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9th International Conference on engineering and Emerging technology, ICEET 2023

作者： Asghar, Laiba Ahmed, Fawad Khan, Muhammad Shahbaz Arshad, Arshad Ahmad, Jawad Pakistan Navy Engineering College National University of Sciences and Technology Department of Cyber Security Karachi Pakistan School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798350316926

To secure the digital images over insecure transmission channels, a new image encryption algorithm Noise-Crypt is proposed in this paper. Noise-Crypt integrates nonlinear random noise, hybrid chaotic maps, and SHA-256 hashing algorithm. The utilized hybrid chaotic maps are the logistic-tent and the logistic-sine-cosine map. The hybrid chaotic maps enhance the pseudorandom sequence generation and selection of substitution boxes, while the logistic-sine-cosine map induces non-linearity in the algorithm through random noise. This deliberate inclusion of noise contributes to increased resistance against cryptanalysis. The proposed scheme has been evaluated for several security parameters, such as differential attacks, entropy, correlation, etc. Extensive evaluation demonstrates the efficacy of the proposed scheme, with almost ideal values of entropy of 7.99 and correlation of -0.0040. Results of the security analysis validate the potency of the proposed scheme in achieving robust image encryption. © 2023 IEEE.

关键词： Entropy

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Blockchain technology in IoT systems: current trends, methodology, problems, applications, and future directions

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Journal of Reliable Intelligent Environments 2021年第2期7卷 115-143页

作者： Alfa, Abraham Ayegba Alhassan, John Kolo Olaniyi, Olayemi Mikail Olalere, Morufu Department of Computer Science Kogi State College of Education Ankpa Nigeria Department of Computer Science Federal University of Technology Minna Nigeria Department of Computer Engineering Federal University of Technology Minna Nigeria Department of Cyber Security Science Federal University of Technology Minna Nigeria

The growth of Internet of Things (IoT) took center stage universally due their capability to advance the course of human lives. Consequent upon this, several challenges were thrown up such as security of huge data stored and transmitted through network communication channels. IoT insecurity is partly due to centralization architecture, low computational strength, resource-constrained devices, variation in standards and protocols of communication. From this Systematic Literature Review (SLR), the 85 articles reviewed showed that privacy and security solutions are still being proposed or at conceptual levels, though a number of researchers favored the integration of Blockchain technology, cryptographic and hashing schemes into IoT. The Blockchain technology in IoT systems remains an open area of interest for top researchers especially in evolving frameworks to fit into the centralized architecture, functionality, and scalability demands of conventional IoT systems. In this article, we investigate security and privacy concerns of IoT from the lens of current trends, pertinent challenges, security methodologies, applications, and gaps for future research directions. Most specifically, there is prospect of utilizing cryptographic and hashing schemes offered by Blockchain technology in IoT. Then, high performance and scalable cryptographic schemes (that is, those in the class of lightweight appraoch) are suggested to deal with privacy and security of data in Blockchain-based IoT system. More importantly, this study provides basis for evolving secure and decentralized applications and systems in several domains such as smart farming. © 2020, Springer Nature Switzerland AG.

关键词： Blockchain

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S3A: State-Attention Inducing Adversarial Attacks on Black-box Proximal Policy Optimisation Models

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IEEE Internet of Things Journal 2025年

作者： Wang, Yichuan Zhao, Chen Liu, Zhiquan Hei, Xinhong Ma, Jianfeng Xi’an University of Technology College of Computer Science and Engineering Xi’an710048 China Jinan University College of Cyber Security Guangzhou510632 China Xidian University School of Cyber Engineering Xi’an710071 China

In the current era when the Internet of Things (IoT) is booming and various smart devices are closely interconnected to build a complex network system, the demand for efficient control and resource management strategies is extremely urgent. Proximal Policy Optimization (PPO), as a highly representative algorithm in deep reinforcement learning, has great potential in aspects such as precise control of IoT devices, rational resource allocation, and intelligent interaction. However, in the practical applications of the IoT, PPO mostly exists as a black-box model, which is vulnerable to adversarial attacks. Moreover, the continuous action space scenarios applicable to PPO further increase the difficulty of analysis and protection. Therefore, this paper innovatively proposes a State-Attention Adversarial Attack (S3A) for black-box PPO models in continuous action space scenarios. This method is based on the model’s attention to states, has a low cost, integrates the parts of model extraction, action clustering, and decision-making interference of the model under specific states, and has a clear interference purpose. By constructing an IoT-related victim model in the MuJoCo environment provided by the Gym library and implementing adversarial attacks, experiments have found that under four repetitions in five simulation environments, S3A reduces the final reward of the victim model by an average of 48.5%, which strongly verifies the effectiveness and influence of this attack method. © 2014 IEEE.

关键词： Adversarial machine learning

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Detection Technique of Software-Induced Rowhammer Attacks

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computers, Materials & Continua 2021年第4期67卷 349-367页

作者： Minkyung Lee Jin Kwak ISAA Lab. Department of Computer EngineeringAjou UniversitySuwon16499Korea Department of Cyber Security Ajou UniversitySuwon16499Korea

Side-channel attacks have recently progressed into software-induced *** particular,a rowhammer attack,which exploits the characteristics of dynamic random access memory(DRAM),can quickly and continuously access the cells as the cell density of DRAM increases,thereby generating a disturbance error affecting the neighboring cells,resulting in bit *** a rowhammer attack is a highly sophisticated attack in which disturbance errors are deliberately generated into data bits,it has been reported that it can be exploited on various platforms such as mobile devices,web browsers,and virtual ***,there have been studies on bypassing the defense measures of DRAM manufacturers and the like to respond to rowhammer attacks.A rowhammer attack can control user access and compromise the integrity of sensitive data with attacks such as a privilege escalation and an alteration of the encryption *** an attempt to mitigate a rowhammer attack,various hardware-and software-based mitigation techniques are being studied,but there are limitations in that the research methods do not detect the rowhammer attack in advance,causing overhead or degradation of the system ***,in this study,a rowhammer attack detection technique is proposed by extracting common features of rowhammer attack files through a static analysis of rowhammer attack codes.

关键词： Rowhammer attack static analysis detecting technique side-channel attack bit flip

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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An Adaptive Threat Defence Mechanism Through Self Defending Network to Prevent Hijacking in WiFi Network

An Adaptive Threat Defence Mechanism Through Self Defending ...

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Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), International Conference on

作者： Datta Sai Manikanta Narayana Sai Bharadwaj Nookala Shraddha Chopra Udhayakumar Shanmugam Department of Computer Science and Engineering (Cyber Security) School of Computing Chennai

IEEE 802.11 generally known as the Wi-Fi network, is our backbone of the present communication system delivering the best effort service to the end users. Access control in wireless network is an intrinsic security problem which creates possibilities for malevolent manipulation by third parties. Hackers make the use of free software to easily execute wireless attacks to create denial of service (DOS) and man in the middle attack and thereby the existing defense becomes passive for new types of network attacks. The paper proposes a self-defending network that can quickly identify the attackers, prevent them from doing further damage and then get itself adapt to the threat behavior. To implement the mechanism a pre-connection attack is performed by targeting a network and analyzing the packets. The proposed work demonstrates how a hacker gains access to a Wi-Fi network by cracking different types of encryptions like Wired Equivalent Privacy (WEP), Wireless Protected Access (WPA)/WPA2 using a simple wireless network adapter. Our proposed adaptive threat defense model identifies the hacker and creates a diverse strategy for self-defense. The proposed model would be effective against dynamic network configuration like Wi-Fi and further prevents hijacking.

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Bl-IEA: A Bit-Level Image Encryption Algorithm using Transformation and Chaotic Skew Tent Map based Substitution for Fingerprint Images

Bl-IEA: A Bit-Level Image Encryption Algorithm using Transfo...

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

作者： Aditya G Kulkarni Prajwalasimha S N Naveen Kulkarni Deepthika Karuppusamy Raveena Dharmappa Yash Bagga 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 order to fortify pre-stored biometric data of fingerprint samples in the biometric authentication system, a hybrid approach based on Pseudo Hadamard transformation and Skew Tent chaotic 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 Skew Tent map to generate random numbers. Fingerprint images are considered from FVC2004. Encrypted/Decrypted images are considered for experimental analysis. Two stage fortifications are provided using transformation and substitution. The results obtained for standard images are compared to existing techniques and high degree of security is achieved by this proposed technique.

关键词： Correlation Chaotic communication Image matching Redundancy Fingerprint recognition Matrices Encryption Information and communication technology Standards Random sequences

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Taking LM as the Brain: A Novel Approach Integrating Language Model and Generative Agent for Intelligent Decision Systems

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International Journal of Network security 2024年第3期26卷 386-393页

作者： Yang, Jingkang Cai, Xiaodong Liu, Yining Chen, Mingyao Chang, Chin-Chen School of Information and Communication Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Research and Development Department Guilin Topintelligent Communication Technology Company Limited Guilin541004 China Department of Information Engineering and Computer Science Feng Chia University Taichung 407102 Taiwan

The Large Language Model (LLM) has demonstrated significant capabilities in intelligent robotics and Autonomous Driving(AD). Compared to traditional end-to-end models, decision reasoning in the form of language exhibits enhanced generalization and interpretability. To harness the inferential decision-making capabilities of large models more effectively, we propose a generative intelligent agent capable of translating various sensor signals into language descriptions, thereby utilizing Language Models (LMs) for decision-making. Furthermore, to address the practical deployment challenges of large models, we fine-tuned a GPT-2 model on an intelligent traffic corpus. The fine-tuned model performs competitively with LLMs in the domain of traffic decision-making. This approach can potentially deploy LMs directly on computationally constrained in-vehicle platforms to reduce communication latency. We evaluated the proposed method on a simulated dataset, and the results indicate that the pure language decision model surpasses the vector-based and fusion models. Moreover, our method exhibits significantly higher efficiency than LLMs. © (2024), (International Journal of Network security). All rights reserved.

关键词： Decision making

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Utilize Multichannel Attention Amplification Fusion for Skin Disease Diagnosis 5

Utilize Multichannel Attention Amplification Fusion for Skin...

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5th International Conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Tang, Jiawei Zheng, Fengbo Liang, Gongbo Jiang, Lifen College of Computer and Information Engineering Tianjin Normal University Tianjin China Department of Computing & Cyber Security Texas A&M University-San Antonio TX78224 United States

ISBN: (纸本)9781510662520

Patients with early-stage skin cancer have a 99% survival rate, but once it spreads, the chances of survival are poor. Deep learning has greatly improved the quality of computer-aided diagnosis of skin diseases. However, the majority of existing methods do not take into account the influence of the extracted features on the classification results. In this paper, we proposed a deep neural network with multichannel attention amplification fusion (MAF) module for skin diseases classification. First, the images go through our specially designed pre-processing process which retains more basic image information. The features extracted by the deep neural network are fed into MAF which is designed to amplify the role of key feature points in the classification to improve the effectiveness of classification. Our model achieved an accuracy of 88.13% and 82.83% on the ISIC2016 dataset and ISIC2017 dataset, respectively. We also performed ablation studies which showed that our proposed pre-processing method and the MAF module effectively improve the expressiveness of the model. © 2023 SPIE.

关键词： computer aided diagnosis

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Blockchain-Powered Authentication and Supply Chain Integration to Combat Counterfeit Drugs

Blockchain-Powered Authentication and Supply Chain Integrati...

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International Conference on Circuits, Communication, Control and Computing

作者： Anu S M Mohana Kumar S Shankarmma M Chaithra Meghana K Sanvi H R Department of CSE Cyber Security Ramaiah Institute of Technology Bangalore India Department of Computer Science Ramaiah Institute of Technology Bangalore Bangalore India Dept.of Computer Science (Cyber Security) Bangalore India Dept.of Computer Science (Cyber Security) Ramaiah Institute of Technology Bangalore India Dept.of CS (AI and ML) Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Counterfeit drugs pose a significant threat to public health and undermine the integrity of pharmaceutical supply chains globally. This research explores a novel application of blockchain technology to authenticate pharmaceutical products and enhance transparency for end users. Although blockchain has been utilized in pharmaceutical supply chains, our study introduces an innovative approach focusing on blockchain-powered authentication mechanisms, such as unique identifiers and cryptographic verification. These mechanisms enable end users to verify the product authenticity of the drug by entering provided identifiers on the company's application. If the identifier fails to display the expected information, it indicates that the product may be counterfeit. Moreover, users can access comprehensive details about the product's journey, including manufacturing, storage conditions, and distribution, promoting transparency and trust. The displayed information is securely extracted from the blockchain-driven supply chain, ensuring data integrity and authenticity, while confidential data remains protected. Through an in-depth analysis of literature, case studies, and implementation strategies, this research demonstrates the potential of blockchain-driven solutions to mitigate counterfeit drug risks. The findings highlight the importance of stakeholder collaboration in leveraging blockchain technology for authentication and transparency, ultimately safeguarding public health and building trust with end users.

关键词： Drugs Data integrity Supply chains Authentication Focusing Blockchains Manufacturing Stakeholders Data mining Public healthcare

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