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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Takkar, Sakshi Rakhra, Manik Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798331542375

Artificial Intelligence (AI) has made huge enhancements in colon cancer research through its methods for diagnosis, treatment, and custom detection of one of the most common and fatal cancers suffered globally. Through AI deep learning and machine learning methods, tremendous possibilities have been realized in automating, for example, polyp detection in colonoscopy images, cancer classification from histopathological slides, and even predicting disease treatment outcomes. The current direction is towards transfer learning, hybrid model building and AI applications in surgery robots aimed at more accurate diagnosis and treatment procedures. Besides them, however, they have serious disadvantages, access to computer learning systems and datasets are limited, AI models come without explanation, embedding of artificial intelligence into clinical practices becomes troublesome. In future development, emphasize should be on creating databases, explainable ethics AI approaches, building bridges between developers and practitioners in medicine. The objective of the paper is how to incorporate research that has been done in moving colon cancer AI systems into practice, understanding the AI satellites put on colon cancer research. © 2025 IEEE.

关键词： Deep learning

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Unified Face Matching and Physical-Digital Spoofing Attack Detection

arXiv

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arXiv 2025年

作者： Kunwar, Arun Rattani, Ajita Dept. of Computer Science and Engineering University of North Texas Denton United States

Face recognition technology has dramatically transformed the landscape of security, surveillance, and authentication systems, offering a user-friendly and non-invasive biometric solution. However, despite its significant advantages, face recognition systems face increasing threats from physical and digital spoofing attacks. Current research typically treats face recognition and attack detection as distinct classification challenges. This approach necessitates the implementation of separate models for each task, leading to considerable computational complexity, particularly on devices with limited resources. Such inefficiencies can stifle scalability and hinder performance. In response to these challenges, this paper introduces an innovative unified model designed for face recognition and detection of physical and digital attacks. By leveraging the advanced Swin Transformer backbone and incorporating HiLo attention in a convolutional neural network framework, we address unified face recognition and spoof attack detection more effectively. Moreover, we introduce augmentation techniques that replicate the traits of physical and digital spoofing cues, significantly enhancing our model robustness. Through comprehensive experimental evaluation across various datasets, we showcase the effectiveness of our model in unified face recognition and spoof detection. Additionally, we confirm its resilience against unseen physical and digital spoofing attacks, underscoring its potential for real-world applications. © 2025, CC BY.

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Performance Evaluation of Quantum-Resistant TLS for Consumer IoT Devices

Performance Evaluation of Quantum-Resistant TLS for Consumer...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Jessica Bozhko Yacoub Hanna Ricardo Harrilal-Parchment Samet Tonyali Kemal Akkaya Dept. of Computer Science Auburn University Auburn USA Dept. of Electrical and Computer Engineering Florida International University Miami USA Dept. of Software Engineering Gumushane University Gumushane Turkey

Post-quantum (PQ) cryptographic algorithms are currently being developed to be able to resist attacks by quantum computers. The practical use of these algorithms for securing networks will depend on their computational and communication efficiency. In particular, this is critical for the security of wireless communications within the context of consumer IoT devices that may have limited computational power and depend on a constrained wireless bandwidth. To this end, there is a need to evaluate the performance of widely used application layer security standards such as transport layer security (TLS) to understand the use of the existing PQ algorithms that are being evaluated by NIST as a replacement to the current cryptographic algorithms. This paper focuses on two widely used IoT standards Bluetooth Low Energy (BLE) and WiFi to find out the optimal performing PQ algorithm for their security when used in end-to-end connections over the Internet. By implementing the capability for IP over BLE and all options of TLS connection establishment, we developed a client-server IoT testbed to measure the efficiency of PQ key encapsulation mechanisms (KEMs) and PQ digital signature algorithms. The test results showed that Kyber512 is the ideal KEM while Falcon-512 and Dilithium2 are the best signatures for BLE and WiFi devices. Based on this outcome, we developed a mechanism for IoT devices with multiple communication interfaces, that dynamically chooses a PQ KEM algorithm based on the MAC layer protocol being used at the time.

关键词： Wireless communication Performance evaluation Encapsulation Heuristic algorithms Resists Internet of Things Cryptography

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Security and Privacy Challenges in Semantic Communication Networks

Security and Privacy Challenges in Semantic Communication Ne...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Quang Tuan Do Dongwook Won Tung Son Do Thanh Phung Truong Sungrae Cho Dept. of Computer Science and Engineering Chung-Ang University Seoul South Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Semantic communication (SemCom) has emerged as a pivotal advancement in communication systems by focusing on the transmission of task-relevant meaning rather than raw data. This paradigm shift enables efficient communication for intelligent systems but also introduces new security and privacy risks. This paper explores these risks, reviews state-of-the-art countermeasures, and identifies key challenges and future directions in ensuring secure and private semantic communication. By addressing these issues, SemCom can fulfill its potential in domains such as healthcare, IoT, and autonomous systems.

关键词： Privacy Reviews Federated learning Mission critical systems Focusing Transforms Medical services Semantic communication Robustness Security

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Survey on Internet of Medical Things Security: A Comprehensive Review

Survey on Internet of Medical Things Security: A Comprehensi...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Seungbin Lee Jiyoon Kim Dept. of Computer Science and Engineering Gyeongsang National University Jinju Republic of Korea

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

IoMT is emerging as a key technology that enables efficient physician diagnosis, such as real-time patient monitoring and data collection in modern healthcare. IoMT is used through IoMT devices such as IMD, IoWDs, and APS. IoMT offers benefits such as improved patient care and optimization of medical processes. However, these advancements are accompanied by security vulnerabilities. Therefore, security requirements and security frameworks are required. In this paper, we examine the major security requirements of IoMT, such as patient privacy protection, data encryption, and integrity of medical information. In addition, we analyze unique characteristics and specific threats in the IoMT environment and present security issues. To solve these issues, this paper comprehensively analyzes encryption strategies, authentication protocols, blockchain, AI, and ML proposed in the latest research. This paper is expected to be the cornerstone of the design and verification of essential security elements in IoMT and will make an important contribution to future IoMT security research.

关键词： Surveys Patient monitoring Protocols Reviews Internet of Medical Things Real-time systems Encryption Security Protection Medical diagnostic imaging

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Deepfakes - Generating Synthetic Images, and Detecting Artificially Generated Fake Visuals Using Deep Learning

Deepfakes - Generating Synthetic Images, and Detecting Artif...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Ayushi Mishra Aadi Bharwaj Aditya Kumar Yadav Khushi Batra Nidhi Mishra Dept. of Computer Science and Engineering Amity University Noida Noida India

The abstract delves into the groundbreaking realm of DeepFakes, a revolutionary synthesis of deep learning and synthetic media. Central to DeepFake generation is the utilization of Generative Adversarial Networks (GANs), particularly the innovative mechanisms of face reenactment involving DCGANs (Deep Convolutional GANs) and Autoencoders. These technologies empower a generator to transform random noise into hyper-realistic visuals, capturing intricate details such as facial expressions and lighting conditions through latent space interpolation. The adversarial interplay between generator and discriminator continuously refines the authenticity of the generated content. While DeepFakes unlocks creative possibilities for artists and filmmakers, the abstract underscores the ethical concerns surrounding misinformation, privacy breaches, and trust erosion in digital media. The discourse navigates the delicate balance between creative freedom and responsible use, highlighting how DeepFakes, with their roots in advanced deep learning techniques, redefine our perception of synthetic media, challenging notions of reality in our increasingly digital world.

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A Multi-Model Rouge Nodes Detection System for Fog Computing

A Multi-Model Rouge Nodes Detection System for Fog Computing

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computer science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Thanh Duc Bui Brajendra Panda Dept. of Computer Science and Computer Engineering University of Arkansas Fayetteville USA

With the increasing speed of advance of both Internet of Things (IoT) and the demands of utilizing IoT devices in different locations with high compatibility, fog computing has been foreseen as a sustainable solution. However, new challenges related to fog computing's cyber security also emerge. Among various cyber-attacks on the fog computing system, one standard method is to implement corrupted internal nodes that allow malicious/compromised access from attackers, threatening the confidentiality of users' information and the system's performance. In this paper, we propose a method to quantify the distribution of each attribute value from historical access logs from each node within a fog computing system. We also utilize an unsupervised machine learning method to separate nodes into smaller clusters with respect to different models to improve the forecasting ability of the system. Finally, our models are evaluated over simulated data using real access logs. Our experimental results illustrate that our proposed method achieves some advanced performance over applying an overall classification, even using the same classification machine learning methods and dataset.

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A Hybrid Deep Learning Approach to Classify Colon Cancer from Colonoscopy Image

A Hybrid Deep Learning Approach to Classify Colon Cancer fro...

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International Conference on electrical and computer engineering (ICECE)

作者： Mahimul Islam Nadim Shayla Alam Setu Amir Sohel Talha Zubaer Rifatul Islam Rifat Md Mizanur Rahman Dept. Of Computer Science & Engineering Daffodil International University Savar Dhaka

ISBN: (数字)9798331531782

ISBN: (纸本)9798331507923

These days, colon cancer is a frightening global problem that kills many people. Particularly in countries in South Asia, it is growing daily. Because early discovery can significantly affect the cancer’s survival. An AI and machine learning integration technique must be developed to do this. Our main goal is to use cutting-edge machine-learning algorithms to detect colon cancer. 6000 raw pictures of colon cancer in four classes—Normal, U1cerative_Colits, Polyps, and Esophagitis— were used in this study. After applying image pre-processing techniques to improve the data quality, we implemented three advanced deep-learning techniques using our Customized model. Our customized model (InceptionResNetV2 + customized layers) provides the maximum accuracy of 95.88%, whereas VGG19, Inception v3, and ResNet50 provide healthy accuracy. We have performed a comparative analysis with related work by those who work with the same dataset, and our model outperformed all of them. Our suggested model, preprocessing strategies, and data verification approach demonstrate an influential body of work in the medical field. Due to its advanced AI and ML-based research, this could have a significant impact on the computerized medical sector.

关键词： Deep learning Analytical models Accuracy Computational modeling Colorectal cancer Data models Reliability Biomedical imaging Diseases Residual neural networks

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A Comparative Analysis of Machine Learning Prediction Algorithms for Detecting IoT Botnet Activities

A Comparative Analysis of Machine Learning Prediction Algori...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Happy Rita Chhikara Neeti Kashyap Dept. of Computer Science & Engineering The NorthCap University Gurugram Haryana India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

As the prevalence of Internet of Things (IoT) products keeps increase, the need for robust security measures becomes increasingly vital. Our paper addresses this concern by conducting a detailed comparative analysis of various machine learning methods, assessing their effectiveness in detecting and predicting malicious activities associated with IoT botnets. This paper meticulously examines initial identification methods for IoT botnet operations using advanced machine learning (ML) prediction techniques. To achieve this, we utilize the CICIoT2023 Dataset, a real-world IoT dataset obtained from networks that captures diverse device interactions and communication patterns. This dataset acts as the framework for constructing and evaluating numerous machine learning techniques, including support vector machines (SVM), k-nearest neighbours (k-NN), Naive Bayes, random forest (RF), logistic regression (LR), and decision trees (DT) approaches. Performance metrics such as accuracy, precision, F1-score, ROC curve, confusion matrix, and recall are employed to gain insights into the algorithms’ capabilities in botnet detection. Furthermore, this paper delves into an examination of the trade-offs between computational complexity and detection accuracy. This analysis aids in selecting the most suitable ML techniques tailored to specific IoT security scenarios. This comparative analysis lays the groundwork for the advancement of IoT botnet discovery strategies, providing essential insights to researchers, practitioners, and industry experts working to strengthen IoT ecosystems against growing cyber threats. We anticipate that our findings will spark more conversations and developments in the sector, promoting the establishment of more robust and adaptable security measures across the IoT landscape.

关键词： Support vector machines Radio frequency Machine learning algorithms Accuracy Botnet Oral communication Prediction algorithms

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An Advanced computer Vision Model for Aiding Automobile in Traffic Sign Classification

An Advanced Computer Vision Model for Aiding Automobile in T...

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Computing and Data science (ICCDS), International Conference on

作者： Veera Satya Pavan Vignesh Kukkadapu Sai Prajith Goddati Parthav Gokul Cherukuri Adarsh Goluguri NV Rajareddy Dept. of Computer Science & Engineering GITAM Deemed to be University Visakhapatnam India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

Accurate traffic sign recognition is crucial for autonomous vehicles. This paper proposes a novel deep learning approach for sign classification that addresses limitations in training data. Our method employs a three-step process: strategic data augmentation with rotation limitations for realistic variations, feature extraction using parallel atrous convolution layers with varying dilation rates to capture multi-scale information, and robust feature map generation through output concatenation. This approach achieves a remarkable accuracy of 94.65%, surpassing the performance of established pre-trained models like VGG-16, ResNet-50, and AlexNet. This research contributes a significant advancement in real-world traffic sign classification in autonomous vehicles.

关键词： Training Solid modeling Accuracy Navigation Computational modeling Roads Transportation

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