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A stacked ensemble approach to detect cyber attacks based on feature selection techniques

International Journal of Cognitive Computing in engineering 2024年第1期5卷 316-331页

作者： Urmi, Wahida Ferdose Uddin, Mohammed Nasir Uddin, Md Ashraf Talukder, Md. Alamin Hasan, Md. Rahat Paul, Souvik Chanda, Moumita Ayoade, John Khraisat, Ansam Hossen, Rakib Imran, Faisal Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia School of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Cyber Security Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakoir Gazipur1750 Bangladesh Department of Electrical and Computer Engineering University of Ottawa Canada

The exponential growth of data and increased reliance on interconnected systems have heightened the need for robust network security. Cyber-Attack Detection Systems (CADS) are essential for identifying and mitigating threats through network traffic analysis. However, the effectiveness of CADS is highly dependent on selecting pertinent features. This research evaluates the impact of three feature selection techniques—Recursive Feature Elimination (RFE), Mutual Information (MI), and Lasso Feature Selection (LFS)—on CADS performance. We propose a novel stacked ensemble classification approach, combining Random Forest, XGBoost, and Extra-Trees classifiers with a Logistic Regression meta-model. Performance is assessed using CICIDS2017 and NSL-KDD datasets. Results show that RFE achieves 100% accuracy for Brute Force attacks, 99.99% for Infiltration and Web Attacks on CICIDS2017, and 99.95% accuracy for all attacks on NSL-KDD, marking a significant improvement over traditional methods. This study demonstrates that optimizing feature selection and leveraging diverse classifiers can substantially enhance the accuracy of CADS, providing stronger protection against evolving cyber threats. © 2024 The Authors

关键词： Feature Selection

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An Intelligent Deep Learning Strategy for Breast Cancer prediction using feature ensemble learning

An Intelligent Deep Learning Strategy for Breast Cancer pred...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prakash, E.P. Satheshkumar, K. Anandhasilambarasan, D. Prasath, B. Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350305708

One in six women will be diagnosed with breast cancer (BC) by the time they reach their forties, making it the leading cause of death among this demographic. It's the leading killer in the world, yet it's hard to spot and diagnose in its early stages. Among women, BC is a foremost origin of death in third world countries. The prevention and treatment of these tumors can be greatly aided by screening and early diagnosis. At the moment, BC is the leading malignancy in females, even more so than ovarian cancer. When researchers get access to patient medical records, they can uncover hidden patterns in healthcare. The National-Cancer-institute (NCI) reports that mortality rates from breast cancer can be reduced with early detection. In order to accurately identify patients with cancer, this work presents a voting classifier based on ensemble learning that integrates the logistic-regression(LR) &stochastic-gradient-descent(SGD) with deep-convoluted features. The ensemble voting classifier is fed deeply-convoluted features mined from the microscopic characteristics. This concept offers a refined framework for differentiating amid benign and malignant tumors. The results show that the highest level of classification accuracy, 100%, may be attained when utilizing voting classifiers with complex features. When compared to the traditional methods, the proposed method showed an improvement in accuracy. Thus, the research aids doctors in diagnosing and avoiding breast cancer. © 2023 IEEE.

关键词： breast-cancer classification deep-convoluted-features ensemble learning healthcare prediction

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Finance and the Low-Carbon Transition: Implications for Market Valuation and Investor Behaviour 2

Finance and the Low-Carbon Transition: Implications for Mark...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Bhuyan, Pragyan Bhoi, Shubh Guleria, Prashant Gauhati University Department of Accountancy India National Institute of Technology Department of Computer Science and Engineering India Birla Institute of Technology Mesra Department of Management India

ISBN: (纸本)9798350360523

The shift to low-carbon capital resources has significant implications. Our comprehensive macroeconomic framework, considering high-carbon and low-carbon tangible assets, reveals that adopting low-carbon practices induces oscillations in both the macroeconomy and financial sphere. Examining the impact of financial market sentiments on the transition trajectory, we highlight the pivotal role of the financial sector. Despite lower operational costs of low-carbon technologies, investor behavior favors established technologies. Our analysis shows that increased 'climate indifference' can prolong the transition. Therefore, we emphasize the importance of climate impact disclosures in finance and the need for strategies to assist investors in mitigating uncertainty. © 2024 IEEE.

关键词： Investments

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Path Planning Solution for Intelligent Robots Using Ex*RRT Algorithm in Disaster Relief 5

Path Planning Solution for Intelligent Robots Using Ex*RRT A...

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5th IEEE International Conference on Recent Trends in computer science and technology, ICRTCST 2024

作者： Hariprasath, V.Y. Mukesh, S. Aruna, T.N. Gayana, R. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. SRM Institute of Science and Technology Department of Computational Intelligence Chennai India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Tiruchirappalli India KGiSL Institute of Technology Department of Information Technology Coimbatore India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350351378

Unmanned Ground Robots (UGVs) are increasingly becoming vital tools in disaster relief operations, offering a means to navigate through hazardous environments where human intervention may be risky or impossible. These intelligent robots are designed to perform a variety of tasks, such as searching for survivors, delivering supplies, and assessing damage. However, the effectiveness of UGVs in such critical missions largely depends on their ability to navigate complex and unpredictable terrains, a challenge that necessitates advanced path-planning strategies. The paper introduces a novel approach to path planning for UGVs in disaster relief scenarios. This approach combines the strengths of advanced rapidly Rapidly-exploring Random Trees (RRT) algorithms with the capabilities of YOLOv4, a state-of-the-art technique for object detection, specifically tailored here for human detection. RRT algorithms are known for their efficiency in exploring complex spaces, making them ideal for navigating through the unpredictable terrains typical of disaster zones. Meanwhile, YOLOv4 enhances the UGVs' ability to detect and respond to human presence, a critical requirement in rescue operations. The UGVs equipped with this novel path-planning strategy were able to navigate safely and efficiently, adapting to the dynamic nature of the environment. More importantly, the accuracy in detecting human presence was significantly improved, a crucial factor in ensuring that aid and rescue efforts are directed where they are most needed. By enhancing the path planning capabilities of UGVs with advanced algorithms and human detection technology, this approach holds the potential to revolutionize disaster response operations, making them more efficient and impactful, ultimately leading to faster and more effective aid delivery to those in dire need. © 2024 IEEE.

关键词： Motion planning

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Research on Multi-Modal Artificial Intelligence Information Fusion technology in Embedded computer Systems 3

Research on Multi-Modal Artificial Intelligence Information ...

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3rd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Wu, Qiongfei Xie, Qubo Chen, Yi Wuhan Institute of Design and Science Department of Information Engineering Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350380989

Multi-scale decomposition (MSD) has some problems, such as missing details and generating noise. This project aims to study multi-modal image based on texture decomposition model and using improved convolutional network and phase-shift consistency techniques. Aiming at the problem that MSD is easy to produce noise, low-pass filter optimization function and structure texture decomposition model are introduced to effectively solve the problem of decomposition technology. An adaptive filtering algorithm based on least square method is proposed, and the structure-texture analysis method based on image is combined to overcome the noise problem of traditional algorithms theoretically. Secondly, for structured texture, the fusion algorithm of convolutional neural network and Gaussian fairing is studied to enhance the effective acquisition of image details and remove noise. The phase-shift matching algorithm is used for high frequency band to achieve effective fusion of high frequency signals. Then the inversion is carried out to obtain the final composite image. The images are studied qualitatively and quantitatively from the perspectives of visual effect, mutual information, feature mutual information, structural similarity, information entropy, PSNR, etc. © 2024 IEEE.

关键词： convolutional neural network low-pass filter multi-scale decomposition Multimodal image fusion

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A Review of AI-Enhanced Navigation Strategies for Mobile Robots in Dynamic Environments

A Review of AI-Enhanced Navigation Strategies for Mobile Rob...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Nasti, Shoaib Mohd Chishti, Mohammad Ahsan Central University of Kashmir Department of Information Technology Ganderbal India National Institute of Technology Department of Computer Science and Engineering Srinagar India

ISBN: (纸本)9798350372229

A mobile robot is a software-controlled machinery that uses sensors and advanced technologies to perceive obstacles and navigate its surroundings to reach its desired destination. The prevalence of mobile robots has increased in recent years due to their ability to autonomously navigate any environment without the need for direction or human intervention. Mobile robots enhance services and daily operations in diverse sectors, including healthcare, enterprises, security, agriculture, education, industries, and defense. The need for mobile robots has surged due to technology improvements and their diverse duties and services, including lifting heavy things, search and rescue operations, lunar exploration, underwater exploration, surveillance, etc. This paper reviews AI-enhanced navigation strategies for mobile robots in dynamic environments, emphasizing the distinction between various approaches. This paper also covers navigation components and types of mobile robots. This paper concludes by tabulating the research gaps and future scope of the recently reviewed literature, providing valuable insights into the state-of-the-art in the field. © 2024 IEEE.

关键词： Autonomous Robots Dynamic Environment Locomotion Navigation Static Environment

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Advancements in Chatbot technology for Enhanced Customer Support in Online Retail 9

Advancements in Chatbot Technology for Enhanced Customer Sup...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Keerthi Kumar, N. Maheswari, K. Abinaya, A. Ramya, J. Ande, Pavan Kumar Ramaian, Christu Paul Bms Institute of Technology And Management Department of Mechanical Engineering Karnataka Bengaluru560064 India Cmr Technical Campus Department of Computer Science And Engineering Telangana Hyderabad501401 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India KoneruLakshmaiah Education Foundation Department OfComputer Science And Engineering Andhra Pradesh Guntur522502 India Hindustan Institute of Technology And Science Department of Automobile Engineering Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350365092

In order to improve customer service and boost sales, this paper describes the creation of an ecommerce sales chatbot. In order to grasp natural language, the system employs machine learning. It is based on a chatbot that is modular. Framework. There are several parts to the system. A web-based framework for natural language teaching comes first. Chat bots are employed in a variety of industries, including e-commerce, healthcare, and education. The objective of this venture is to make Hebron, an online chatbot for the Contract College People group Shopping center. Python and Respond are both utilized in the chatbot's creation. Conventional customer service chatbots frequently copy human discussion, yet they have serious protection and data adaptability issues. Here, we present SuperAgent, a customer support chatbot that utilizes broad and promptly open internet business data. © 2024 IEEE.

关键词： Sales

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A Novel Approach Based on Hybrid Algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks

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computer Systems science & engineering 2022年第10期43卷 259-273页

作者： C.Ram Kumar K.Murali Krishna Mohammad Shabbir Alam K.Vigneshwaran Sridharan Kannan C.Bharatiraja Department of Electronics and Communication Engineering Dr.N.G.P.Institute of TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Vignan’s Institute of Information TechnologyDuvvadaVisakhapatnam530049India Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJizan45142Kingdom of Saudi Arabia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering JKK Munirajah College of TechnologyErode638506India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data *** clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster *** major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor *** proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster *** performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.

关键词： Wireless sensor network cluster cluster head hybrid model glowworm swarm optimization artificial bee colony algorithm energy consumption

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Edge Computing for Distributed and Cooperative Media Algorithms in Connected Networks 15

Edge Computing for Distributed and Cooperative Media Algorit...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Grace, V. Lilly Gupta, Anchal Giri, Rakesh Kumar Dhope, Tanuja Satish Vittapu, Mani Sarma Dhabliya, Dharmesh G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Kurnool India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura India Sunrise University Computer Science & Engineering Alwar India College of Engineering Electronics and Communication Maharashtra Pune India Mallareddy Engineering College and Management Sciences Kistapur Village Computer Science and Engineering Medchal Telangana Hyderabad India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350370249

Side Computing is a rising technique used for connected networks' allotted and cooperative media algorithms. Part Computing refers to processing operations being carried out near the source of the facts, generally close to the edge of the network, in place in a centralized cloud. It allows information to be processed quickly, securely, and reliably in remote or low-connectivity locations, allowing dispensed computing operations inside connected networks. This distributed machine enables latency when working with significant facts sets and enhances security and reliability. Cooperating media algorithms, including laptop vision, audio processing, or natural language processing, are frequently optimized using aspect Computing in related networks. The advantages of facet Computing allow for progressed scalability, green helpful resource utilization, and better latency performance, mainly in areas that suffer from restrained or negative networking situations and bandwidth. Moreover, aspect computing enables enhanced privacy, protection, and personal revel when handling sensitive information and data stored within the cloud. © 2024 IEEE.

关键词： Computing Distributed Handling Networking Performance Scalability

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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