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Malware Detection Method Based on TPE Optimized Stacking 3

Malware Detection Method Based on TPE Optimized Stacking

引用

3rd international conference on High Performance computing and Communication Engineering, HPCCE 2023

作者： Zhao, Dongbo Yuan, Zesong Xie, Nannan Jilin Province Key Laboratory of Network and Information Security School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Fast detection and categorization of malware are increasingly important for securing hosts and networks. Although many machine learning models have been utilized to detect malware, single-model detection may not remain efficient in the face of diverse datasets. The continuously increasing size of malware need the distributed storage systems and distributed computing. The distributed methods and ensemble learning are introduced into malware detection, and an optimal combination of base classifiers suitable for stacking are designed. A hyperparameter optimization method based on the Tree-structured Parzen Estimator (TPE) approach to enhance malware detection. The proposed method is implemented on Apache Spark and Hadoop Distributed File System(HDFS). Experiments conducted on four independent datasets, including Android and Windows, demonstrate that the proposed method can achieve 99.41% accuracy on the Android dataset and 96.96% accuracy on the Windows dataset. © 2024 SPIE.

关键词： learning systems

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Financial Forecasting Through Hybrid Algorithms of machine learning & Deep learning 3

Financial Forecasting Through Hybrid Algorithms of Machine L...

引用

3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raman, Ramakrishnan Kafila Gehlot, Anita Shah, Heli Sanjay, C.P. Ponnusamy, R. Institute of Business Management Maharashtra Pune India SR University School of Business Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India Parul University Mechatronics & Robotics PIT India Sri Balaji University Department of IT& Business Analytics SBUP Pune India Chennai Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350399264

This article serves as a primer where integration of different techniques related to machine learning in the realm of economic prediction, planning, and analysis (FP&A). With its ability to extract insights quickly and automatically from massive datasets, machine learning looks to be a natural fit for supporting FP&*** this article, we highlight the caveats of utilizing classic machine learning approaches for scheduling and data allocation, since most of these methods are designed for forecasting (prediction) (causal inference). Newly designed double machine cognitive approach may answer causal concerns of interest, although naïve use of machine learning often fails in this context. In this paper, we survey the existing literature on the use of machine learning to financial planning and analysis, and we utilize a case study from the field of simulation research to demonstrate the utility of this technique for both decision making and planning. We also look at how better predicting and planning may be achieved with more data sets. © 2023 IEEE.

关键词： Data mining

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Exploring Advanced Techniques in Computer and Network Forensics for Enhanced Cybersecurity

Exploring Advanced Techniques in Computer and Network Forens...

引用

2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Nagpal, Amandeep Kumar, S Vinod Reddy, Uma Radha, K. Kumar, Ashwani Abdulla, Namaat R. Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India St. Martin's Engineering College Dept. of It Telangana Secunderabad India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Because cybersecurity is continually changing, it's crucial to have effective, adaptable defenses. This paper demonstrates a novel computer security method. This is done via blockchain technology, enhanced cryptography, threat intelligence integration, machine learning-based strangeness detection, and behavioral analysis. After carefully comparing our solution to others, we feel it is the best way to secure computer and network systems. The machine learning-Based Anomaly Identification approach accurately identifies anomalies using complex mathematical calculations and repetitive procedures to adjust parameters. Blockchain technology unites odd data into a decentralized ledger using encryption for forensics efficiency. Homomorphic encryption and quantum-resistant signatures protect the program against new attacks. The proposed method exhibits superior performance across various metrics, achieving a detection accuracy of 98.5% with a low false positive rate of 1.0%. Visualizations elucidate trade-offs, trends, and classification metrics. This cybersecurity framework offers a promising solution for addressing the dynamic challenges posed by a wide range of cybersecurity threats. Its adaptability, versatility, and collective efficacy position it as a noteworthy advancement in the pursuit of securing digital environments from evolving and sophisticated cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Research and Application on Anomaly Detection of Dynamic Baseline Performance Time Series Data Based on machine learning

Research and Application on Anomaly Detection of Dynamic Bas...

引用

2023 IEEE international conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Song, Ningxi Li, Wencui Liu, Boyu Zhang, Jing Niu, Binbin Jia, Jingli Henan Zhengzhou China State Grid Henan Electric Power Company Henan Zhengzhou China

ISBN: (纸本)9798350314670

Anomaly detection of dynamic baseline performance time series data is an important problem in data center and cloud computing. In order to solve this problem, an anomaly detection method based on machine learning for the time series data of dynamic baseline performance was proposed. Firstly, sliding window is used to extract data features, and a variety of machine learning algorithms are used for anomaly detection. At the same time, a method based on dynamic baseline is proposed, which can adjust the baseline adaptively and detect anomalies. Experimental results show that the proposed method can achieve good anomaly detection results in data center and cloud computing. © 2023 IEEE.

关键词： Anomaly detection

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machine learning based Fault Detection for Non-Linear Systems 9

Machine Learning based Fault Detection for Non-Linear System...

引用

9th international conference on Smart computing and Communications, ICSCC 2023

作者： Harikrishna, Billa Kanagalakshmi, S. National Institute of Technology Calicut Department of Electrical Engineering Kerala India

ISBN: (纸本)9798350314090

Brushless DC motors (BLDC) are utilized widely as compared to traditional motors due to features like a high dynamic response, noiseless operation, variable operating speed range, low maintenance, etc. BLDC drive finds application in hazardous industrial environments;there by the identification and isolation of the faults need to be done at an earlier stage. BLDC drives are subjected to various kinds of faults like short circuit faults, open circuit faults, and inter-turn faults. In this paper, machine learning algorithms are applied to overcome the shortcomings of short-circuit fault detection through conventional methods. machine learning techniques are divided into four major stages: 1) Generation of data set from SIMULINK model under different fault conditions (Pre-fault, LLG, LLLG) 2)Feature extraction and preprocessing of the dataset 3) Classification utilizing machine learning algorithms 4) Classifier execution assessment utilizing the confusion matrix. © 2023 IEEE.

关键词： Fault detection

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AI-Powered Smart and Personalized Education Platform

AI-Powered Smart and Personalized Education Platform

引用

2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Dunusinghe, A.V. Ranasinghe, T.K.S.A. Gamage, J.G.A.C.H. Perera, K.G.D.T. Thelijjagoda, Samantha Gunatilake, Poojani Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Research and International Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350319200

An AI assistant addresses online education's shortcomings, focusing on personalized learning and career guidance. It provides tailored recommendations, assesses performance, suggests subjects, and crafts learning plans. Objectives include accurate grade prediction, subject stream advice, customized assessments, and personalized strategies. The study shows data analytics and machine learning forecast grades, while multi-tenancy architecture improves data handling. Personalized assessments consider both academics and soft skills. machine learning promises accurate subject stream predictions, and engagement detection aids in optimal learning. This AI system holds potential in reshaping online education, but ongoing improvements are vital for enhanced learning. © 2023 IEEE.

关键词： career guidance learning strategies machine learning multi-tenancy Online education personalized assessment personalized learning subject stream recommendation

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Federated learning Based Elderly Fall Detection Using Edge computing

Federated Learning Based Elderly Fall Detection Using Edge C...

引用

2023 IEEE international conference on Next Generation Electronics, NEleX 2023

作者： Niyas, S.K. Sathya, P. School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798350319088

Human falls lead to severe complications in daily life activities. In most cases, it may affect the physical and mental well-being of the person. Fall events become critical when they happen to elderly people. Most of the human falls occurring in the elderly community aggravate their physical and mental conditions. The severity of fall incidents may be mitigated by real-time detection. Many research works were done and many were in progress to detect the fall events so that immediate medical action can be taken to save their lives. Edge computing platforms are gaining popularity in the 5G and beyond 5G communication eras as they provide low latent, high-energy-efficient, and secure computing. Recent research trends in artificial intelligence and machine learning include improvising methods for the classification and detection of events from a set of inputs. Federated learning is one of the most promising machine learning algorithms. In this paper, we propose a federated learning approach for elderly fall detection using edge computing techniques. Two sensory inputs, one from a smart shoe and the other from a smartwatch, are used to collect data from the person, which is then analysed on the edge computing server. If any one of the input sets detects a fall event, it will be immediately communicated as a medical emergency. Also, images of the event can be captured using the camera placed in the room to confirm the event. © 2023 IEEE.

关键词： Fall detection

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Stubble Burning Detection and Prevention using machine learning and Blockchain 10

Stubble Burning Detection and Prevention using Machine Learn...

引用

17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Faridi, Arman Rasool Hafeez, Aiman Masood, Faraz Salmony, Monir Yahya Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9789380544472

Stubble burning in India contributes significantly to air pollution by emitting harmful gases and particles into the atmosphere. This annual practice is responsible for the severe pollution that envelops the country every winter, particularly in the National Capital Region. Lower temperatures and steadier air in winter exacerbate the impact of rice stubble burning. However, research on using machine learning with blockchain to address this problem is limited. This study aims to provide a conceptual framework for using machine learning and blockchain to prevent and detect stubble burning. With the help of machine learning and satellite images, areas originating the pollution can be identified. This can be done with pre-processed images sent to pre-trained models to identify the place of origin of the pollution. If the pollution crosses a threshold limit, then the location will be stored in the blockchain. A decentralized application will be created from where these details can be accessed by concerned authorities. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain

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ECG Signals- Early detection of Arrhythmia using machine learning approaches 13

ECG Signals- Early detection of Arrhythmia using Machine Lea...

引用

13th international conference on Cloud computing, Data Science and Engineering, Confluence 2023

作者： Nandanwar, Jahnavi Singh, Jasmeet Patidar, Sanjay Delhi Technological University Department of Software Engineering Delhi India

ISBN: (纸本)9781665462631

Heartbeats can be recorded in the form of electrical signals using a machine called an electrocardiogram. The cardiovascular system's performance can be tracked using an electrocardiogram (ECG), which can also be used to diagnose various cardiac diseases. The paper by Mohammad Kachuee et al., entitled 'ECG Heartbeat Classification: A Deep Transferable Representation' classifies heartbeats into five different classes according to AAMI EC57 standards. In this paper, we continue the work on their preprocessed dataset and train the model to detect arrhythmia, i.e., abnormalities in the heartbeat. We also propose a comparative study between different machine learning and deep learning models, namely random forest classification, support vector machines, and convolutional neural networks, for detecting abnormalities in heartbeats. The results we got after the application of all our classifiers showed the best accuracy score of 98.26% for the MIT-BIH dataset and a 100% accuracy score for the PTB Diagnostic dataset. © 2023 IEEE.

关键词： Electrocardiograms

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Adapting to Evasive Tactics through Resilient Adversarial machine learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain Consensus algorithm Data privacy Digital signatures Distributed ledger Faces Organizations Smart contracts

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