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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Prajwal N Halvi Prajwalasimha S N Durbadal Chattaraj Naveen Kulkarni Deepthika Karuppusamy Department of Computer Science and Engineering (Cyber Security) School of Engineering (SoE) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

In this paper, a hybrid approach based on Pseudo Hadamard transformation and Logistic Substitution is proposed. Inter pixel redundancy and strong correlation between the adjacent pixels in an image is condensed by imperiling it to transformation and chaotic substitution. Pixel value variation is accomplished using modified Pseudo Hadamard chaotic transformation and pixel position variation is made by substitution using S-boxes. Secrete key is used for encryption with 256 bits. S-box is constructed using chaotic logistic map to generate random numbers. Multimedia and biometric images are considered from CVG, University of Granada, Spain for experimental analysis. Two stage fortifications are provided using transformation and substitution. The results obtained for standard images are compared with existing techniques and high degree of security is achieved by this proposed technique.

关键词： Biometrics Ciphers Correlation Redundancy Mean square error methods Multimedia databases Encryption Information and communication technology Standards Logistics

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Slotted ALOHA with Code Combining for IoT Networks 3

Slotted ALOHA with Code Combining for IoT Networks

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Tyrovolas, Dimitrios Chrysologou, Athanasios Chondrogiannis, Georgios Tegos, Sotiris Mekikis, Prodromos-Vasileios Diamantoulakis, Panagiotis Ioannidis, Sotiris Liaskos, Christos Chatzidiamantis, Nestor Karagiannidis, George Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering Thessaloniki Greece Technical University of Chania Dept. of Electrical and Computer Engineering Chania Greece University of Ioannina Computer Science Engineering Department Ioannina Greece Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350333732

Internet of Things (IoT) networks are expected to be a key enabler technology for smart cities, due to their ability to offer real-time monitoring. In this work, we propose a novel medium access control (MAC) protocol, inspired by the synergy of slotted ALOHA and hybrid automatic repeat request with code combining (HARQ-CC), termed as code combining slotted ALOHA (CCSA), to improve the performance of IoT networks with multiple randomly deployed sensors. Specifically, we provide analytical closed-form expressions for the network's outage probability as well as for its average throughput when CCSA is applied. Finally, we provide simulation results that reveal the authenticity of the derived expressions and reveal the capabilities of HARQ-CC to significantly improve the network's performance when the sensors' access is based on a random access scheme. © 2023 IEEE.

关键词： Wireless sensor networks

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Prediction of Software Bug Fixing Time Using Intuitionistic Fuzzy Similarity Measure on Bug Informative Terms

Prediction of Software Bug Fixing Time Using Intuitionistic ...

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Information technology (OCIT), OITS International Conference on

作者： Rama Ranjan Panda Naresh Kumar Nagwani Department of Computer Science and Engineering Center for Cyber Security ITER SOA Deemed to be University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

Nowadays, industries are more concerned with the timely delivery of quality software. The release of software totally depends upon the time required to fix the software bugs. The project team has to estimate the average time required to fix the bugs in accordance with the project schedule and available resources. The severity and priority of the software bug play a significant role in estimating the average time required to fix the bugs. However, the presence of multiple classes of severity and priority poses a greater challenge for the project team in terms of bug fixing time estimation. To address the aforementioned problem in this paper, an intuitionistic fuzzy similarity (IFS) measure-based bug time estimation technique is developed using the priority and severity of the software bug. The informative terms of severity and priority are used to determine the IFS measure of a new bug to multiple classes of severity and priority. The average bug fixing time for each severity and priority groups is utilized to predict the bug fixing time of a newly reported bug. The proposed bug estimation techniques provide a better MAE, RMSE, and $R^{2}$ score over various software bug repositories compared to other bug fixing time estimation techniques.

关键词： Industries Software maintenance Schedules computer bugs Estimation Machine learning Software Time measurement Software measurement Information technology

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ML-assisted security for Anomaly Detection in Industrial IoT (IIoT) Applications 2

ML-assisted Security for Anomaly Detection in Industrial IoT...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Konatham, Bharath Reedy Simra, Tabassum Ghimire, Ashutosh Amsaad, Fathi Ibrahem, Mohamed I. Jhanjhi, N.Z. Department of Computer Science and Engineering Wright State University DaytonOH United States Department of Cyber Security Engineering George Mason University FairfaxVA United States School of Computer Science SCS Taylor’s University Subang Jaya47500 Malaysia

ISBN: (纸本)9798350305418

The industrial Internet of Things (IIoT) uses intelligent sensors and actuators, etc., to facilitate the application of IoT in different industries. This paper presents a comparative analysis using several machine learning (ML)-assisted deep learning techniques to develop efficient cybersecurity anomaly detection within IoT applications. To accomplish that, several deep learning techniques are investigated and analyzed, including Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), their hybrid (CNN+GRU), and Long Short-Term Memory (LSTM). The performance of the developed deep learning models is evaluated in terms of accuracy, precision, recall, F1-score, and False Alarm Rate (FAR). Our experimental results show that the developed hybrid CNN+GRU model outperforms the others, achieving an accuracy of 94.94%, a recall of 92.29%, a precision of 98.49%, an F1-score of 95.24%, and a low false alarm rate of 0.001. However, it is essential to note that the hybrid model requires a longer convergence time, indicating a trade-off between performance and computational efficiency. Notably, individual CNN and GRU models also showcase strong performance as time-efficient alternatives. In conclusion, our adopted comprehensive dataset and rigorous evaluation prove that we have developed practical deep-learning approaches to obtain an accurate measure for an efficient IIoT anomaly detection framework. © 2023 IEEE.

关键词： Anomaly Detection CNN GRU Industrial Internet of Things (IIoT) LSTM ML-assisted security

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Conformal Graph-level Out-of-distribution Detection with Adaptive Data Augmentation 25

Conformal Graph-level Out-of-distribution Detection with Ada...

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34th ACM Web Conference, WWW 2025

作者： Lin, Xixun Cao, Yanan Sun, Nan Zou, Lixin Zhou, Chuan Zhang, Peng Zhang, Shuai Zhang, Ge Wu, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Wuhan University Wuhan China Academy of Mathematics and Systems Science CAS School of Cyber Security University of Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Academy of Mathematics and Systems Science CAS Beijing China School of Computer Science and Technology Donghua University Shanghai China Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9798400712746

Graph-level out-of-distribution (OOD) detection, which attempts to identify OOD graphs originated from an unknown distribution, is a vital building block for safety-critical applications in Web and society. Current approaches concentrate on how to learn better graph representations, but fail to provide any statistically guarantee on detection results, therefore impeding their deployments in the scenario where detection errors would result in serious consequences. To overcome this critical issue, we propose the Conformal Graph-level Out-of-distribution Detection (CGOD), extending the theory of conformal prediction to graph-level OOD detection with a rigorous control over the false positive rate. In CGOD, we develop a new aggregated non-conformity score function based on the proposed adaptive data augmentation. Through the guidance from two designed metrics, i.e., score consistency and representation diversity, our augmentation strategy can generate multiple non-conformity scores, and aggregating these generated non-conformity scores together is robust to the misleading information. Meanwhile, our score function can perceive the subsequent process of conformal inference, enabling the aggregated non-conformity score to be adaptive to different input graphs and deriving a more accurate score estimation. We conduct experiments on multiple real-world datasets with different empirical settings. Extensive results and model analyses demonstrate the superior performance of our approach over several competitive baselines. © 2025 Copyright held by the owner/author(s).

关键词： Network theory (graphs)

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Adversarial Robustness in DeepFake Detection: Enhancing Model Resilience with Defensive Strategies

Adversarial Robustness in DeepFake Detection: Enhancing Mode...

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Intelligent cybernetics technology & Applications (ICICyTA), International Conference on

作者： Pallavi N Pallavi T P Sushma Bylaiah Goutam R Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka India Department of Computer Science and Engineering (Cyber Security) M S Ramaiah Institute of Technology Karnataka India Department of Computer Science and Engineering Atria Institute of Technology Karnataka India

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Detection of DeepFakes presents formidable challenges primarily due to adversarial attacks that can dramatically reduce model accuracy. Current detection models are reasonably effective in real scenarios; however, these detectors are vulnerable to adversarial attacks, resulting in misclassifications. The current study examines defensive approaches to strengthen the robustness of DeepFake detection approaches when facing adversarial attacks. Explored several alternatives, including adversarial training, input preprocessing, and model ensembles against a manipulated media dataset. The results demonstrate that the model has predicted image appropriately at average of 86.34% but after the adversarial attacks the prediction of the image was 99.8% inappropriate. although adversarial attacks greatly lower the effectiveness of a model, it is possible to improve the robustness of a model through defense methods, particularly adversarial training in combination with an ensemble method which predicted the image appropriately at an average of 52.06%. However, despite the improvement, the defense methods still fail to protect all adversarial examples. The results illustrate the importance of developing new methods of defense against DeepFake detection in light of future adjustments and variants of an attack.

关键词： Training Deep learning Deepfakes Predictive models Prediction algorithms Robustness Ensemble learning security Resilience Residual neural networks

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Transparency order versus confusion coefficient:a case study of NIST lightweight cryptography S‑Boxes

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cybersecurity 2021年第1期4卷 541-560页

作者： Huizhong Li Guang Yang Jingdian Ming Yongbin Zhou Chengbin Jin Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China School of Cyber Security Nanjing University of Science and TechnologyNanjing 210094China Department of Software Engineering and Software Evaluation Beijing Institute of Computer Technology and ApplicationBeijing 100039China

Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic *** most cases,non-linear components(e.g.S-Boxes)of cryptographic algorithms will be chosen as primary targets of side-channel attacks(SCAs).In order to measure side-channel resistance of S-Boxes,three theoretical metrics are proposed and they are reVisited transparency order(VTO),confusion coefficients variance(CCV),and minimum confusion coefficient(MCC),***,the practical effectiveness of these metrics remains still *** the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples,this paper takes a comprehensive study of the applicability of these *** of all,we empirically investigate the relations among three metrics for targeted S-boxes,and find that CCV is almost linearly correlated with VTO,while MCC is inconsistent with the other ***,in order to verify which metric is more effective in which scenarios,we perform simulated and practical experiments on nine 4-bit S-Boxes under the nonprofiled attacks and profiled attacks,*** experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks,VTO and CCV are more reliable while MCC *** also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled ***,we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different ***,all of them are invalid in this scenario.

关键词： Side-channel attacks NIST lightweight cryptography S-Boxes Transparency order Confusion coefficient

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Decentralization of Identities Using Blockchain 10

Decentralization of Identities Using Blockchain

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Srivastava, Shreshth Grover, Veena Nallakaruppan, M.K. Krishwanth, B. Saravanan, K. Customer Engineering Qualcomm Hyderabad India School of Management Studies Noida Institute of Engineering and Technology Delhi NCR Noida India School of Computer Science Engineering and Information Systems Vit University Vellore India Cyber Security Engineer Datafoundry Private Limited India Kpr Institute of Engineering and Technology Department of Mechatronics Engineering Coimbatore India

ISBN: (纸本)9798350382471

Digital Identity Management has become an essential component of federal business units today. It involves the gathering and storing of an individual's personal information entrusted to identity providers. With an increase in the number of data breaches occurring every passing year, vulnerabilities posed by technology conglomerates and social identity providers' access to personal data represent a severe cause of alarm. Blockchain technology, with its inherent ability to prevent manipulation and unauthorized alteration of recorded information, represents an excellent solution to identity ownership concerns. This paper seeks to provide an in-depth exploration of digital identities and the use of Blockchain technology to create a completely decentralized system of managing identities. © 2023 IEEE.

关键词： - Digital Identity Blockchain Decentralization Identity Providers Social Identity

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A Linked List Encryption Scheme for Image Steganography without Embedding

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computer Modeling in engineering & Sciences 2024年第10期141卷 331-352页

作者： Pengbiao Zhao Qi Zhong Jingxue Chen Xiaopei Wang Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information *** enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover *** this paper,a novel encrypted communication scheme for image SWE is *** reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer *** employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be *** receiver can restore the secretmessage fromthe received image using only the list header position *** scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover *** boasts high concealment and security,along with a complete message restoration rate,making it resistant to ***,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of *** validate the proposed framework,practical tests and comparisons are conducted using multiple *** results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

关键词： Steganography encryption steganography without embedding coverless steganography

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A Logical Evaluation of Error Free Data Communication Scheme based on High Speed Visible Light Communication (VLC) Methodology 3

A Logical Evaluation of Error Free Data Communication Scheme...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bharathi, P. Shyamala Pradeep, D. Judgi, T. Arulananth, T.S. Giri, Jayant Odeh, Mahmoud Department of Ece Chennai India M.Kumarasamy College of Engineering Department of Cse Tamilnadu Karur639113 India Sathyabama Institiute of Science and Technology Department of Ece Chennai India Mlr Institute of Technology Department of Ece Telangana Hyderabad500043 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

Visible Light Communication (VLC) is an emerging technology that offers a viable alternative to traditional RF-based communication systems. This paper presents a high-speed, error-free data communication scheme based on VLC technology using high-brightness LEDs and photodiodes. The proposed system modulates light intensity to encode and transmit data, achieving data rates up to 1000 Mbps with minimal errors. Experimental results demonstrate that the system maintains an exceptionally low error rate of less than 0.01% at a distance of 1 meter and light intensity of 1000 Lux. Additionally, the system exhibits low latency, making it suitable for real-time applications like video streaming and remote monitoring. The system's power consumption is optimized, with energy usage remaining below 6 watts even at maximum transmission speeds. Overall, the results indicate that VLC can achieve high levels of accuracy and efficiency, making it an ideal choice for environments where RF communication is restricted or undesirable. The system's performance metrics, including speed, error rate, latency, and power consumption, highlight its potential for applications requiring secure and efficient wireless data transmission. © 2025 IEEE.

关键词： Visible light communication

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