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Analyzing Network Traffic Features for IoT Malware Detection through Forensic Methods 14

Analyzing Network Traffic Features for IoT Malware Detection...

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14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Kapoor, Mehak Patidar, Nitesh Arya, Namita Centre of Internet of Things MITS Gwalior Madhya Pradesh India

ISBN: (纸本)9798331531935

this emerges as an issue given that the rapid proliferation of IoT (Internet of things) devices came alongside several security issues in which IoT malware became an issue of interest in this regard. Due to the fact that IoT devices are of different kinds then their network interaction is also not uniform hence this work proposes a new approach for detecting IoT malware using forensic methods that analyze network traffic features. the focus of the study will be on the network traffic patterns (data flow, packet characteristics and communication protocols) in order to identify anomalous behaviors which are likely to contain malicious activity. Forensic analysis techniques are applied on network logs to build up a picture of what attack scenarios might look like and where suspicious traffic is coming from. Statistical and machine learning methods are applied at an advanced level to classify traffic as normal or abnormal in order to improve detection accuracy. this approach provides a non-intrusive and scalable solution for real-time detection of IoT malware without exposing vulnerable IoT ecosystems to emerging threats. © 2025 IEEE.

关键词： computer viruses

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Network and security Solution using Jaya Algorithm-based graph search for Drone Transportation 14

Network and Security Solution using Jaya Algorithm-based gra...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Patel, Pramod Kumar Khan, Nikhat Raza Mishra, Aishwarya Agrawal, Akrati Daravai, Archana Department of Electronics and Communication Engineering IES College of Technology Bhopal India Department of Computer Science and Engineering IES College of Technology Bhopal India

ISBN: (纸本)9798331531935

this study reports the suitability of several algorithms to identify the best course in a network of lines that is utilized to guide an Unmanned Arial Vehicle (UAV) aircraft in a safety configuration. It utilizes a waypoint graph using Jaya algorithm for this purpose, which is based on the GPS coordinates of flight route movement locations in three dimensions. It performs a comparative analysis of the drone path prediction using a dynamic adjacency matrix that represents the shortest path in the graph. In the context of the area observation mode, evaluate the benefits and drawbacks of the two routing methods. © 2025 IEEE.

关键词： Target drones

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Mitigating security Vulnerabilities in Cloud-Based Home Automation through Advanced Edge Computing: A Comprehensive Framework for Privacy Enhancement and Data Protection 14

Mitigating Security Vulnerabilities in Cloud-Based Home Auto...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Biresh Kumari, Shreya Kumar, Ritesh Singh, Vineet Kumar Sharma, Binod Kumar Gupta, Kamaldeep Amity Institute of Information Technology Amity University Jharkhand Jharkhand Ranchi India Cambridge Institute of Technology Tatisilwai Jharkhand Ranchi India Dept Of IT Dr. R.M.L. Avadh University UP Ayodhya India Dept. of Computer Sci. & Engg. BIT Mesra Jharkhand Ranchi India St. Xavier’s College Jharkhand Ranchi India

ISBN: (纸本)9798331531935

With the soaring adoption of cloud-constructed home automation systems with CAGR of 8.5% from 2023 to 2030, security and privacy concerns have become critical considerations. this paper examines the security risks in cloud-based home automation systems and proposes leveraging edge computing to improve data security and privacy. We examine weaknesses including unapproved access, data leaks and highlight limitations of cloud-centric architectures. By integrating edge computing, we reduce data exposure, enable local processing, and improve control within the home environment. We explore how edge computing addresses specific attacks and offline operation during network disruptions. Challenges of incorporating edge computing into cloud-based home automation are discussed. this research contributes insights for achieving a more secure and privacy-enhanced home automation system. © 2025 IEEE.

关键词： Sensitive data

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the Evolution of Cloud Computing: A Study of Aspirational Technologies and Practical Achievements 14

The Evolution of Cloud Computing: A Study of Aspirational Te...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Upreti, Kamal Jain, Rituraj Kumar, Raj Goyal, Kamal Deepika Gupta, Komal Dept. of Computer Sciecne CHRIST University DelhiNCR India Dept. of IT Marwadi University Gujarat Rajkot India Gautam Buddha University U.P Gautam Budh Nagar India Pimpri Chinchwad University Pune India Dept. of Computer Science & Engg. Maharishi MM Haryana Ambala India Accenture Bangalore India

ISBN: (纸本)9798331531935

Cloud computing has transformed the digital landscape, providing scalability, cost efficiency, and seamless access to computing resources. Yet, there is a gap between its theoretical aspirations and real-world achievements because of data privacy concerns, regulatory compliance, vendor lock-in, and performance bottlenecks. this paper critically examines these disparities through case studies, technological breakthroughs, and industry trends as a guide to understanding these impediments to cloud adoption. the study reviews the theoretical background of cloud computing, which include models related to deployment and service, and illustrates its successes in performance, reliability, and security. Persistent barriers, however, mean security and compliance are insecure while cost unpredictability remains a concern for organizations in using the maximum potential of a cloud. the study proposes strategic solutions that can serve to bridge the gap in terms of hybrid and multi-cloud adoption, AI-driven security frameworks, regulatory compliance automation, and various cost optimization techniques. Emerging trends like quantum computing, edge computing, and green cloud initiatives are shaping the future of cloud computing. By implementing these solutions, more is made out of the potential of cloud computing in securing a more efficient and sustainable digital infrastructure. © 2025 IEEE.

关键词： Crime

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Image-based DeepFake Detection Using Artificial Intelligence 14

Image-based DeepFake Detection Using Artificial Intelligence

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Singh, Shailendra Kumar Kumar, Gautam Dubey, Aditya Kumar Yadav, Ritik Rai, Vishal Sharma, Anil Department of Computer Science and Engineering Amity University Noida India

ISBN: (纸本)9798331531935

the increasing sophistication of deepfake technology poses significant risks to privacy, security, and public trust in digital media. As a result, deepfake detection has become a critical research area, with artificial intelligence -driven approaches playing a central role. this paper presents a comprehensive analysis of convolutional neural network -based deepfake detection methods, highlighting key challenges such as dataset limitations, social media compression effects, real-time detection constraints, and the rapid evolution of manipulation techniques. While existing detection models demonstrate promising accuracy, challenges remain in terms of generalizability, computational efficiency, and robustness against complex manipulations, including audio and full-body deepfakes. this study explores strategies to enhance detection performance through data augmentation and optimized architectural design. Furthermore, it discusses potential future directions, such as leveraging multimodal analysis, federated learning, and explainable AI, to improve the reliability and adaptability of deepfake detection systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Deep Learning Enabled Secure IoT Module for Smart Agriculture in Diverse Environmental Conditions 14

Deep Learning Enabled Secure IoT Module for Smart Agricultur...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Ansari, Shaharyar Alam Ali, Salman Luqman, Mohammad Ahmad, Shahnawaz School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science Aligrah Muslim University Aligarh India

ISBN: (纸本)9798331531935

the Internet of things (IoT) stands as a revolutionary technology which impacts smart agriculture and different sectors beyond it. the research describes an intelligent IoT-enabled monitoring system which uses sensors to bring improved agricultural performance through environmental real-time observations combined with artificial intelligence decision systems. the proposed system completes its operation by executing sensor data acquisition followed by deep learning-based analysis and secure data transmission procedures. the initial stage of IoT-enabled operation utilizes environment-sensing devices which collect essential parameters for precision farming. A Recurrent Neural Network (RNN) processing system applies classification methods to collected data to generate useful insights. Secure cloud data transmission takes place through an encryption system which combines Substitution-Caesar cipher with Improved Elliptic Curve Cryptography (IECC). the system demonstrates 99.64% accuracy, and the proposed encryption method proves efficiency through its average execution times of encryption at 0.3904 ms and decryption at 0.1102 ms which exceeds current models in speed and security performance. © 2025 IEEE.

关键词： Recurrent neural networks

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Advanced Fault Sleuth System Architectures for Prognostic Anomaly Detection and Autonomous Irregularity Mitigation in Intelligent Power Networks 14

Advanced Fault Sleuth System Architectures for Prognostic An...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Biresh Anurag, Arpit Anand, Sanyam Kumar, Ritesh Gupta, Kamaldeep Sharma, Binod Kumar Amity Inst. of Information Technology Amity University Jharkhand Ranchi India Cambridge Institute of Technology Tatisilwai Jharkhand Ranchi India St. Xavier’s College Jharkhand Ranchi India Dept. of Computer Sci. & Engg. BIT Mesra Jharkhand Ranchi India

ISBN: (纸本)9798331531935

Revenue losses, inconsistent service, delayed problem detection, and electricity theft are just a few of the major issues that power distribution networks deal with. the suggested Fault Sleuth System makes use of Internet of things (IoT) technologies to facilitate remote system control, automatic anomaly detection, and real-time electrical parameter monitoring. the technology guarantees accurate fault localization and proactive theft prevention by combining sophisticated data analytics and feature extraction techniques. the technology greatly lowers maintenance costs and security threats while improving operating efficiency through thorough data collecting, preprocessing, and intelligent analysis. In addition to improving problem detection and theft mitigation, this strategy also raises customer happiness, power quality, and service reliability. the study's conclusions show how the Fault Sleuth System can revolutionize the way that important power distribution issues are handled. this technology opens the door for a more robust, safe, and effective energy infrastructure that can satisfy needs in the future by leveraging real-time analytics and state-of-the-art IoT technologies. © 2025 IEEE.

关键词： Network security

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Techniques in reliability of internet of things (IoT) 16

Techniques in reliability of internet of things (IoT)

引用

16th International conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International conference on Project MANagement, ProjMAN 2024 - 14th International conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Singh, Khushwant Yadav, Mohit Singh, Yudhvir Moreira, Fernando Department of Computer Science & Engineering U.I.E.T Maharshi Dayanand University Rohtak124001 India Department of Mathematics University Institute of Sciences Chandigarh University Punjab Mohali140413 India REMIT IJP Universidade Portucalense Porto & IEETA Universidade de Aveiro Aveiro Portugal

the Internet of things (IoT) has rapidly transformed the way we interact with technology, connecting numerous smart devices to the internet. However, with the proliferation of IoT applications, ensuring the reliability of these interconnected systems has become a critical concern. the current paper presents the reliability of IoT systems achieved through a combination of fault tolerance, robust communication protocols, data integrity, predictive maintenance, energy efficiency, and comprehensive testing. By adopting these techniques, IoT deployments can ensure dependable and resilient operations, fostering the growth and adoption of IoT technologies across diverse industries. this abstract highlights various techniques employed to enhance the reliability of IoT systems. Fault tolerance plays a crucial role in increasing IoT system reliability. Redundancy techniques such as replication and backup are applied to ensure continued functionality in the event of device failures. Additionally, fault detection and self-healing mechanisms are integrated into IoT devices, allowing them to identify and correct errors autonomously. Robust communication protocols are essential to maintain reliable connections among IoT devices. these protocols should be designed to handle intermittent network connectivity, reduce latency, and ensure secure data transmission. Solutions like message queuing telemetry transport (MQTT) and Constrained Application Protocol (CoAP) are commonly used for their lightweight and efficient communication properties. Data integrity and security are paramount in IoT applications. Various cryptographic techniques, like asymmetric encryption and digital signatures, are implemented to safeguard data and prevent unauthorized access. Moreover, regular security audits and updates are necessary to address emerging threats and vulnerabilities. Predictive maintenance is a valuable technique for enhancing IoT device reliability. By utilizing sensors and analytics, IoT sy

关键词： Fault tolerance

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Perceptual Learning Technology Based on computer Vision and Machine Learning Algorithms 14

Perceptual Learning Technology Based on Computer Vision and ...

引用

14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Wang, Tianjian School of Computer Science and Technology Wuhan Qingchuan University Hubei Wuhan430204 China

ISBN: (纸本)9798331531935

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. the mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. this may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. the mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. the perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security. © 2025 IEEE.

关键词： Image segmentation

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Enhancing IoT Cyber Attack Detection in the Presence of Highly Imbalanced Data 14

Enhancing IoT Cyber Attack Detection in the Presence of High...

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14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Haque, Md. Ehsanul Polash, Md. Saymon Hosen Al-Imran, Md. Simla, Sanjida Hossain, Md Alomgir Jahan, Sarwar Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9798331531935

Due to the rapid growth in the number of Internet of things (IoT) networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. High rate of missed threats can be the result as traditional machine learning models tend to struggle in identifying attacks as normal data volume is so much higher than the volume of attacks. For example, the data set used in this study reveals a strong class imbalance with 94,659 instances of the majority class and only 28 instances of the minority class, in which determining rare attacks accurately is quite challenging. the challenges presented in this research are addressed by hybrid sampling techniques designed to drive data imbalance detection accuracy in IoT domains. After doing so, we then evaluate the performance of several machine learning models such as Random Forest, Soft Voting, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), MultiLayer Perceptron (MLP) and Logistic Regression with respect to the classification of cyber-attacks accurately. the obtained results indicate that the Random Forest model achieved the best performance value of 0.9903 of Kappa score, 0.9961 of test accuracy and 0.9994 of AUC. It also shows strong performance in the Soft Voting model, with an accuracy of 0.9952 and AUC of 0.9997, while the latter is an indication of the benefits of combining models’ prediction. Overall, this work has shown the great benefit of hybrid sampling combined with robust model selection and feature selection to deliver a dramatic increase in of IoT security against cyber-attack, an important factor for implementing security in environments with strongly imbalanced data. © 2025 IEEE.

关键词： Cyber attacks

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