shrinkage of 12.5 % and open porosity of 4.9 % however close porosity reaches as high as 9.07 % which results in a very low mechanical strength. The x-ray tomography shows that before sintering, large air bubbles already existed in the green body, while after the sintering, large porosity with a non-sphericity shape resides in the center with smaller porosity but a more spherical shape distributed in the specimen. © 2024 Elsevier Ltd and Techna Group S.r.l.

关键词： Sintering

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Apple Leaf Disease Detection Using Transfer Learning

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Annals of Data science 2025年第1期12卷 213-222页

作者： Wani, Ozair Ahmad Zahoor, Umer Shah, Syed Zubair Ahmad Khan, Rijwan Department of Computer Science & Engineering Islamic University of Science and Technology Jammu & Kashmir Awantipora India Department of Computer Science and Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India

Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant health, reduced production severely impacts the country’s economy. Traditional disease identification methods, relying on human experts, are slow, time-consuming, and impractical for large farms. Our proposed model utilizes a combination of pre-trained Resnet18, Alexnet, GoogLeNet, and VGG16 networks to classify apple tree leaves into categories such as healthy, black rot, apple cedar rust, and apple scab based on images. Various image enhancement techniques were employed to enhance the model’s accuracy. Ultimately, our model achieved an accuracy of 97.25% on the validation dataset, demonstrating excellent performance across various metrics. This suggests its potential for efficient and accurate plant health monitoring in the agricultural sector. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Tracking PII ex-filtration: Exploring decision tree and neural network with explainable AI 17

Tracking PII ex-filtration: Exploring decision tree and neur...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Kohli, Rishika Chatterjee, Shreyas Gupta, Shaifu Singh Gaur, Manoj Indian Institute of Technology Jammu Computer Science and Engineering India

ISBN: (纸本)9798350307672

The swift evolution of digital technology has led to the creation of devices that have substantially simplified people's lives. Consequently, individuals are spending more and more time engaging with the internet. The impact of this has been positive as well as negative for users. Users' interaction with smart-phone applications generates a large volume of sensitive personal data, which is stored in the device itself. The problem arises when data is ex-filtrated from the device without the user's knowledge. Consequently, there is a dire need to detect any leak of user information from devices. In this view, we propose variations in machine learning frameworks that improve the state-of-the-art framework's ability to detect users' personal information disclosure with greater accuracy and precision. In addition, our framework integrates the XAI algorithm SHAP to enhance the interpretability and comprehension of the black-box approach when detecting sensitive data reliably. Our experimental findings validate that XAI offers improved explanations and responsiveness, making it a more user-friendly and transparent solution. © 2023 IEEE.

关键词： Sensitive data

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Dominance Maximization in Uncertain Graphs 9th

Dominance Maximization in Uncertain Graphs

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Tekawade, Atharva Banerjee, Suman Department of Computer Science and Engineering Indian Institute of Technology Jammu Jammu181221 India

ISBN: (纸本)9783031466700

An uncertain graph is defined as a collection of nodes with their interconnected edges, where each edge is assigned with its existence probability value. Motivated by many real-world scenarios, in this paper, we define the notion of dominance for a given subset of nodes as the sum of its cardinality and the total probability with which the remaining nodes in the graph are dominated by the subset. For a given uncertain graph G(V, E, P), and a positive integer k, we introduce the Problem of Dominance Maximization where the goal is to choose a k-element subset to maximize the dominance in the uncertain graph. First, we show that the dominance function is non-negative, monotone, and submodular. Subsequently, we show that the problem of maximizing dominance in an uncertain graph is NP-hard. Then we propose an incremental greedy strategy based on the marginal gain in dominance computation that leads to (1-1e) -factor approximate solution. With a slight change, we show that this strategy can be made efficient without losing the approximation ratio. Finally, we exploit the submodularity property of the dominance function and construct a combinatorial object called ‘Pruned Submodularity Graph’ and use this effectively to solve the dominance maximization problem. All the methodologies have been analyzed to understand their time and space requirements. We implement the proposed solution approaches with real-world network datasets and conduct many experiments. We observe that for larger datasets the Pruned Submodularity Graph-based approach takes reasonable computational time and produces better solutions in terms of dominance value compared to the baseline methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Graphic methods

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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An integrated approach for prediction of magnitude using deep learning techniques

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Neural Computing and Applications 2024年第27期36卷 16991-17006页

作者： Joshi, Anushka Raman, Balasubramanian Mohan, C. Krishna Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Telangana Sangareddy502285 India

Timely estimation of earthquake magnitude plays a crucial role in the early warning systems for earthquakes. Despite the inherent danger associated with earthquake energy, earthquake research necessitates extensive parameter estimation and predictive techniques to account for uncertain trends in earthquake waveforms when determining earthquake magnitudes using a single station. This study introduces an effective solution to tackle the issue through the automatic magnitude deep network (AMagDN) model. The proposed model includes long short-term memory (LSTM), a bidirectional LSTM, an autocorrelation attention mechanism, and a machine learning block that can capture detailed information from the seismic waveform recorded during an earthquake. The unique feature of this model is the use of multivariate time series waveforms derived from recorded accelerograms specifically tailored to their energy significance with magnitude and seven fusion tabular parameters involving source and geospatial features. The proposed model’s training, validation and testing are done using independent 15014, 1287 and 3448 records maintained by the Kyoshin network, Japan, for moderate to great impact earthquakes between 5.5 and 8.0 (MJMA). A comparative study shows that the proposed model outperforms recent state-of-the-art models and common linear relations, reducing mean absolute prediction error by 40% from the second-best model. The multi-stations data are also used for successfully forecasting the magnitudes of two significant earthquakes of 7.7 and 7.3 magnitude (MJMA) using the proposed model. The reliable prediction capabilities of the proposed model for both single and multi-station data clearly demonstrate its utility in reducing earthquake hazards. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Long short-term memory

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Influence Maximization with Tag Revisited: Exploiting the Bi-submodularity of the Tag-Based Influence Function 9th

Influence Maximization with Tag Revisited: Exploiting the ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Tekawade, Atharva Banerjee, Suman Department of Computer Science and Engineering Indian Institute of Technology Jammu Jammu181221 India

ISBN: (纸本)9783031466601

Given a Social Network how to select a small number of influential users to maximize the influence in the network has been studied extensively in the past two decades and formally referred to as the Influence Maximization Problem. Among most of the existing studies, it has been implicitly assumed that there exists a single probability value that represents the influence probability between the users. However, in reality, the influence probability between any two users is dependent on the context (formally referred to as tag e.g.;a sportsman can influence his friends related to any news related to sports with high probability). In this paper, we bridge the gap by studying the Tag-Based Influence Maximization Problem. In this problem, we are given with a social network where each edge is marked with one probability value for every tag and the goal here is to select k influential users and r influential tags to maximize the influence in the network. First, we define a tag-based influence function and show that this function is bi-submodular. We use the orthent-wise maximization procedure of bi-submodular function which gives a constant factor approximation guarantee. Subsequently, we propose a number of efficient pruning techniques that reduces the computational time significantly. We perform an extensive number of experiments with real-world datasets to show the effectiveness and efficiency of the proposed solution approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Method of moments

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