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Intrusion Detection System in Wireless Sensor Networks using Modified Recurrent Neural Network with Long Short-Term Memory 2

Intrusion Detection System in Wireless Sensor Networks using...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Ramkumar, K. Alzubaidi, Laith H. Malathy, V. Venkatesh, T. Kruthika, C.G. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India The Islamic University Najaf Iraq Sr University School of Engineering Department of Ece Telangana India Kpr Institute of Engineering and Technology Department of Ece Coimbatore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350317558

Leveraging deep learning, Intrusion Detection Systems (IDS) for Wireless Sensor Networks (WSN) enhance security through advanced analytics, detecting and mitigating network intrusions in real-time for robust protection. Evaluations and scalability considerations are critical for improving the applicability and robustness of intrusion detection systems based on deep learning techniques. This paper introduces a novel approach: a modified Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) for IDS in WSN classification, utilizing the NSL-KDD dataset. Rigorous performance validation demonstrates outstanding metrics, including an accuracy of 99.95%, precision at 99.93%, and a robust recall of 95.69%, with the F1-score reaching 99.80%, surpassing benchmarks set by existing methods. Notably, current approaches like Deep Neural Network (DNN), Convolutional Neural Network-LSTM (CNN-LSTM), Conditional Generative Adversarial Network (CGAN), and Stacked Autoencoders-DNN (SAE-DNN) face limitations in achieving optimal accuracy and robustness. The proposed RNN-LSTM method exhibits significant advancements in addressing these challenges for effective intrusion detection in WSN © 2024 IEEE.

关键词： Convolutional Neural Network Deep Neural Network Intrusion Detection System Long Short-Term Memory Wireless Sensor Network

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Malware Detection Using the Machine Learning Based Modified Partial Swarm Optimization Approach 1

Malware Detection Using the Machine Learning Based Modified ...

引用

1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICaiSC 2023

作者： Sivakumar, S. Saminathan, S. Ranjana, R. Mohan, M. Pareek, Piyush Kumar Department of Computer Science Perambalur India Srinivasan College of Arts and Science Department of Computer Science and IT Perambalur India Panimalar Engineering College ECE Department India AP/CSE KPR Institute of Engineering and Technology India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350323795

Malware analysis includes a crucial step after malware detection called malware categorization, which classifies dangerous files. There have been many reported static and dynamic methods for classifying malware up to this point. The ml-MD strategy presented in this study uses static methods to categorise various malware families and is based on machine learning. In order to detect malware, we create a new machine learning-based framework. The characteristics from the dataset are extracted in this case using principal component analysis (PCA). In order to offer the best malware detection solutions, introduce a machine learning-based Modified Particle Swarm Optimization (MPSO) algorithm. Improved Accuracy and detection rate using ml-based MPSO technique. The effectiveness of the suggested technique in detecting malware is demonstrated by the experimental results on several benchmark data sets, which greatly outperform alternative approaches. © 2023 IEEE.

关键词： Machine learning

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Pandemic state’s impact on agriculture

Pandemic state’s impact on agriculture

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Rohith Bhat, C. Sudha, I. Michael, G. Manikandan, T. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Oman Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9781032669663

The COVID-19 outbreak has significantly disrupted the agricultural system in India. In spite of this, the most recent quarterly GDP forecasts post-COVID scenario demonstrate the tenacity and durability of the Indian agricultural sector, which is anticipated to be the only sector to record a positive growth rate of 4% for fiscal years (FY) 2020 and 2021. Similarly, growth for the most recent quarter is projected to have been 5.89%, representing a reduction of 2.4% point. In this context, our goal is to compile a summary of the early evidence on the impact of COVID-19 on the agricultural sector in India, including production, marketing, and consumption, and then to design a set of pragmatic strategies to recover and thrive after the pandemic. According to the survey’s findings, the pandemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity prices, altering consumption patterns. The pandemic caused great emotional, social, economic, and bodily harm to everyone involved in India’s agriculture industry. The state has responded to the crisis by seizing the opportunity it presents and launching a variety of programmes and necessary changes. We suggest a ten-point plan that includes social safety nets, family farming, the trading of buffer stock, staggered procurement, and secondary agriculture in order to revitalise and prosper after the epidemic. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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Robust Face Recognition Using Histogram of Oriented Gradients and Auto-Associative Memory Network

Robust Face Recognition Using Histogram of Oriented Gradient...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Jay Kant Pratap Singh Yadav Zainual Abdin Jaffery Laxman Singh Electrical Engineering Department Jamia Millia Islamia (Central University) New Delhi India Computer Science & Engg.(AI&ML) Department KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Facial recognition technology is based on a person's facial features. It is often the core of applications such as biometrics, access control, and forensics. However, due to environmental factors such as dust accumulation on the camera lens or equipment failure, the accuracy of the system is often questioned; especially when it comes to noise and partial occlusions in the test images. This paper proposes a new approach for automatic face recognition using histogram oriented gradient features of facial images with auto-associative memory in the form of a “Multi-Connection Hopfield Neural Network”. The method described here efficiently stores and retrieves facial patterns in noisy or partially occluded environments. In testing, this proposed approach achieved a recognition rate of 98.5% on the ORL dataset, outperforming these other state-of-the-art methods. Furthermore, this method maintains 95.0% accuracy even with 50% random noise and exhibits superior performance and robustness under partial occlusion and variable lighting conditions.

关键词： Histograms Accuracy Face recognition Noise Lighting Hopfield neural networks Robustness Computational efficiency Noise measurement Testing

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An Analysis of Privacy Attacks in Supervised Machine Learning Algorithms

An Analysis of Privacy Attacks in Supervised Machine Learnin...

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Kavya Gupta Anurag Gupta Abha Kiran Raipoot Department ofCSE(AI&ML) KIET Group of Institutions Ghaziabad India Department of CSE(AI) KIET Group of Institutions Ghaziabad India School of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

With the widespread integration of machine learning (ml) into diverse applications, it has become paramount to scrutinize its implications for security and privacy. Despite the burgeoning body of work in privacy, there is a noticeable gap in research attention between the security and privacy aspects of ml, specifically supervised machine learning (S-ml). This paper bridges this gap by analyzing research papers focusing on privacy attacks against S-ml over the past years. This study offers a comprehensive overview of prevalent defense mechanisms and initiates a thoughtful discussion on unresolved challenges and prospective research directions. As ml continues to permeate various domains, the scientific community's interest has expanded beyond performance metrics to encompass broader concerns such as security, privacy, fairness, and explainability. This survey investigates state-of-the-art privacy-related attacks, elucidating common design patterns and distinctive characteristics. Through this exploration, we identify several open problems that demand further investigation, emphasizing the critical need to fortify privacy frameworks alongside the escalating ubiquity of ml technologies.

关键词： Surveys Measurement Privacy Data privacy Machine learning algorithms Quantum computing Organizations Machine learning Robustness Security

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A Novel Approach to Identify Pituitary Tumor Using an Optimal CNN Model with Deep Learning

A Novel Approach to Identify Pituitary Tumor Using an Optima...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Ebin, P.M. Thomas, Roja Ismail, Safad Kavitha Nair, R. Alliance School of Advance Computing Alliance University Banglore India Mangalam College of Engineering Department of Computer Science and Engineering Ettumanoor India College of Engineering Chengannur Department of Computer Engineering Chengannur India Acharya Institute of Technology Department of Ai &ml Banglore India

ISBN: (纸本)9798350313796

The technical paper introduces a novel approach to identifying pituitary tumors using an Optimized Convolutional Neural Network (OCNN) model with deep learning. Pituitary tumors rank among the most prevalent variety of brain tumors, and accurate identification is critical for proper diagnosis and treatment. The suggested method makes use of a deep learning framework that combines an ideal feature extraction method with a pre-trained CNN model. The pulled out features are then used to train the CNN model to classify pituitary tumors accurately. The effectiveness of the suggested method is assessed using a publicly accessible dataset, revealing that it surpasses the performance of current leading techniques. The suggested method possesses the capacity to act as a valuable asset for making clinical decisions in the field of neuroimaging, particularly for the accurate identification of pituitary tumors. The results of the experiment indicate that our suggested model successfully detects and classifies tumors with high accuracy, sensitivity, and specificity. Our model specifically achieves a 99.55% overall accuracy, 99% sensitivity, 99% specificity and 99% F1 score. These outcomes show that our suggested CNN design for brain tumor identification and classification performs better than several state-of-the-art techniques. © 2023 IEEE.

关键词： Tumors

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Enhancing Customer Retention through Feature Selection and XGBoost Classification for Churn Prediction

Enhancing Customer Retention through Feature Selection and X...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Raye Haarika Ayain John Department of Computer Science and Engineering(AI & ML) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Customer churn remains a significant and pressing challenge for both large enterprises and businesses across various industries. Particularly within the banking sector, where revenue is directly influenced by customer retention, there is a concerted effort to enhance predictive methods for identifying potential churn. This paper explores different machine learning (ml) algorithms employed in constructing a churn model to assist banking operators in predicting customers at risk of churning using minimum attributes and aiming to achieve good accuracy. The research delves into experimental results to compare the efficacy of various techniques, identifying the model that works best for predicting customer attrition in the banking sector in the end. This study uses feature selection and the XGBoost algorithm to select attributes based on their ranks.

关键词： Training Industries Accuracy Costs Computational modeling Machine learning Banking

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Enhanced Large Language Models

Enhanced Large Language Models

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International Conference on Inventive Computation Technologies (ICICT)

作者： J.R.V. Jeny G. Bharath Babu S. Jaganmohan Reddy N. Muthukumaran K. Abhiram Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This work studies ways for improving the effectiveness of a language model in creating and understanding dialogue summaries. Two primary approaches are investigated, in the Complete Fine-Tuning step, a pre-trained FLAN-T5 model is fine-tuned using a dataset of dialog-summary pairs. The dataset is pre-processed to provide explicit instructions to the model, and the fine-tuning process involves training the entire model on the dataset. Qualitative and quantitative evaluations are then conducted to assess the effectiveness of the fine-tuned model in generating summaries. In the PEFT phase, a more computationally efficient method is employed, utilizing Low-Rank Adaptation (LoRA) to train a new layer/parameter adapter while keeping the underlying FLAN-T5 model frozen. This approach significantly reduces computational resources compared to Complete Fine-Tuning while maintaining comparable performance. The resulting model, equipped with the PEFT adapter, is evaluated qualitatively and quantitatively to measure its summarization capabilities. Both approaches sheds light on the compromises between computing efficiency and model performance.

关键词： Training Measurement Adaptation models Generative ai Computational modeling Natural languages Information retrieval

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Classification of DDoS Attacks Using Ensemble Learning

Classification of DDoS Attacks Using Ensemble Learning

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Adepu Sai Aashrith Vishwaroju Sirichandana Barka Tarun Kumar M A Jabbar Y Sridevi Computer Science and Engineering (AI & ML) Vardhaman College of Engineering Department of English Vardhaman College of Engineering

In an era where the digital realm is indispensable to daily life, Constantly posing a threat, distributed denial of service (DDoS) attacks are capable of disrupting vital online services. Traditional methods of DDoS attack classification often fall short when facing the ever-evolving landscape of attack techniques. This paper unveils a journey through the complexities of enhancing DDoS attack classification with a focus on improving accuracy and adaptability. We embarked on this research by individually training a plethora of machine learning models. However, our preliminary findings highlighted the striking dis-parity in accuracy across different models, highlighting how complex and varied DDoS attacks are. Driven to overcome these constraints, we resorted to the sturdy framework of ensemble learning. Ensemble learning changed the game for accurate DDoS attack classification by effortlessly combining the best features of several base models. To be more precise, we blended two distinct baseline models: K-Nearest Neighbors (KNN) and Random Forest (RF). These models, each with distinct characteristics, combined to form a flexible and adaptive DDoS attack classification system. K-Nearest Neighbors (KNN) uses the proximity of data points to find trends and parallels. Resilience, high-dimensional data processing, and overfitting resistance are all favoured by the Random Forest (RF) ensemble of decision trees. Combining these several methods into a group greatly improved our categorization accuracy. Our research's ability to forecast accuracy using the ensembled model was one of its main results. This prediction accuracy indicates the flexibility and dependability of the classification model in addition to providing a crucial performance standard for assessing system effectiveness. Finally, our explo-ration of the DDoS assault classification demonstrated Ensemble Learning's superiority. The proposed strategy surpassed the existing methodologies, according to experimental

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MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer 31

MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs th...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hong, Seongtae Lee, Seungyoon Moon, Hyeonseok Lim, Heuiseok Department of Computer Science and Engineering Korea University Korea Republic of Human-inspired AI Research Korea Republic of

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have rapidly advanced, with domain-specific expert models emerging to handle specialized tasks across various fields. However, the predominant focus on English-centric models demands extensive data, making it challenging to develop comparable models for middle and low-resource languages. To address this limitation, we introduce MIGRATE, a novel method that leverages open-source static embedding models and up to 3 million tokens of code-switching data to facilitate the seamless transfer of embeddings to target languages. MIGRATE enables effective cross-lingual adaptation without requiring large-scale domain-specific corpora in the target language, promoting the accessibility of expert LLMs to a diverse range of linguistic communities. Our experimental results demonstrate that MIGRATE significantly enhances model performance in target languages, outperforming baseline and existing cross-lingual transfer methods. This approach provides a practical and efficient solution for extending the capabilities of domain-specific expert models. © 2025 Association for Computational Linguistics.

关键词： Embeddings

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