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Seagull-optimized Deep Convolutional Neural Network for Detecting distributed Denial of Service Attack in Cloud Computing

Seagull-optimized Deep Convolutional Neural Network for Dete...

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Self Sustainable Artificial Intelligence systems (ICSSAS), international conference on

作者： Yogesh B. Sanap Pushpalata G. Aher Computer Science and Engineering Sandip University Nashik Sandip University Nashik Maharashtra

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Due to the enormous benefits and applications of cloud computing, there is a lot of interest from researchers on cloud computing. The most significant security risk associated with cloud computing is insider distributed denial of service (DDoS), which affects the entire deactivation of services due to its dispersed architecture and utter dependency on the internet for service supply. Machine learning and Deep learning have appeared as a potent tool in cyber security recently, and numerous attack detection methods have been put out in recent studies. Nevertheless, existing studies on attack detection encounter several challenges, including data imbalance, interpretability issues, inaccurate detection, overfitting, and time complexity issues. Therefore, in order to overcome these difficulties, a deep learning model, Seagull optimized Synthetic Minority Over-sampling Technique enabled Deep Convolutional Neural Network (SgO-SCNN) is developed in this research to detect DDoS attacks. The optimized SMOTE employed in the model addresses the imbalanced data issue and improves the model’s performance by effectively rebalancing the data. The DCNN classifier can detect the attacks more efficiently and reduces the computation of the model. The seagull optimization tunes the DCNN classifier’s parameters and improves the model’s performance in attack detection with fast convergence, which reduces the model’s time complexity. The experimental outcomes indicate that the developed $\mathrm{SgO}-\mathrm{SCNN}$ model outperforms the existing approaches by attaining enhanced values of accuracy 96.05%, F1-score 94.66%, precision 94.84%, and recall 94.48%.

关键词： Cloud computing Accuracy Computational modeling Denial-of-service attack Data models Convolutional neural networks computer crime Time complexity Optimization Overfitting

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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international conference on Parallel and distributed systems (ICPADS)

作者： Ran Qiu Hu Liang Yuchen Liu Shengrong Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures.

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E-Voting System Using Blockchain Technology 4

E-Voting System Using Blockchain Technology

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Nagesh, H.R. Guru Prasad, M.S. Shivaraj, B.G. Jain, Dhawal Puneeth, B.R. Anadkumar, M. Sahyadri College of Engg. & Management Dept. of Computer Science & Engg. Mangaluru India Dept. of Computer Science & Engg. Dehradun India Mangalore Institute of Technology & Engg. Dept. of Computer Science & Engg. Moodabidri India Sdm Institute of Technology Dept. of Computer Science & Engg. Ujire India Department of Mca Nitte Karnataka India Dept. of Computer Applications Dehradun India

ISBN: (纸本)9781665474368

In every nation, democratic elections are a momentous and weighty occurrence, and the voting system that is now in place requires the use of ballots or electronic voting machines (EVM). Transparency, poor turnout, vote manipulation, distrust of electoral organizations, fabrication of unique IDs (voting party IDs), and delays in posting results are some of the issues that arise as a result of these procedures. The matter of safety is of the utmost importance. When considering the installation of a computerized voting system, voter confidentiality has always been one of the most important concerns. There is no question regarding the system's capability to secure itself in contrast to prospective assaults and safeguard data in the face of such big choices. Utilization of blockchain technology is one approach that might be taken to resolve security concerns. The blockchain technology has an endless number of different uses that might be implemented. The technology known as blockchain is a distributed ledger that makes it possible for peer-to-peer networks all over the world to handle digital assets. In this context, distributed ledger technology represents an intriguing development. A grouping of all transactions is referred to as a block. Immutability, decentralisation, security, transparency, and anonymity are some of the outstanding properties offered by blockchain technology. The combination of blockchain technology with smart contracts has shown promise as a viable option for the development of trustworthy and open-source electronic voting systems. In this article, we demonstrate how to use blockchain technology with the help of a wallet and the Solidity programming language to build an electronic voting application. The programme was designed as a smart contract for the Ethereum network. In order to avoid having the same person vote twice, the user's wallet will only hold a certain number of tokens (gas), which will be depleted each time the user casts a vote. Thi

关键词： Blockchain

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Role of Neural Network, Fuzzy, and IoT in Integrating Artificial Intelligence as a Cyber security System

Role of Neural Network, Fuzzy, and IoT in Integrating Artifi...

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Electronics and Renewable systems (ICEARS), international conference on

作者： Papri Das Manikumari Illa Rajesh Pokhariyal Akhilesh Latoria Hemlata DilipKumar Jang Bahadur Saini School of Information Technology AURO University Surat Gujarat Artificial Intelligence & Data Science (AI&DS) Vignan's Institute Of Information Technology Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Central University of Haryana Mahendergarh Haryana Department of Computer Science & Engineering Sawmi Rama Himalayan University Dehradun India

The "Internet of Things" has a vast number interconnected devices. These interconnected devices collect vital data that may have a significant effect on the company, society, and the environment as a whole. IoT application has grown significantly in recent times, and with it, so do worries about cybersecurity. Artificial intelligence (AI) is at the forefront of the technology of cybersecurity and is employed to create intricate algorithms to safeguard systems and networks like IoT devices. But cybercriminals have learned how to take advantage of AI, and they have even started to deploy AI in analyzing cyberattacks. Due to the limited computing power and memory capacities of IoT systems, conventional high-end cybersecurity measures are inadequate to protect an IoT system. The need for accessible, distributed, and robust smart security systems is highlighted by this. Large- and small-scale heterogeneous datasets are no match for DL. In this research, a multilayer cybersecurity strategy based on DL is used to safeguard the TL of IoT systems. The developed framework tests the proposed multi-layer strategy using the intrusion identification statistics obtained from CIC-IDS (2018), ToN, and BoT-IoT. Consequently, depending on the analyzed parameters, the proposed model has outperformed the other approaches and achieved 98% accuracy.

关键词： Training Renewable energy sources Artificial neural networks Nonhomogeneous media Solids Internet of Things Artificial intelligence

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Blockchain Based security Enabled Smart Contract for Energy Management

Blockchain Based Security Enabled Smart Contract for Energy ...

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security of Information and networks (SIN), international conference on

作者： Aditi Sharma Mukta Patel Arjun Choudhary Gajender Kumar Department of Computer Science and Engineering Symbiosis Institute of Engineering and Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Parul Institute of Technology Parul University Vadodara Gujarat India Center for Cyber Security Security and Criminal Justice Sardar Patel University of Police Jodhpur India Department of Computer Science and Engineering SRM Institute of Science and Technology (SRMIST) SRM University Delhi-NCR Campus India

Energy consumption is highly demanded area for major security concerns due to the high eye from attackers. In 2020, the “Recorded future” released report for massive power outage in India[1]. With the increasing adoption of renewable power sector and increasing demand for efficient energy management, the need for secure and transparent solutions in the energy sector has become paramount. This research introduces an innovative solution to tackle these difficulties by creating a Blockchain-Based security Enabled Smart Contract (BSESC) system for energy management. The BSESC system uses blockchain technology to provide a secure and decentralised platform for the management of energy resources. It guarantees the honesty and openness of energy transactions while reducing the hazards linked to centralised energy management systems. Smart contracts, implemented on the blockchain, provide automatic and tamper-resistant execution of energy agreements among producers, consumers, and grid operators. The BSESC system offers real-time monitoring and management of energy production, distribution, and consumption as its main characteristics. This research paper focuses on the design and management of spread power production with customers in the domestic, corporate, and manufacturing sectors. The energy demand side management is initially proposed using a hierarchical structure based on P2P information and energy exchange in a dynamic economy. The smooth and efficient trading system is created using smart contracts supported by blockchain. The advantages of distributed energy management are discussed, including cost savings, reduced peak demand, and better market efficiency, all of which are facilitated by blockchain.

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From Attacks to Insights: XAI and Defense in Network security

From Attacks to Insights: XAI and Defense in Network Securit...

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Sustainable Communication networks and Application (ICSCNA), international conference on

作者： Bharathi Ganesh Kumar E. Shanthini Department of Computer Science and Engineering PSG College of Technology Tamil Nadu India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Network intrusion poses significant threats to cyber security, as malicious attacks can disrupt systems and compromise sensitive information. Real-world scenarios include unauthorized access to corporate networks, data breaches exposing sensitive customer information, and distributed Denial of Service (DDoS) attacks that can impair critical infrastructure. This paper addresses these challenges using the UNSW-NB15 dataset, a comprehensive collection of real-world network traffic data. This dataset was chosen for its diversity in capturing contemporary cyber threats, making it ideal for evaluating model performance in detecting intrusions. While numerous classification models exist for this task, the Multi-Layer Perceptron (MLP) classifier is selected due to its ability to capture complex patterns and provide higher accuracy in non-linear datasets. To enhance the interpretability of the model, Explainable AI (XAI) techniques are employed, including SHAP, LIME, and permutation feature importance, which offer insights into model decisions by explaining feature contributions. However, adversarial machine learning introduces an additional layer of threat by exploiting model vulnerabilities. This paper focuses on the Zeroth Order Optimization (ZOO) attack, a black-box method that only requires access to the model's predictions, without needing knowledge of the architecture or gradients, to demonstrate how adversarial examples can drastically reduce model accuracy and compromise network security. Upon generating adversarial examples, a significant drop in accuracy is observed, identifying the features most susceptible to manipulation. Then XAI methods are applied again to analyze the features that contribute most to the misclassification, allowing us to understand the underlying vulnerabilities. Based on these insights, adversarial training is implemented as a defense mechanism, successfully restoring model accuracy and strengthening resilience against attacks. The results

关键词： Training Accuracy Explainable AI Network intrusion detection Telecommunication traffic Predictive models Network security Traffic control computer crime Optimization

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Deep Neural Network Model for Improved DDoS Attack Detection in Cloud Environments

Deep Neural Network Model for Improved DDoS Attack Detection...

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Emerging Technology (INCET), international conference for

作者： Rashmi Verma Manisha Jailia Munish Kumar Bhawna Kaliraman Department of Computer Science and Engineering Banasthali Vidyapith Tonk Rajasthan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

As the prevalence of distributed Denial of Service (DDoS) attacks continues to escalate, safeguarding cloud environments against these threats becomes paramount. This paper introduces a Deep Neural Network (DNN) model designed to enhance the accuracy and efficiency of DDoS attack detection in cloud environments. Leveraging the inherent capabilities of deep learning, the proposed model exhibits improved performance on the widely recognized NSL-KDD dataset. The research findings demonstrate a substantial increase in accuracy, underscoring the efficacy of the DNN model in fortifying the security posture of cloud infrastructures. The escalating frequency and sophistication of distributed Denial of Service (DDoS) attacks pose a substantial threat to the security of cloud environments. In response to this pressing concern, this paper introduces a Deep Neural Network (DNN) model engineered to significantly enhance the accuracy and efficiency of DDoS attack detection in cloud infrastructures. By harnessing the inherent capabilities of deep learning, the proposed model represents a breakthrough in fortifying the security posture of cloud systems. This research employs the widely recognized NSL-KDD dataset, a comprehensive resource for Intrusion Detection System (IDS) evaluation, to evaluate the model's performance. The proposed DNN model transcends conventional methods by autonomously learning intricate patterns within network traffic data, adapting to the evolving landscape of DDoS attacks. The literature survey delves into the vulnerabilities associated with DDoS attacks in cloud environments, emphasizing the need for innovative detection mechanisms. Previous research has underscored the effectiveness of deep learning, particularly DNNs, in addressing complex cybersecurity challenges, positioning them as ideal candidates for enhancing threat detection capabilities.

关键词： Deep learning Adaptation models Accuracy Artificial neural networks Pressing Telecommunication traffic Denial-of-service attack

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Trust-based Approach to Secure Low-Power and Lossy networks Routing Protocol 5

Trust-based Approach to Secure Low-Power and Lossy Networks ...

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5th IEEE international conference on Networking and Advanced systems, ICNAS 2021

作者： Abid, Meriem Bahloul, Sarah Nait Hamlili, Sara University of Mostaganem Dept. of Mathematics and Computer Science Algeria University of USTO-MB Dept. of Computer Science Oran Algeria

ISBN: (纸本)9781665428217

Resource-constrained things can now connect to the Internet through Wireless Personal Area networks (WPAN) and Low Power Wide Area networks (LPWAN). Since then, they are vulnerable to both attacks from inside the network and from the Internet as well. Many research has already investigated the security threats of such an environment. In this article, we focus on how securing the RPL routing protocol in such a constrained and vulnerable *** tackle these issues, we investigate how trust that is inspired by social human behavior can enhance RPL security. Our trust-based solution enables motes evaluating locally the threat level when interacting with their neighbors. However, evaluating the trust in such environment is a challenge due to various factors to take into account and the motes resource *** this end, we propose a distributed trust management approach based on machine learning to evaluate trust and detect untrustworthy behaviors. Machine learning-based approach have been widely investigated in IoT and *** evaluate the performance through significant metrics so as to obtain a good assessment of the effectiveness of our method. © 2021 IEEE.

关键词： Routing protocols

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DDoS Detection in SDN using Deep Learning

DDoS Detection in SDN using Deep Learning

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international conference on Internet of Things and Applications (IoT)

作者： Zahra Fatehi Ahmadreza Montazerolghaem Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (数字)9798350374674

ISBN: (纸本)9798350374681

In this paper, we present a deep learning approach for the detection of distributed Denial of Service (DDoS) attacks within Software-Defined Networking (SDN) environments. The escalating threat of DDoS attacks poses significant challenges to SDN security, necessitating innovative detection methods. Our approach leverages a Multi-Layer Perceptron (MLP) model trained on a comprehensive SDN traffic dataset, exhibiting enhanced accuracy and efficiency compared to traditional machine learning algorithms. Integration with the Ryu controller facilitates real-time DDoS attack detection in live SDN environments, showcasing the practical applicability of deep learning in enhancing network security. We emphasize the creation of a robust SDN traffic dataset that enables rigorous evaluation and comparison of detection techniques, addressing a critical gap in current research. Through advancements in deep learning, our study underscores the importance of developing sophisticated security mechanisms to safeguard SDN architectures against evolving cyber threats. The effectiveness of our proposed method signifies a substantial contribution to the field, promoting the integrity and availability of network resources amidst increasing DDoS vulnerabilities. Our work not only highlights the technical prowess of deep learning in SDN security but also underscores the imperative for ongoing research to refine and optimize detection methods. By addressing key limitations and exploring hybrid approaches, we aim to fortify SDN networks against malicious activities, paving the way for robust and adaptive security solutions in dynamic network environments.

关键词： Deep learning Machine learning algorithms Adaptive systems Smart cities Network security Denial-of-service attack Real-time systems

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Cross-chain Transaction Validation using Lock-and-Key Method for Multi-System Blockchain

Cross-chain Transaction Validation using Lock-and-Key Method...

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IEEE international conference on Communications Workshops, ICC

作者： Gaurav Kumar Samarthi Lahiri Amit Dua Gagangeet Singh Aujla Computer Science and Information Systems BITS Pilani Pilani India Department of Computer Science Durham University UK

Blockchains have profoundly impacted finance and administration, but there are several issues with the current blockchain platforms, including a lack of system interoperability. Currently used blockchain application platforms only work within their networks. Although the underlying concept of all blockchain networks is mainly similar, it involves centralised third-party mediators to transact from other blockchain networks. The current third-party intermediates establish security and trust by keeping track of “account balances” and attesting to the validity of transactions in a centralised ledger. The lack of sufficient inter-blockchain connectivity hinders the mainstream adoption of blockchain. Blockchain technology may be a solid solution for many systems if it grows and works with other systems. For the multi-system blockchain concept to materialise, a mechanism that would connect and communicate with the blockchain systems of various entities in a distributed manner (without any intermediary) while maintaining the property of trust and integrity established by individual blockchains is required. Several methods for verifying cross-chain transactions have been explored in this paper among various blockchains. The efficient verification of cross-chain transactions faces many difficulties, and current research has yet to scratch the surface. In addition to summarising and categorising these strategies, the report also suggests a novel mechanism that gets beyond the existing drawbacks.

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