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

作者： Gurushanker, Abirami Anandakumaran, Kirtanaa Christopher Columbus, C. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

As 5G networks advance, cybersecurity vulnerabilities in IoT devices are increasingly exposed. While intrusion detection systems (IDS) have progressed, there is a notable gap in IDS specifically designed for the unique protocols and complexities of 5G networks. Current research often emphasises binary classification, missing the nuanced multiclass classification needed for effective threat detection. This study addresses these deficiencies by integrating machine learning and deep learning techniques within the 5G core architecture to develop an advanced IDS. Utilising TCP/IP flow statistics and PFCP signalling attack data, and focusing on control plane signalling between the Session Management Function (SMF) and User Plane Function (UPF), various models are evaluated for detecting anomalous activities. The study achieves 97% accuracy with TCP/IP data and 69% with PFCP data. The proposed IDS, tailored for 5G-specific workloads and trained on PFCP protocol data, enhances detection capabilities and addresses significant security challenges, improving the resilience of IoT devices in 5G networks. © 2024 IEEE.

关键词： Network intrusion

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Complementarity Fusion RGB-T Tracking Based on Siamese Network 9

Complementarity Fusion RGB-T Tracking Based on Siamese Netwo...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Wang, Wei Yang, Congxian Yan, Zhengbang Quan, Wenjun School of computer science and engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9798350376548

In recent years, the Siamese network has gained popularity in RGB-T tracking primarily due to its notable success in RGB object tracking. Although the speed of the existing RGB-T Siamese tracker is faster than the real-time speed, it suffers poor robustness and low accuracy when encountering tracking challenges such as low illumination and deformation. To tackle these challenges, we introduce a novel RGB-T tracking approach utilizing complementarity fusion based on Siamese network framework. In addition, we design dual-modal complementary features fusion module (referred to as CFF) to effectively capture information from RGB images and thermal infrared images. Our newly proposed tracker demonstrates outstanding performance, validated through a large number of experiments on public RGB-T tracking benchmark datasets. Our tracker achieves an impressive tracking speed surpassing 35 frames per second. © 2024 IEEE.

关键词： Object tracking

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Comparative and Preventive Analysis of Dictionary Attacks 1st

Comparative and Preventive Analysis of Dictionary Attacks

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1st International Conference on Sci/Tech and engineering, ICSTE 2023

作者： Shourya, Sanat Venkatachalam, Ilayaraja Patel, Harpal Mittal, Manit School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819947126

Hacking is one of the most widespread issues that the general public faces today. Hackers essentially use some social engineering techniques, combined with the publicly available information, to crack open the social media accounts, banking accounts, or cloud storage accounts. As a result, security and protection becomes the main motive for organizations running these platforms. The severity of threats linked to such incidents is tantamount to those linked with data leaks, security breaches, and security lapse events of their likes. Among others, dictionary attacks, a strategy employed for password cracking by trying every common-use word present in an extant wordlist, are the most common. Even though nowadays password strength classifiers are being used extensively almost everywhere which tends to keep these attacks at bay, they are still prone to hyper-personalized dictionary attacks as people are inclined to choose a password which revolves around their personal details itself. Consequently, the necessary awareness for the prevention of dictionary attacks is called for and is provided by performing an analysis of the time taken by different types of dictionary attacks and what type of password or otherwise is safe or not. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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A Computationally efficient CNN-based Deep Learning Technique for Sickle Cell Detection

A Computationally efficient CNN-based Deep Learning Techniqu...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Hemavarshini, S. Sathya Shree, S. Arumuga Arun, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331522988

An amino acid change in the hemoglobin protein causes sickle cell disease (SCD), a prevalent hereditary illness that causes red blood cells to take on a sickle shape. These malformed cells cause severe health complications, including painful episodes, organ damage, increased infection risk, and a significantly reduced lifespan. Accurate and timely diagnosis is essential for timely therapy and better results for patients. This study investigates the potential of various lightweight deep learning models, MobileNetV1, MobileNetV2, MobileNetV3 Small, and EfficientNetV0 as an efficient and accurate underlying model for SCD detection. All of these trained them on the publicly available Sickle Cell Disease Dataset, where various augmentation methods are used to enhance the resilience and generalization of the models, such as rotating, shifting, shearing, zooming, brightness, and contrast adjustments. The MobileNetV1 has achieved better performance with an accuracy of 93.2% and F1Score of 95.43% with a lesser complexity with computational parameters of 4.855 Million and a model size of 18.52 MB. These findings highlight the potential of these lightweight models to revolutionize SCD screening and diagnosis in resource-constrained environments, offering faster, more accessible, and cost-effective diagnostic tools, ultimately improving healthcare outcomes for individuals with SCD. © 2024 IEEE.

关键词： Patient treatment

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Breaking the Barrier 2k for Subset Feedback Vertex Set in Chordal Graphs 49

Breaking the Barrier 2k for Subset Feedback Vertex Set in Ch...

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49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Bai, Tian Xiao, Mingyu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783959773355

The Subset Feedback Vertex Set problem (SFVS) is to delete k vertices from a given graph such that in the remaining graph, any vertex in a subset T of vertices (called a terminal set) is not in a cycle. The famous Feedback Vertex Set problem is the special case of SFVS with T being the whole set of vertices. In this paper, we study exact algorithms for SFVS in Split Graphs (SFVS-S) and SFVS in Chordal Graphs (SFVS-C). SFVS-S generalizes the minimum vertex cover problem and the prize-collecting version of the maximum independent set problem in hypergraphs (PCMIS), and SFVS-C further generalizes SFVS-S. Both SFVS-S and SFVS-C are implicit 3-Hitting Set problems. However, it is not easy to solve them faster than 3-Hitting Set. In 2019, Philip, Rajan, Saurabh, and Tale (Algorithmica 2019) proved that SFVS-C can be solved in O*p2kq time, slightly improving the best result O*p2.0755kq for 3-Hitting Set. In this paper, we break the "2k-barrier" for SFVS-S and SFVS-C by introducing an O*p1.8192kq-time algorithm. This achievement also indicates that PCMIS can be solved in O*p1.8192nq time, marking the first exact algorithm for PCMIS that outperforms the trivial O*p2nq threshold. Our algorithm uses reduction and branching rules based on the Dulmage-Mendelsohn decomposition and a divide-and-conquer method. © Tian Bai and Mingyu Xiao.

关键词： Graph algorithms

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Survey About Value Numbering Optimization 21

Survey About Value Numbering Optimization

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Haokun, Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

Value Numbering is a fundamental technique used in optimizer compilers and model checking tools to detect equivalence between different expressions and eliminate the redundant computations. This survey reviews the evolution of value numbering, explores various algorithms, and discusses related techniques such as Herbrand equivalence. We compare the efficiency and precision of different value numbering algorithms, highlighting their strengths and weaknesses. This paper aims to provide a comprehensive understanding of value numbering and its implementations in practical, contributing to the development of more effective optimization algorithms. © 2024 IEEE.

关键词： Program compilers

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MFN: A Brain-Like Auditory Model Based on Spiking Neural Networks 21

MFN: A Brain-Like Auditory Model Based on Spiking Neural Net...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Hongyu, Qing Guangyuan, Gao Yang, Zhang Xuntong, Lin School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

In the human sensing system, information is transmitted, received, and processed in the form of action voltages or electrical pulses. The birth of Spiking Neural Networks (SNNs) has brought neural networks closer to the human brain. However, there is still a lack of a brain like model that can effectively simulate the biological auditory pathway in terms of its operational mechanism. This paper proposes a brain like auditory model that is more in line with biological speech processing mechanisms. Specifically, in the data input stage, mask encoding was introduced to simulate the masking effect in acoustics. In the feature extraction stage, a multi-scale feedback neural network was designed based on the top-down feedback mechanism in the biological auditory system. Meanwhile, the experimental results on the speech recognition task in the Google Speech Command dataset demonstrate that our auditory model has superior performance. © 2024 IEEE.

关键词： Encoding (symbols)

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Harnessing Advanced Learning for Sarcasm Detection 3

Harnessing Advanced Learning for Sarcasm Detection

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Prasanna Kumar, M. Veeramani, Karthika Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

Sarcasm in social media and in various methods of communication attracts higher attention and create profound impact even more than the customary positive and negative responses. It is necessary to propose a prototype and henceforth a fully-fledged system as a solution to detect sarcasm, to spread positive etiquette and avoid misguidance and misinformation. Unlike other text classification tasks, sarcasm detection is bit more challenging as it relies more on contextual nuances rather than actual word sentiment. The work leverages 5 learning algorithms (Logistic Regression, Multi-head Attention Encoder, Siamese Networks, BiLSTM and BiGRU) with data extracted from SARC (Self Annotated Reddit Corpus) Dataset and News Headline dataset. The models use contextual nuances, linguistic features to detect and analyze sarcasm. An analysis on how the model's performance varies based on the nature of text data (ie. formal and informal) has been made to learn about the nuanced nature of sarcasm. © 2024 IEEE.

关键词： Logistic regression

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Flourishing Fields: Modernizing Farming with Real Time AI-powered Tomato Disease Detection and Monitoring

Flourishing Fields: Modernizing Farming with Real Time AI-po...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sriram, G. Saran, D. Vignesh, M. Nallusamy, Priyanka Arumuga Arun, R. Adaline Suji, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331541170

The agricultural sector faces significant challenges due to plant diseases, which can drastically reduce crop yields and quality. This paper presents a cutting-edge solution for real-time detection and instant remediation of tomato plant diseases using a web-based interface. Leveraging deep learning models and advanced image processing techniques, our system accurately identifies ten distinct tomato diseases from leaf images. The core of the system is built using TensorFlow and OpenCV, and it operates through a user-friendly interface developed with Streamlit. This interface not only detects diseases in real-time but also provides actionable solutions, recommending specific fertilizers and treatment protocols. This holistic approach will pinpoint the location of infected crops, apply targeted treatments, and monitor the effectiveness of interventions. By harnessing technology, we aspire to empower farmers with timely information and tools to enhance crop health, reduce losses, and increase agricultural productivity. Our initial focus on tomatoes provides a robust framework that can be adapted to other crops, paving the way for smarter, more resilient agricultural practices. © 2024 IEEE.

关键词： Fruits

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