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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Vaidya, Akhil Tulsiyan, Darsh Surve, Omkar Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350352931

Accurate traffic sign recognition is crucial for driver assistance systems, yet challenges persist due to environmental factors and camera distortions. This study introduces a novel framework for real-time traffic sign detection and classification using vehicle dash-cams. The system employs a fine-tuned YOLOv8 model for detection, achieving mAP50 of 99.348%. Classification utilizes a two-stage approach: VGG16-based image embedding generation followed by Random Forest classification, resulting in an F 1 score of 98.61%. The model is trained on a diverse dataset combining GTSRB and web-scraped images, with extensive augmentation to enhance robustness. Additionally, Microsoft's TrOCR extracts numerical values from speed limit signs. This hybrid approach merges the strengths of convolutional neural networks and Random Forests, producing a robust solution even with a very limited amount of training data. The system's high accuracy and real-time performance significantly enhance driver safety by ensuring reliable traffic sign recognition across various conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Temporal Intelligence: Recognizing User Activities with Stacked LSTM Networks

Temporal Intelligence: Recognizing User Activities with Sta...

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International Conference on Advanced Communications and Machine Intelligence, MICA 2023

作者： Singh, Vikram Kumar, Sohan Computer Engineering Department National Institute of Technology Kurukshetra136119 India

ISBN: (纸本)9789819762217

The advent of sensor data extracted from the smartphone offers opportunities to accurately model the human’s physical activities for context-aware scenarios or maybe for human-centered computing, e.g., human-in-loop, personalized computational tasks, etc. In the paper, we proposed a stacked LSTM-based human activity recognition strategy, which operates on multi-input (sensor datasets, accelerometer, gyroscope, and linear acceleration). The designed activity detection model asserts higher recognition accuracy, 98.56% and significant improvement as compared to other equivalents, over seven human physical activities: Walking, Sitting, Standing, Jogging, Biking, Walking upstairs, and Walking downstairs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ambient intelligence

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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Network Intrusion Detection System for Smart Environments with High performing Novel Feature Selection Approach 2

Network Intrusion Detection System for Smart Environments wi...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Walling, Supongmen Lodh, Sibesh National Institute of Technology Department of Computer Science and Engineering Dimapur India

ISBN: (纸本)9798350388916

The Internet of Things (IoT) has experienced significant growth in data traffic, resulting in increased complexity and dimensionality. Intrusion Detection Systems (IDS) play a vital role in protecting against various types of cyber threats. IoT IDS systems encounter major challenges because of the varied functionality and physical diversity within IoT environments. The vast and ever-changing nature of IoT traffic makes it challenging and impractical to utilize all features and attributes for effective self-protection in IDS. To tackle these challenges, this study presents AN-SFS: A Dynamic and Adaptive Method for Optimal Feature Selection that adjusts to local data statistics. As opposed to standard approaches, AN-SFS modifies its threshold based on current feature subsets and neighboring feature statistics, enhancing its ability to capture subtle relationships and dependencies for more robust and effective selection. We assessed our model using the CSECICIDS-2018 dataset and found it to outperform traditional machine learning classifiers in detection rate, precision, and recall. It achieved an exceptional accuracy rate of 99.9% and a low false positive rate of 0.022, markedly exceeding the performance of current methods. © 2024 IEEE.

关键词： Network intrusion

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Leveraging Linguistically Enhanced Embeddings for Open Information Extraction 30

Leveraging Linguistically Enhanced Embeddings for Open Infor...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Farooqui, Fauzan Jayakumar, Thanmay Mathur, Pulkit Radke, Mansi Department of Computer Science and Engineering Visvesvaraya National Institute of Technology India

ISBN: (纸本)9782493814104

Open Information Extraction (OIE) is a structured prediction (SP) task in Natural Language Processing (NLP) that aims to extract structured n-ary tuples - usually subject-relation-object triples - from free text. The word embeddings in the input text can be enhanced with linguistic features, usually Part-of-Speech (PoS) and Syntactic Dependency Parse (SynDP) labels. However, past enhancement techniques cannot leverage the power of pretrained language models (PLMs), which themselves have been hardly used for OIE. To bridge this gap, we are the first to leverage linguistic features with a Seq2Seq PLM for OIE. We do so by introducing two methods - Weighted Addition and Linearized Concatenation. Our work can give any neural OIE architecture the key performance boost from both PLMs and linguistic features in one go. In our settings, this shows wide improvements of up to 24.9%, 27.3% and 14.9% on Precision, Recall and F1 scores respectively over the baseline. Beyond this, we address other important challenges in the field: to reduce compute overheads with the features, we are the first ones to exploit Semantic Dependency Parse (SemDP) tags;to address flaws in current datasets, we create a clean synthetic dataset;finally, we contribute the first known study of OIE behaviour in SP models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Embeddings

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A Novel Approach for Detection of Malicious Attack during Post Disaster Using UAV Assisted Delay Tolerant Network 2

A Novel Approach for Detection of Malicious Attack during Po...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Chakrabarti, Chandrima Narula Institute Of Technology Department Of Computer Science And Engineering Kolkata India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381665

Utilizing delay tolerant networks (DTN) via smartphones is a practical approach, especially when traditional networks are compromised. The use of short-range communication technologies like Bluetooth or WiFi ensures connectivity among ground nodes, albeit with limitations in determining efficient routes across the entire disaster area. Incorporating Unmanned Aerial Vehicles (UAVs) into the network introduces a dynamic element that can significantly enhance message delivery speed and route determination However, the introduction of UAVs also brings new challenges, particularly in ensuring the security and reliability of communication. Malicious attacks, such as message dropping or manipulation, can disrupt the network's overall functionality and effectiveness. This paper proposes the use of monitor nodes to track both UAVs and ground nodes, identifying and mitigating malicious nodes to ensure successful message delivery. Simulation is done using Opportunistic Network Environment (ONE). The simulator provides valuable insights into the proposed methods' effectiveness. © 2024 IEEE.

关键词： Delay tolerant networks

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Implementation of Elliptic Curve Cryptosystem with Bitcoin Curves on SECP256k1, NIST256p, NIST521p, and LLL

Journal of ICT Standardization

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Journal of ICT Standardization 2023年第4期11卷 329-354页

作者： Ulla, Mohammed Mujeer Preethi Khan, Md Sameeruddin Sakkari, Deepak S. School of Computer Science and Engineering Presidency University Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bangalore India

Very recent attacks like ladder leaks demonstrated the feasibility of recovering private keys with side-channel attacks using just one bit of secret nonce. ECDSA nonce bias can be exploited in many ways. Some attacks on ECDSA involve complicated Fourier analysis and lattice mathematics. This paper will enable cryptographers to identify efficient ways in which ECDSA can be cracked on curves NIST256p, SECP256k1, NIST521p, and weak nonce, kind of attacks that can crack ECDSA and how to protect yourself. Initially, we begin with an ECDSA signature to sign a message using the private key and validate the generated signature using the shared public key. Then we use a nonce or a random value to randomize the generated signature. Every time we sign, a new verifiable random nonce value is created, and a way in which the intruder can discover the private key if the signer leaks any one of the nonce values. Then we use Lenstra–Lenstra–Lovasz (LLL) method as a black box, we will try to attack signatures generated from bad nonce or bad random number generator (RAG) on NIST256p, SECP256k1 curves. The combination of nonce generation, post-message signing, and validation in ECDSA helps achieve Uniqueness, Authentication, Integrity, and Non-Repudiation. The analysis is performed by considering all three curves for the implementation of the Elliptic Curve Digital Signature Algorithm (ECDSA). The comparative analysis for each of the selected curves in terms of computational time is done with the leak of nonce and with the Lenstra–Lenstra–Lovasz method to crack ECDSA. The average computational costs to break ECDSA with curves NIST256p, NIST521p, and SECP256k1 are 0.016, 0.34,0.46 respectively which is almost zero depicting the strength of the algorithm. The average computational costs to break ECDSA with curves SECP256K1 and NIST256p using LLL are 2.9 and 3.4 respectively. © 2023 River Publishers.

关键词： Authentication

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An improved evaporation rate water cycle algorithm for energy-efficient routing protocol in WSNs

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International Journal of Intelligent Computing and Cybernetics 2023年第1期16卷 30-45页

作者： Vimala Dayalan Manikandan Kuppusamy School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndia

Purpose-The paper aims to introduce an efficient routing algorithm for wireless sensor networks(WSNs).It proposes an improved evaporation rate water cycle(improved ER-WC)algorithm and outlining the systems performance in improving the energy efficiency of *** proposed technique mainly analyzes the clustering problem of WSNs when huge tasks are ***/methodology/approach-This proposed improved ER-WC algorithm is used for analyzing various factors such as network cluster-head(CH)energy,CH location and CH density in improved *** proposed study will solve the energy efficiency and improve network throughput in ***-This proposed work provides optimal clustering method for Fuzzy C-means(FCM)where efficiency is improved in *** evaluations are conducted to find network lifespan,network throughput,total network residual energy and network *** limitations/implications-The proposed improved ER-WC algorithm has some implications when different energy levels of node are used in *** implications-This research work analyzes the nodes’energy and throughput by selecting correct CHs in intra-cluster *** can possibly analyze the factors such as CH location,network CH energy and CH ***/value-This proposed research work proves to be performing better for improving the network throughput and increases energy efficiency for WSNs.

关键词： WSNs Improved ER-WCA Energy efficiency Routing protocol PCM Fuzzy C-Means Paper type Research paper

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AGG: attention-based gated convolutional GAN with prior guidance for image inpainting

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Neural Computing and Applications 2024年第20期36卷 12589-12604页

作者： Yu, Xiankang Dai, Lei Chen, Zhihua Sheng, Bin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Image inpainting has made great achievements recently, but it is often tough to generate a semantically consistent image when faced with large missing areas in complex scenes. To address semantic and structural alignment in existing methods for image inpainting, this paper proposes an end-to-end attention-based gated convolution GAN with prior guidance named AGG, which designs the spatial and channel attention mechanisms for full extraction of semantic and structural features. Moreover, AGG constructs the attention-based upsampling module based on the channel attention to refine the feature map and capture more details from features of up-level low sizes. AGG uses the image contour as prior, allowing the gated convolution and attention mechanism may fill the image efficiently by focusing on the contour information. The attention-based gated convolution can effectively capture the global features and compensate for the limitations of the restricted receptive field of the naive convolution. Compared to other models, AGG generates images with finer outline features and no common problems such as the watermark and blur, which shows the best overall performance on the Paris StreetView, CelebA-HQ, and Places2 datasets. The best FID, LPIPS, PSNR, and SSIM values achieved by AGG are 2.18, 0.046, 30.82, and 0.951 on the CelebA-HQ dataset, with at least 3.21% and 6.52% performance improvement on FID and LPIPS compared to state-of-the-art methods, respectively. The source code will be available at https://***/Shawn-Yu-1/AGGNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. corrected publication 2024.

关键词： Convolution

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Adaptable Machine Learning Methods for a Twitter Followee Recommendation System 2

Adaptable Machine Learning Methods for a Twitter Followee Re...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Yadav, Sandeep Srivastav, Shashank Buddha Institute of Technology Computer Science and Engineering Department Gorakhpur India

ISBN: (纸本)9798350368109

Recommender systems for social networking have recently used machine learning (ML) methods. This paper describes a multi-agent system that simulates a Twitter recommender system to give users a list of helpful suggestions, i.e., a list of users who are followed, or they want to follow. When processing data in parallel on multi-node distributed systems, a machine learning technique (such as a neural network or multilayer perceptron) is evaluated for scalability using a simulator. A multi-agent modelling simulates the dispersed environment. The quantity of nodes, the method used within the model recommender framework, and the real followers and followees data are the first parameters that need to be configured on the simulator. The experimental findings were acquired on three different datasets to evaluate the speed and precision of a simulated recommender system when evaluating the machine learning method under various conditions. © 2024 IEEE.

关键词： Tweets

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