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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 ...

引用

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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DynaCache: A Checkpoint Aware Reconfigurable Cache for Intermittently Powered Computing Systems 32

DynaCache: A Checkpoint Aware Reconfigurable Cache for Inter...

引用

32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Mahanta, Rishabh Kapoor, Hemangee K. Indian Institute of Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331539672

Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undeniable environmental costs. Distinct challenges have emerged such as performance enhancement, crash consistency of data, energy management, etc. To tackle these challenges we propose DynaCache: A checkpoint-aware reconfigurable cache for Intermittent powered computing systems. It is an intermittent computing architecture consisting of a reconfigurable L1 cache that dynamically trans- forms its write policy to strike a favorable trade-off between per- formance, data consistency, and forward progress of a program. Our proposal achieves a speedup in performance of 1.85x and 1.5x compared to non-volatile write-back cache and volatile write- through cache respectively. It also achieves a 99% reduction in dirty data compared to a volatile write-back cache guaranteeing a near absolute data consistency in a scenario of a power failure. © 2024 IEEE.

关键词： Cache memory

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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...

引用

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...

引用

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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PraaKrum: A Practical Byzantine-Resilient Federated Learning Algorithm 16

PraaKrum: A Practical Byzantine-Resilient Federated Learning...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kukreja, Prabhleen Mahendran, V. Indian Institute of Technology Tirupati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Federated Learning (FL) is considered as a suitable paradigm for intelligent data analytics over Internet of Thing (IoT) devices. While the data-privacy preserving feature of FL is useful, the lack of data auditing abilities of the participating IoTs creates a potential attack surface. To this end, a potential attacker can perform data/model poisoning attacks on the FL. To protect from such attacks, a popular theoretically proven algorithm for FL namely, Krum [1] was proposed. From an implementation perspective, the Krum algorithm needs to be provided with apriori information such as the total number of attackers in each round, which is impractical. It is not feasible to know, in advance, which of the participating IoT clients are attackers. Furthermore, in the presence of mobile Parameter Server (PS) which stays in the WiFi coverage area consisting of stationary clients for an unknown time, the implementation of Krum algorithm in such practical setting is not investigated, to the best of our knowledge. With an aim to provide a practical implementation of a resilient FL framework, for the first time, this paper investigates the sensitivity of the attackers information towards the robustness offered by the Krum-based FL algorithm. The insights gained from the sensitivity analysis are used to develop a novel and Practical algorithm of Krum (PraaKrum) for FL for a WiFi network with mobile PS. The developed analysis is helpful in both understanding the key resilient characteristics offered by the Krum algorithm and providing the research community with a guideline towards the practical implementation of the attacker-resilient Krum-based FL algorithm in a mobile wireless setting. © 2024 IEEE.

关键词： Byzantine-resilience Federated learning insensitivity result Krum algorithm WiFi wireless IoT

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Real-Time Myanmar Hand Gestures Recognition Using Deep Learning Models 8th

Real-Time Myanmar Hand Gestures Recognition Using Deep Learn...

引用

8th International Conference on Intelligent Technologies, ICIT 2023

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Indian Knowledge System Lab International Institute of Information Technology Bangalore Karnataka Bengaluru560100 India

ISBN: (纸本)9789819738588

The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture images depicting 14 classes of Myanmar words and numbers. The present system utilizes a new dataset comprising 7834 images for 14 classes obtained through Webcam. The objective of this study is to enhance the robustness of the new model. Next, we proceed to evaluate the proposed model by comparing them to those of existing convolutional neural network models. These models are generally recognized as effective deep-learning approaches for recognition tasks. The proposed model exhibits a performance level of 96% for both validation and testing accuracies. Furthermore, the experimental finding demonstrates that the model, convolutional layers, achieved an accuracy of 100% in recognizing Myanmar Sign Language (MSL). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Practical Defense Against Adversarial WiFi Sensing 18

Practical Defense Against Adversarial WiFi Sensing

引用

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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Emotion Privacy: A Privacy-Preserving Framework for Emotionally Enriched Video

Emotion Privacy: A Privacy-Preserving Framework for Emotiona...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Kaur, Kirandeep Agrawal, Ansh Kaur, Harkeerat Computer Science Engineering Indian Institute of Technology Jammu & Kashmir Jammu India

ISBN: (纸本)9798331533311

As the use of video systems continues to expand across various domains, addressing privacy concerns while maintaining data integrity has become increasingly challenging. This paper introduces Emotion Privacy, a versatile framework that anonymizes faces in video feeds by replacing them with their own neutral faces. Unlike traditional anonymization techniques that primarily obscure identity but often fail to hide emotional data, our approach ensures compliance with privacy regulations by neutralizing emotional expressions. By applying neutral 3D face models in real time, we achieve complete emotional concealment, thus protecting emotional cues. Experimental validation reveals that Emotion Privacy achieves a great success in anonymization, ensuring that no emotional content can be inferred from the modified videos. This work presents a scalable, privacy-preserving solution that meets contemporary regulatory standards, offering broad applicability across diverse sectors where video content and privacy concerns intersect. © 2024 IEEE.

关键词： Anonymity

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Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Networks 16

Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Ne...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Tapadar, Karnish N A Khatua, Manas Tamarapalli, Venkatesh Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The 6TiSCH standard uses IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) as a MAC layer protocol. TSCH cell scheduling determines packet transmission schedules in terms of timing and channel allocation. The Minimal Scheduling Function (MSF) and the 6P protocol facilitate schedule management dynamically. MSF involves a decision-making process to add/delete one cell at a time from the current schedule to adapt the schedule to modified traffic rate. The cell deletion is accomplished by randomly selecting one of the scheduled cells between two communicating nodes. However, random cell deletion can elevate end-to-end latency and energy consumption. Therefore, this paper proposes a Latency-aware Cell Deletion (LCD) mechanism to minimize associated average end-to-end latency and energy consumption. Simulations using the 6TiSCH simulator verified that in an 80-node network, compared to the standard MSF and another benchmark scheme IMSF, LCD achieved a substantial 44.40% and 42.39% reduction in end-to-end latency, respectively. © 2024 IEEE.

关键词： 6TiSCH Network Adaptive Link Scheduling Link Scheduling Minimal Scheduling Function

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Generative AI-Based Health Hazard Prediction from Smartphone Usage

Generative AI-Based Health Hazard Prediction from Smartphone...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Hansda, Sudeep Saha, Ruelia Misra, Sudip Indian Institute of Technology Department of Computer Science & Engineering Kharagpur721302 India

ISBN: (纸本)9798350351255

The significant increase in mobile phone usage in the past few decades has resulted in various health hazards in individuals due to continuous exposure to radiation for a longer duration of time. To address the health hazards caused by mobile phone radiation, in this paper, we propose a mobile application (app)-based recommendation system to analyze smartphone usage patterns and Generative Artificial Intelligence (GAI)-based alert generation and recommendations to the user. We collect data on user's mobile phone usage, including screen time, number of near-the-ear long-duration calls, and exposure to external radiation. These collected data offer a deeper understating of radiation exposure through mobile phone usage patterns of the user using the k-means clustering algorithm. As the excessive use of mobile phones leads to various health risks, including adverse physical and mental health conditions besides reduced productivity and social isolation, the designed application tracks the duration of user's different application usage and generates an alert on excessive mobile usage, including longer near-the-ear calls and screen time as well as exposure to high external radiation. The app-based recommendation system also uses the GAI algorithm to inform users about their mobile usage pattern on a daily basis and the possibility of health hazards to help users take corrective action to reduce mobile usage. From the obtained result, we observe that the designed application is capable of precisely identifying and alerting the users based on their mobile usage. © 2024 IEEE.

关键词： Radiation hazards

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