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GAN-Based Anomaly Intrusion Detection for Industrial Controller System 1

World Conference on Information Systems for Business Management, ISBM 2023

作者： Riya V. Raj Sangeetha, V. Amritha, P.P. TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Information Systems BITS Pilani Pilani India

ISBN: (数字)9789819983469

ISBN: (纸本)9789819983452

Industrial controller system (ICS) is becoming more and more important in daily lives. In recent years, ICS has become more frequent targets of cyberattacks. In addition to the system, the environment is also significantly impacted by the ICS cyberattack. The main aim of ICS intrusion detection is a process of anomaly detection because cyberthreats cause anomalies to occur in the ICS and components under its control. With a machine learning or deep learning aid, the IDS can produce precise detection outcomes. Though machine learning models can be used to detect cyberattacks, there is a challenge in handling imbalanced real-time data. In this paper, we have implemented generative adversarial networks, to resolve the issue of imbalance in datasets by creating class-specific adversarial samples and further detecting anomalies with greater efficiency. This proposed method is tested on Secure Water Treatment Dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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CNN-BO-LSTM: an ensemble framework for prognosis of liver cancer

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1103-1109页

作者： K N, Sunil Kumar Kashyap, Pavan P. Bhyratae, Darshan A. Bhyratae, Suhas A. Kalaivani, A. Department of ISE Sri Venkateshwara College of Engineering Bangalore India Department of Electronics & amp Communication Engineering Sri Venkateshwara College of Engineering Bangalore India Computer Science and Engineering Sahyadri College of Engineering and Management Mangaluru India Department of MCA New Horizon College of Engineering Marathahalli Bangalore India Department of CSE-Cyber Security Sri Venkateshwara College of Engineering Bangalore India

computer-assisted diagnosis (CAD) is preferred for cancer identification across the globe, which relies on computerized image processing. The creation of previous CAD instruments involved a semi-automated approach that employed traditional deep learning techniques. Such techniques are not well versed with CAD instruments in terms of accuracy. Therefore, the given manuscript presents a convolutional neural network normalized architecture embedded with Bayesian optimization and long short-term memory (CNN-BO-LSTM) for the identification of liver cancer. Early pre-processing is done on the input magnetic resonance imaging (MRI) scan images to improve clarity. Next, we used dynamic binary classification to apply the accurate and region of interest (ROI) extracting approach. It is followed by automatic retrieval of CNN-based appearances from the ROI approach. For classification purposes, LSTM is used, which categorizes the images as benign or malignant. The proposed design’s testing outcomes, which combine characteristics with CNN-based ROI extraction and LSTM classification, surpassed the current state-of-the-art techniques. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： computer aided diagnosis (CAD) Convolutional neural network (CNN) Features extraction and bayesian optimization Long short term memory (LSTM)

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Blockchain-Based Cognitive Computing Model for Data security on a Cloud Platform

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computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized Data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

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security and Privacy Preservation for Future Vehicular Transportation Systems: A Survey 12

Security and Privacy Preservation for Future Vehicular Trans...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Almarshoodi, Abdulazeiz Keenan, James Campbell, Ivan Hassan, Tashfin Ibrahem, Mohamed I. Fouda, Mostafa M. George Mason University Department of Cyber Security Engineering FairfaxVA United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9781665462617

Electric Vehicles (EVs) with numerous optimized features are becoming more popular in today's world of vehicle technology. This is especially in these days when climate change is becoming more of a concern, and the usage of renewable energy is promoted. These EVs require frequent charging for daily use, and the usage of different charging stations would store sensitive data about EVs, e.g., location, driver's license, etc. This leads to significant privacy and security concerns. In this paper, we will investigate the existing solutions proposed in the literature to address these concerns. Some of these solutions are implementing a framework that depends on cryptography and the Matching Market, where cryptography plays a vital role in securing user information privacy. Other solutions include implementing a secure and privacy-preserving physical layer-assisted scheme for charging stations to improve authentication and preserve user privacy. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance the security level for future electric vehicle transportation systems. © 2023 IEEE.

关键词： Electric vehicles

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Intermittent Fault Diagnosis of Split-Star Networks and its Applications

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IEEE Transactions on Parallel and Distributed Systems 2023年第4期34卷 1253-1264页

作者： Song, Jiankang Lin, Limei Huang, Yanze Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou Fujian350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou Fujian350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid increase of the number of processors in multiprocessor systems and the fast expansion of interconnection networks, the reliability of interconnection network is facing severe challenges, where the fast recognition of fault processors is crucial. In practice, most of the processor failures are intermittent faults. In this article, we first determine the intermittent fault diagnosability t I PMC(S n 2) tIPMC(Sn2) of n-dimensional split-star network Sn2 under the PMC model. In addition, we propose a fast intermittent fault probabilistic diagnosis algorithm FIFPDPMC to identify the nodes with intermittent fault in the n-dimensional split-star network Sn2 under the PMC model, and we calculated the time complexity of the algorithm FIFPDPMC. Then we implement the algorithm FIFPDPMC in the IoT-based wireless sensor network (IoTWSN) and a randomly generated network (RGN) under different number of nodes with intermittent fault, and we evaluate the performance and efficiency of the algorithm FIFPDPMC in terms of accuracy, precision, recall (TPR), F1, G-mean, FPR, TNR and FNR. Experimental results show that, as the number of stages of executing the algorithm FIFPDPMC increases, the number of nodes with intermittent fault being diagnosed by the algorithm FIFPDPMC increases, which implies that the algorithm FIFPDPMC has good performance and efficiency in both IoTWSN and RGN. © 1990-2012 IEEE.

关键词： Fault detection

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Design and Implementation of Side Channel Attack Based on Deep Learning LSTM

Design and Implementation of Side Channel Attack Based on De...

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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Center for Cyber Security Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9781665482585

Encryption algorithms and encryption devices both play a key role in ensuring the safety of data that has been encrypted. Various types of attacks, such as energy analysis, can be used to assess the reliability of the encryption devices. Since it was originally introduced, side channel attacks' deep learning-based methodology has drawn plenty of attention. This is one of several different attack strategies. In this paper, a side channel attack method based on the LSTM deep learning network is suggested. The method use Correlation Power Analysis (CPA) to find the relevant information in the side channel power consumption data. The choice of a suitable interest interval to utilize as the feature vector in the creation of the neural network model is then guided by the position of the interest points. The trials' findings show that the LSTM model outperforms both MLP and CNN in terms of how well it executes side channel attacks. © 2023 IEEE.

关键词： Side channel attack

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach 3

Breast Cancer Detection using Domain-Adversarial Training (D...

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

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 IEEE.

关键词： Adversarial machine learning

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Blockchain Based security Enabled Smart Contract for Energy Management 16

Blockchain Based Security Enabled Smart Contract for Energy ...

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16th International Conference on security of Information and Networks, SIN 2023

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

ISBN: (纸本)9798350343212

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. © 2023 IEEE.

关键词： Smart contract

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A Review Paper on Designing Intelligent Intrusion Detection System Using Deep Learning 11

A Review Paper on Designing Intelligent Intrusion Detection ...

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11th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Wasnik, Priyanka Chavhan, Nekita G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India G H Raisoni College of Engineering IoT and Cyber Security Department of Data Science Nagpur India

ISBN: (纸本)9798350348422

Machine learning techniques have been have proven to be more effective than conventional extensively used in the creation of intrusion detection systems (IDS) that can swiftly and automatically identify and classify cyber attacks at the host-and network-levels. A scalable solution is needed since destructive attacks are happening so quickly and are changing all the time. For more investigation, the malware community has access to a number of malware databases. The performance of several machine learning algorithms on a range of datasets that were made available to the general public, however, has not yet been thoroughly evaluated by any study. The publicly available malware datasets should be regularly updated and benchmarked due to the dynamic nature of malware and the continuously evolving attacking techniques. In this study, a deep neural network (DNN), a type of deep learning model, is examined in order to create a flexible and efficient IDS to identify and categorise unexpected and unanticipated cyber threats. In order to analyse a variety of datasets that have been produced throughout time using both static and dynamic methodologies, it is vital to take into account the rapid increase in attacks and the constant evolution of network behaviour. It is simpler to select the most effective algorithm for accurately predicting forthcoming cyber attacks with the help of this type of research. Many publicly available benchmark malware datasets are used to offer a thorough review of DNN and other conventional machine learning classifier studies. The KDDCup 99 dataset and the accompanying hyper parameter selection techniques are used to choose the ideal network parameters and topologies for DNNs. A learning rate of [0.01-0.5] is applied to every 1,000-epoch DNN experiment. A variety of datasets, including NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, as well as the DNN model that performed well on KDDCup 99 are used to conduct the benchmark. Our DNN model trains a

关键词： Intrusion detection

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Designing Intelligent Intrusion Detection System Using Deep Learning 14

Designing Intelligent Intrusion Detection System Using Deep ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Wasnik, Priyanka Chavhan, Nekita G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India G H Raisoni College of Engineering Department of Data Science IoT and Cyber Security Nagpur India

ISBN: (纸本)9798350335095

In order to increase the effectiveness and precision of cyber threat detection in computer networks, deep learning techniques are being applied in the construction of intelligent intrusion detection systems (IDS). The suggested IDS can automatically learn and recognize complicated patterns and characteristics from network traffic data by utilizing the capabilities of deep neural networks. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), or deep belief networks (DBNs) are some examples of deep learning architectures that can be built as part of the system design. These models can then be trained on labelled data to distinguish between legitimate and nefarious network behavior. In order to detect both well-known and new threats, the IDS concentrates on capturing both known attack signatures and unusual behaviors. Deep learning models are well suited for real-time intrusion detection in dynamic network environments because they can generalize and adapt to changing threats. By using methods like transfer learning, dimensionality reduction, and visualization, the system also handles issues like feature extraction, scalability, and interpretability. The suggested IDS seeks to achieve higher detection rates, lower false positive rates, and improved resilience against evasion strategies through thorough experimentation and review. The ultimate objective is to create an intelligent IDS that accurately detects and mitigates hostile activity to successfully protect computer networks, improving the overall security posture of the network infrastructure. © 2023 IEEE.

关键词： Intrusion detection

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