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Anomaly Detection in SCADA Industrial Control Systems Using Bi-Directional Long Short-Term Memory 2nd

Anomaly Detection in SCADA Industrial Control Systems Using ...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Nakkeeran, M. Narayanan, V. Anantha Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819934805

In the modern era, industries are migrating to Industry 4.0 in the aspects of smarter and faster with efficiency to increase productivity to attain a profit. Thus, it becomes a part of Cyber-Physical Systems (CPS) which is connected to the internet and pays a path for intrusion in the networks when dealing with the network traffic of ICS. In this paper, we evaluated the Industrial Control Systems (ICS)/Supervisory Control and Data Acquisition (SCADA) Cyber Attack New Gas Pipeline Dataset from Mississippi State University with specialized two-time-series Recurrent Neural Network architectures of Deep Learning for anomaly detection which will act as an Intrusion Detection System (IDS). Here, we used two-time-series models: Bi-LSTM (Bi-directional long short-term memory) and Bi-GRU (Bi-directional Gated recurrent units) were used along with the LOCF (Last Observation Carried Forward) pre-processing technique to fill out the gaps and SMOTE (Synthetic Minority Oversampling Technique) for balancing the dataset. The main contribution of this paper involves identifying the attack type and its location related to specific attack classification apart from binary and category attacks classification which helps in preventing processing downtime from the zero-day exploits. Bi-LSTM outperforms better in all three types of attacks such as Binary (97.78%), Category (95.56%), and Specific (95.40%) in terms of accuracy metrics compared to Bi-GRU and other Deep learning methods. However, computation time is a trade-off between Bi-LSTM and Bi-GRU, resulting in more or less similar results. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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PIT Performance Measurement using NFD Pipeline Parameters in Named Data Networking (NDN) 10

PIT Performance Measurement using NFD Pipeline Parameters in...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Sucipto, Adi Ahdan, Syaiful Iskandar Syambas, Nana Rachmana Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science 35132 Indonesia

ISBN: (纸本)9798350377057

Although NDN is an entirely new architecture, its design principles are based on the success of the current Internet, they represent our knowledge of the advantages and disadvantages of the current Internet architecture, and they can be implemented gradually using the operations of the current Internet. The Pending Interest Table (PIT), the Forwarding Information Base (FIB), and the Content Store (CS) are the three data structures found inside the NDN router. Interest sent upstream to the content source is tracked by the Pending Interest Table (PIT), for data to be transmitted to requesters downstream. PIT is also used for measurement, loop detection, and the containment of recently satisfied interest. In this study, we did the simulation to measure and analyze the performance of PIT in NDN using NFD Pipeline Measurements. The NDNSim simulator was used in the research to observe PIT performance. The findings of the simulation and analysis indicated that in an NDN network, Satisfied Interest was influenced by the granularity of a prefix. At the number of interests 1000, the results showed a decrease in packets, including a decrease of around 21% in the Ininterest packet, and around 30% inSatisfiedInterest and InData. The effect of Prefix length also decreased in Ininterest ranging from 0.014-0.015%, while in inSatisfiedInterest and InData it ranged from 4.2-8.5%. The PIT's performance was influenced by the length of the prefix and the number of interest messages the customer sent. © 2024 IEEE.

关键词： Natural gas pipelines

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Detection of Brain Stroke by using the Novel Adaboost Classifier Algorithm Classifier Algorithm Compared with Multi-layer Perceptron

Detection of Brain Stroke by using the Novel Adaboost Classi...

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Manikanta, P. Kannan, K. Nattar Padmakala, S. Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Saveetha University Chennai India

ISBN: (纸本)9798350358926

Compare Novel Adaboost Classifier Algorithm and MultiLayer Perceptron to improve accuracy. Materials and Methods: Research groups include Novel Adaboost Classifier Algorithm and MultiLayer Perceptron. The 64.4 MB dataset includes 30 healthy and 30 damaged brain images. Kaggle provided CT images for brain stroke detection. Identifying the training dataset requires 500 records-80% for training and 20% for testing. A unique Adaboost method and a multilayer perceptron approach were compared using 10 sets to improve research accuracy. Ten times per iteration, twenty sets were compared. The G power test with α=0.05 and β=0.2 yields approximately 80%. A novel Adaboost classifier algorithm (88.84%) beats MultiLayer Perceptron (83.40%). The independent sample test value is significant at 0.01 (p © 2024 IEEE.

关键词： Machine learning

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Real-Time Bitcoin Cost Identification to Improve Efficiency Using Lasso Regression in Comparison with Decision Tree

Real-Time Bitcoin Cost Identification to Improve Efficiency ...

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Pavankumar, Gajula Velmurugan, J. Padmakala, S. Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Saveetha University Chennai India

ISBN: (纸本)9798350358926

Enhancing the Bitcoin cost prediction to improve accuracy using a decision tree in comparison with Lasso regression. For trend confirmations, we often utilize the financial phrases support and resistance to the prediction. Materials and Methods: The price of bitcoin is forecasted using a straightforward prediction technique called Lasso regression. The effectiveness of each model's performance on the task was evaluated, and the findings were examined. The goal of testing so many different models was to examine how the fundamental hypotheses of each of them varied. We used Lasso regression together with the best parameters to weigh the input variables because it is a reliable technique. Lasso regression is a Model based on a sample T-test with a value of p is 0.000 (Independent sample t-test p © 2024 IEEE.

关键词： Forecasting

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User Interface and User Experience Design Methodologies and Evaluation Techniques: A Systematic Literature Review 1

User Interface and User Experience Design Methodologies and ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Raj, Gaurav Chakraverti, Ashish Kumar Mehta, Shruti N. Sharda University Department of Computer Science & Engineering School of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350356816

Effective User Interface (UI) and User Experience (UX) design are essential for digital applications, crucial for enhancing user interactions and engagement. Developing a high-quality UI is now a key aspect of software development. This study presents a comprehensive literature review of recent research on UI and UX design methodologies for digital products. It serves as the preliminary findings for an ongoing research project aimed at using this knowledge to develop a mobile game. The qualitative analysis insights are expected to significantly improve user interaction and experience in digital applications. © 2024 IEEE.

关键词： User centered design

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Advancing Industrial Effluent Monitoring Systems for Environmental Protection and Sustainable Development 11

Advancing Industrial Effluent Monitoring Systems for Environ...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mitra, Milan Kumar, Akash Yadav, Lokesh Kaur, Mandeep School of Engineering & Technology Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350350357

Industrial Effluent Monitoring Systems (IEMS) serve as vital tools for upholding environmental regulations and safeguarding public health and ecosystems. These systems are designed to assess the quality of wastewater discharged from industrial facilities, enabling monitoring of various parameters including pH, temperature, conductivity, Total Dissolved Solids (TDS), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), nutrients, heavy metals, and toxic organic compounds. The choice of parameters to monitor depends on the specific industry and the regulations that pertain to it. In recent years, there has been a notable shift towards automated IEMS, driven by their increased efficiency and accuracy. Automated systems offer the significant advantage of real-time data collection, which facilitates the early identification and prompt resolution of environmental issues, reducing the risk of non-compliance and pollution incidents. This paper presents a comprehensive overview of the latest advancements in IEMS technology and underscores their role in enhancing environmental protection. It also references related research, contributing to a broader understanding of the field. By embracing these innovative monitoring systems and staying abreast of technological developments, industries can not only ensure compliance but also minimize their ecological footprint, fostering a more sustainable and responsible approach to wastewater management. © 2024 IEEE.

关键词： Environmental regulations

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Brain Tumor Detection using Optimized Stochastic Gradient Descent Function

Brain Tumor Detection using Optimized Stochastic Gradient De...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Mishra, Annu Chaturvedi, Ravi Prakash Sharma, Hirdesh Kumar, Prashant Asthana, Sameer Parashar, Mayank Sharda School of Engineeringand Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineeringand Technology Department of Computer Science and Applications Greater Noida India JEMTEC Department of Computer Science and Engineering Greater Noida India ITS Engineering College Department of Computer Science and Engineering Greater Noida India GNIOT Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

The learning rate of a CNN determines the efficiency of the neural network. In brain tumor detection, the learning rate and the optimization function plays a key role in deciding the final output. The Optimized Stochastic Gradient Descent (OSGD) optimizer giver better result in terms of Accuracy, Recall and other determining factors. We have also compared the results obtained with other optimizers and it could be clearly stated that our proposed model. The OSGD optimizer controls the maxima and minima, and move the near optimum solution buy controlling the learning rate. So via this method we can control our right *** we detect our object in les error. © 2024 IEEE.

关键词： Stochastic systems

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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques 2

Review on Predicting the Landslide Susceptibility using Mach...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sinha, Abhishek Giri, Shivam Kumar Yadav, Himanshu Prasad, Sanjeev Kumar Mishra, Priya School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generaliz ability challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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Multimodal Sentimental Analysis For Real-Time Monitoring of Work Stress Levels 6

Multimodal Sentimental Analysis For Real-Time Monitoring of ...

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Singh, Arpit Kumar Shukla, Anubhav Aggarwal, Gunjan Upadhyay, Prashant Department of Computer Science Engineering School of Engineering & Technology Sharda University Greater Noida India

ISBN: (纸本)9798350374629

Stress at work is a widespread issue that affects both employee well-being and organizational productivity. Conventional techniques for evaluating stress frequently depend on subjective self-reporting, which provides little information on changes in stress levels in real-time. As a result, the incorporation of multimodal sentiment analysis has become a viable strategy for the ongoing, impartial tracking of stress at work. This survey study offers a thorough overview and comparison of previous studies on multimodal sentiment analysis for measuring job stress in real time. This literature review delves into the elements of multimodal sentiment analysis, comparing and contrasting it with conventional stress assessment techniques and scrutinizes the many methodologies utilized in recent *** in-depth examination of the techniques and performance measures used in these studies provides valuable information on the advantages and disadvantages of various strategies. Research future directions are discussed in light of identified obstacles, such as sample size restrictions and applicability in varied work situations. The study closes with a summary of the most important discoveries, a discussion of the practical ramifications, and suggestions for furthering the topic. This comparative assessment paves the way for future research in this crucial area while also deepening our grasp of the state-of-the-art in multimodal sentiment analysis for real-time job stress monitoring. ©2024 IEEE.

关键词： Sentiment analysis

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The Vulnerabilities and Risks of Implementing Internet of Things (IoT) in Cyber Security 11

The Vulnerabilities and Risks of Implementing Internet of Th...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Cambosuela, Luis Kaur, Mandeep Astya, Rani School of Engineering & Technology Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350350357

The Internet of Things (IoT) refers to the growing network of connected physical objects embedded with sensors, software and connectivity. While IoT has potential benefits, it also introduces new cyber security risks. This paper provides an overview of IoT security issues, vulnerabilities, threats, and mitigation strategies. The key vulnerabilities arising from IoT's scale, ubiquity and connectivity include inadequate authentication, lack of encryption, poor software security, and privacy concerns. Common attacks against IoT devices and networks include denial of service, ransom-ware, man-in-the-middle, and spoofing. An analysis of recent literature highlights emerging attack trends like swarm-based DDoS, IoT botnets, and automated large-scale exploits. Recommended techniques to secure IoT include building security into architecture and design, access control, cryptography, regular patching and upgrades, activity monitoring, incident response plans, and end-user education. Future technologies like blockchain, AI-enabled defense, and post-quantum cryptography can help strengthen IoT security. Additional focus areas include shared threat intelligence, security testing, certification programs, international standards and collaboration between industry, government and academia. A robust multilayered defense combining preventive and detective controls is required to combat rising IoT threats. This paper provides a comprehensive overview of the IoT security landscape and identifies areas for continued research and development. © 2024 IEEE.

关键词： Cybersecurity

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