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A multi-level DDoS defense mechanism in container based cloud environment

Cluster Computing 2025年第5期28卷 1-19页

作者： Kumar, Anmol Agarwal, Mayank Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta Bihar Patna801103 India

DDoS attacks target the victim’s service availability. In this paper, we propose a multi-level DDoS defense mechanism that combines two approaches: bandwidth limitation and resource isolation. We consider requests coming from the same source IP that exceed a threshold value as suspicious and treat other requests as genuine. To achieve this goal, we serve suspicious and legitimate user requests in separate containers. We also apply the bandwidth limitation to incoming traffic from containers that serve suspicious requests, ensuring that the victim container does not create a resource contention problem. We chose three different strategies to test our proposed mechanisms: the 50-25-75 strategy, the 100-50-150 strategy, and the 150-75-225 strategy. The experimental results demonstrate that the proposed approach, when subjected to DDoS attacks, reduces the failure rate of genuine requests and maintains response times that are comparable to those under normal network conditions. When there is a DDoS attack, the proposed defense system can make the target service available 87% of the time with the 50-25-75 strategy, 70% of the time with the 100-50-150 strategy, and 85% of the time with the 150-75-225 strategy, in contrast to a single-level DDoS defense system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Bandwidth

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Harnessing Machine Learning in Dynamic Thermal Management in Embedded CPU-GPU Platforms

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ACM Transactions on Design Automation of Electronic Systems 2025年第2期30卷 1-32页

作者： Maity, Srijeeta Majumder, Anirban Roy, Rudrajyoti Hota, Ashish Dey, Soumyajit Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India Electronics And Communication Engineering Indian Institute of Technology Kharagpur Kharagpur India

With increasing transistor density, modern heterogeneous embedded processors often exhibit high temperature gradients due to complex application scheduling scenarios which may have missed design considerations. In many use cases, off-chip "active"cooling solutions are considered prohibitive in such reduced form factors. Core frequency throttling by existing dynamic thermal management techniques often compromises the Quality-of-Service (QoS) and violates real-time deadlines. This necessitates the adoption of intelligent resource management that simultaneously manages both thermal and latency performance. Coupled with the complexity of modern heterogeneous multi-cores, the periodic application updates that cater to ever-changing user requirements often render model-driven thermal-aware resource allocation approaches unsuitable for heterogeneous multi-core systems. For such application-architecture scenarios, we propose a novel self-learning based resource manager using Reinforcement Learning that intelligently manipulates core frequencies and task set mappings to fulfill thermal and latency objectives. Our framework employs a data-driven system modeling technique using Gaussian Process Regression to enable efficient offline training of this learning-based resource manager to avoid challenges associated with initial online training. We evaluate the approach on a heterogeneous embedded CPU-GPU platform with real workloads and observe a significant reduction in peak operating temperature when compared to the default onboard frequency governor as well as other learning-based state-of-the-art approaches. © 2025 Copyright held by the owner/author(s).

关键词： Quality of service

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Unlocking Eye Gestures with Earable Inertial Sensing for Accessible HCI 17

Unlocking Eye Gestures with Earable Inertial Sensing for Acc...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chugh, Garvit Chakraborty, Suchetana Chakraborty, Sandip Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Eye gestures are widely used in many applications, including device control, biometrics, visual analytics, and health-care, like Alzheimer's, accessibility, etc. The conventional method for eye gesture detection needs visual information processing devices or smart glasses to capture the movement patterns of the eyeballs. In this paper, we present a novel framework that utilizes inertial measurement unit (IMU) sensors embedded in earable devices for real-time eye gesture detection, which can work as a complementary to visual information processing devices. The framework translates specific eye movements into actionable commands, enabling hands-free interaction for users with limited mobility. Through extensive experimentation with 14 participants across diverse activities such as sitting, walking, running, and driving, the framework demonstrated high accuracy in eye gesture recognition, achieving a macro F1 score of 0.85 on our self-collected dataset. © 2025 IEEE.

关键词： Eye movements

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8 17

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. This system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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A Stratified Seed Selection Algorithm for K-Means Clustering on Big Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1334-1344页

作者： Bajpai, Namita Paik, Jiaul H. Sarkar, Sudeshna Indian Institute of Technology Department of Artificial Intelligence Kharagpur721302 India Indian Institute of Technology Department of Computer Science Kharagpur721302 India

In k-means clustering, the selection of initial seeds significantly influences the quality of the resulting clusters. Moreover, clustering large-sized data introduces an additional challenge for seed selection. We propose a novel and scalable seed selection approach by jointly modeling the quality and diversity of the potential seeds through a principled probabilistic stochastic point process. To this end, we also propose a novel seed quality estimation approach on large data. Our approach quantifies the quality of a seed by measuring the divergence between the distribution of similarity between the closest neighbors and that of the randomly chosen neighbors from exhaustive stratified batches of samples. Unlike many existing scalable approaches, we do not rely on a small sample of the original data;instead, we use the entire data, thereby minimizing the chance of leaving out information about a potentially high-quality seed. The extensive evaluation on a set of benchmark data shows that it outperforms a number of strong, well-known, and recent algorithms measured by three standard metrics. © 2020 IEEE.

关键词： k-nearest neighbors

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ProxaDyn: A Proximity-Aware Dynamic Caching Approach for Named Data Networks

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IEEE Transactions on Network science and engineering 2025年第3期12卷 2360-2372页

作者： Kumari, Matta Krishna Tripathi, Nikhil Joshi, Piyush Indian Institute of Information Technology Computer Science and Engineering Group Sri City517 646 India Department of Computer Science and Engineering Dhanbad826 004 India

Named Data Network (NDN), a future Internet architecture is introduced to address the shortcomings of the current Internet architecture. NDN supports in-network caching to facilitate scalable content distribution and enhance overall network performance. However, the known NDN caching strategies suffer from a few common drawbacks, such as inefficient cache utilization, high content redundancy, and overhead due to lookup repetition. To address these issues, in this paper, we propose a novel caching strategy called ProxaDyn for efficient content lookup, placement, and replacement. During the content lookup phase, ProxaDyn interacts exclusively with the router responsible for caching a particular content. This eliminates interaction with other intermediate routers, thereby significantly reducing content access latency. For content placement, ProxaDyn strategically selects an on-path router based on content popularity. Popular content is placed in the cache of a router closer to the consumer, while less popular content is cached in a router away from the consumer. This approach significantly improves the cache hits and reduces the access latency. We test ProxaDyn over a diverse range of real-world network topologies. Using extensive experiments, we show that ProxaDyn could achieve significantly better results compared to the state-of-the-art NDN caching strategies. © 2013 IEEE.

关键词： Data communication systems

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Deformation Behavior of Commercially Pure Titanium Subjected to Blast Assisted Deformation: New Insights on f112̅1g Extension Twinning

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Materials Transactions 2025年第5期66卷 521-531页

作者： Bisht, Anuj Subburaj, Janardhanraj Jagadeesh, Gopalan Suwas, Satyam Department of Mechanical and Industrial Engineering Indian Institute of Technology Roorkee Roorkee India Department of Aerospace Engineering Indian Institute of Science Bangalore India Department of Materials Engineering Indian Institute of Science Bangalore India

In the present work, the microstructure and texture evolution in commercially pure titanium subjected to blast assisted deformation has been investigated by means of electron backscattered diffraction and transmission electron microscopy. The evolved texture in the deformed material is primarily attributed to the dominant h1¯010i 64.4° contraction twinning. Other deformation twins were also observed in a low fraction. A uniform dislocation background with other microstructural features observed suggested the formation of a superimposed microstructure in the material. f112¯1g extension twins (ET2) with a thick uniform pile-up of dislocation along its boundary have formed in the material. Parts of ET2 have detwinned leaving gaps along the twin length, revealing the unstable nature of ET2 twins. The in-depth microstructural analysis reveals the ET2 twin formation mechanism, where the instability developed in the material due to progressive lattice rotation via accumulative slip is relieved by ET2 twin formation by atomic shuffling. The gaps (local detwinning) observed in the present case is attributed to the transient oscillatory response of the material under impulsive loading, which has been previously reported in the literature. ©2025 The Japan institute of Metals and Materials.

关键词： High resolution transmission electron microscopy

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Parkinson’s disease Severity Detection Based on Fused features with tuned Residual Deep Convolutional Neural Network

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Multimedia Tools and Applications 2025年第14期84卷 13111-13131页

作者： Vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh Computer Science & Engineering Indian Institute of Information Technology MP Bhopal India Computer Science & Engineering Maulana Azad National Institute of Technology MP Bhopal India Department of Computer Applications Maulana Azad National Institute of Technology MP Bhopal India

Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. During the diagnosis process, clinicians must consider gait abnormalities as one of the key symptoms. However, relying solely on clinical judgment for the diagnosis of PD may have limitations. In this work, we have proposed a Multikernel SVM (MK-SVM) based Parkinson's severity analysis method. Initially, an adaptive median filter is used to reduce the noise in the gait image. Then, to extract the features, we propose a dual-layer method that employs stacked autoencoders (SAEs) for local feature extraction and residual deep convolution neural networks (RDCNN) for global feature extraction. The hyper-parameters from the RDCNN are optimized using the Enhanced walrus optimization algorithm (EWO). In the final step, we use MK-SVM to classify PD according to severity. This model is evaluated using the GAIT-IT and GAIT-IST datasets. With the help of Python, we evaluated the proposed model in terms of precision, accuracy, recall, specificity, and F-scores, and the model achieved a maximum accuracy of 97.3%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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QoS-Aware Application Assignment and Resource Utilisation Maximization Using AHP in Edge Computing

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IEEE Internet of Things Journal 2025年第11期12卷 17717-17728页

作者： Koganti, Yasasvitha Sridhar, Vidhyuth Yadav, Ram Narayan Pratap, Ajay Iitram Department of Electrical and Computer Science Engineering Ahmedabad380026 India Indiana University Department of Computer Science and Engineering Bloomington United States Banaras Hindu University Indian Institute of Technology Department of Computer Science and Engineering Varanasi India

Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be done in the proximity of IoT users. Thus, the required constraints related to tasks such as latency requirements, and the Quality Of Service (QoS) can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, we study the task offloading problem from a matching perspective and propose an Edge-User Assignment Algorithm (EUAA) that aims to maximize the resource utilization of edge servers and maximize the number of assigned IoT users. The main concern, in any matching algorithm, is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We have used state-of-the-art schemes to compare the performance of the proposed algorithm under different simulation scenarios. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, edge server resources used, and execution time. The simulation results confirm that significant profit and satisfied user can be achieved by the proposed algorithm. © 2014 IEEE.

关键词： Computation offloading

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Laser surface treatment for enhanced surface quality of Si3N4 + SiO2 wave-transparent ceramics

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Ceramics International 2025年第13期51卷 17079-17089页

作者： Gupta, Rajaram Kumar Rana, Divya Deva, Dinesh Pal, Vijay Kumar Department of Mechanical Engineering Indian Institute of Technology Jammu Jammu & Kashmir 181221 India Department of Material Science and Engineering Indian Institute of Technology Kanpur 208016 India Department of Chemical Engineering Indian Institute of Technology Kanpur 208016 India

Si₃N₄ + SiO₂-based wave-transparent ceramics are valued for their excellent mechanical strength and superior dielectric properties, making them suitable for radome applications. A recent study explored the production of these ceramics through compression molding of green pellets, followed by sintering (at 1500 °C). However, further improvements in mechanical properties without compromising dielectric performance are essential for use in harsh conditions. In this study, surface treatment using a CO₂ laser with a 10.6 μm wavelength was applied to green Si₃N₄ + SiO₂ ceramic compacts (GP), prepared by compression molding at 1912 MPa, to investigate changes in surface integrity, microstructure, and chemical composition before and after sintering. Thermal gravimetric analysis (TGA) confirmed that oxidation began at 700 °C, with a mass gain of 18 % up to 1500 °C. XRD and XPS confirmed Si₃N₄ and SiO₂ phases in the green pellets, with optimal laser parameters (10 W power, 15 mm/s) maximizing SiO₂ formation and inducing compressive residual stresses, of up to −128 MPa on green ceramic surface to −2045 MPa after sintering. This treatment significantly improved microhardness, increasing from 1566 ± 6.7 HV₀.₁ in untreated sintered samples to 1651 ± 7.1 HV₀.₁ in sintered, laser-treated samples. XRD results confirmed the formation of Si₃N₄, cristobalite-SiO₂, and Si₂N₂O phases in the laser-treated sintered ceramics which was also confirmed by the SEM micrograph. Line EDS (energy-dispersive spectroscopy) verified that the elemental composition of the laser-treated sintered surface remained consistent with that of the untreated sintered surface. These findings demonstrate that CO₂ laser treatment significantly enhances the mechanical properties of Si₃N₄-SiO₂ ceramics without compromising their dielectric performance, thereby greatly improving the material's suitability for radome applications in challenging environmental conditions. © 2025

关键词： Compression molding

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