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20 articles for “KDD”
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Intrusion Detection System with Machine Learning Algorithms
Abstract: Machine learning has become increasingly relevant in recent years, including in IT security. Algorithms are used to train intrusion detection systems to be able to react to new attack vectors. In this work, the basics of machine learning are explained and the results of two research projects are presented in order to investigate which algorithms are suitable for training a machine-learning intrusion detection system. In addition, the software library Scikit-Learn …
Published in Journal of Advancements in Robotics · Vol. 10, Issue 3, 2023 · pp. 28–37 Read article
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Intrusion Detection Using ANN Machine Learning for MIM, DOS, BO
Abstract: Intrusion detection system is a software program developed to use on computer systems so that it can identify intrusion attack with help of different techniques like the machine learning algorithms. The variety of assaults over the internet has multiplied through the years because of the development and smooth availability of computing technologies. Attackers develop new attack types, so in order to save you from those assaults, intrusion detection systems must …
Published in Journal Of Network security Read article
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Intrusion Detection Using ANN Machine Learning for MIM, DOS, BO
Abstract: Intrusion detection system is a software program developed to use on computer systems so that it can identify intrusion attack with help of different techniques like the machine learning algorithms. The variety of assaults over the internet has multiplied through the years because of the development and smooth availability of computing technologies. Attackers develop new attack types, so in order to save you from those assaults, intrusion detection systems must …
Published in Journal Of Network security · Vol. 10, Issue 1, 2022 · pp. 46–53 Read article
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A Review Paper Based on Methodologies and Applications of Data Mining
Abstract: Data mining is like extracting record from a heap of data. Understanding database concepts is vital for data mining. Knowledge Discovery in Databases (KDD) is used to discover crucial data-related information. Our motive is being inundated with exceptional types of information science statistics, environmental records, financial records, and mathematical records. Summarizing the information manually is not possible because of the notable data boom inside the age of community and records …
Published in Recent Trends in Parallel Computing · Vol. 9, Issue 3, 2022 · pp. 8–15 Read article
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Analysis of Various Diabetes Prediction Techniques Based on Machine Learning
Abstract: AbstractData mining is a method of mine from the underdone data for maintains useful information. In organize to obtain fundamental knowledge it is important to remove bulky quantity of data. This method is consist of many important components like data cleaning, data selection, data integrity, pattern evaluation with data mining engine and database with graphical user interface. Diabetes is a chronic disease that is also known as Non‐Insulin Dependent Diabetes …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 8, Issue 1, 2021 · pp. 17–22 Read article
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Predicting the Computer Access Needs of Employee Using Data Mining Algorithms
Abstract: Every business relies on data to better organize and access the information to improve their business operations and to identify opportunities for improvement. A better framework improves the ability to valuable products and better services to their customers. The wide swaths of data that every organization gathers requires a proper methodology to gain knowledge about the latest trends in the market. Data mining is the process that analyses the data …
Published in Journal of Advances in Shell Programming · Vol. 10, Issue 1, 2023 · pp. 12–16 Read article
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An Intelligent Gray Wolf Optimizer: A Nature Inspired Technique in Intrusion Detection System (IDS)
Abstract: AbstractFeature choice algorithmic program investigates the knowledge to get rid of creaking, irrelevant, overabundance information, and everyone the while optimizes the classification performance. In this work, an intelligent grey wolf optimization (GWO) technique is used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) is one of the most recent bio-enlivened optimization strategies, which impersonate the authority pecking order and chasing system of gray wolves in nature. …
Published in Journal of Advancements in Robotics · Vol. 6, Issue 1, 2019 · pp. 18–24 Read article
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A Study on Privacy Preserving Techniques in Data Mining
Abstract: Data Mining (DM) is the technique of examining data from specific summarizing and perspectives the outcome as useful information. It has been describing as "the non-trivial procedure of new, potentially helpful and ultimately conceivable patterns and identifying valid in data". The word “Knowledge” in KDD submits to the discovery of example which is removing since the processed data. Preserving privacy is becoming a key apprehension as personal data is publicly …
Published in Journal of Operating Systems Development & Trends · Vol. 5, Issue 2, 2018 · pp. 18–23 Read article
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Data Mining Made Simple by A priori Algorithm for Market Analysis
Abstract: Data mining and data warehousing are increasingly becoming popular among IT professionals, academics and researchers from different disciplines. Business enterprises, small, medium or large scale, are considering the deployment of a warehouse as a major step and as a matter of pride. Data mining or knowledge discovery in data bases combines the techniques from mathematics, statistics, algorithms and artificial intelligence to extract the knowledge. Data mining is a main phase …
Published in Current Trends in Information Technology · Vol. 3, Issue 3, 2013 · pp. 1–4 Read article
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Advancements in Intrusion Detection: Tackling Imbalanced Network Traffic with Machine Learning and Deep Learning Techniques
Abstract: Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 18–24 Read article
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A Survey of Several Machine Learning (ML) Algorithms for Security Solution in Internet of Things (IoT) Networks
Abstract: The Internet of Things (IoT) refers to the integration of physical objects with the Internet, allowing for connectivity and monitoring. This idea has garnered immense attention from researchers and users alike, driven by the widespread accessibility of the Internet. It spans a wide range of devices, including smart versions of conventional appliances, innovative tools tailored for Internet-enabled ecosystems, and sensors that leverage connectivity to revolutionize industries such as manufacturing, healthcare, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 1–11 Read article
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DDoS Detection Using Cascade Correlation for Improving Network Resources in Cloud Environment
Abstract: Intrusion detection is critical for protecting network security from emerging cyber threats. This study describes a unique intrusion detection system (IDS) based on the Random Forest algorithm. Random Forests are used as an effective classifier to identify patterns linked with malevolent behaviour. This technique uses Random Forests to improve the accuracy and efficiency of intrusion detection systems. The suggested methodology's value is shown by its performance on the benchmark KDD …
Published in International Journal of Wireless Security and Networks · Vol. 3, Issue 2, 2025 · pp. 17–22 Read article
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Convolution Neural Network Model for Intrusion Detection in Network
Abstract: The evolution of the internet has made protecting information a necessity. Network intrusion and prevention plays an integral role in network-based security. The Intrusion technologies primarily used in today’s world deploy various machine learning algorithms and train models based on them resulting in effectively low detection rates. A technical advancement from machine learning, Deep Learning employs complex mechanisms to extract features from samples. As observed that conventional intrusion detection systems …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 8, Issue 1, 2021 · pp. 7–13 Read article
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Evaluation of Ensemble and Deep Learning Classifiers on CSE-CIC-IDS2018 Dataset for Intelligent NIDS
Abstract: Network Intrusion Detection System (NIDS) plays an active role in preventing cyberattacks by early detection of threats before it really starts affecting targeted information services. Over the years, many intrusion detection system (IDS) have been developed applying signature or rule-based approach to prevent unauthorised access of network or computer devices. However, ever growing landscape of cyberattacks in recent years has motivated present day researchers to design and develop more accurate …
Published in Current Trends in Information Technology · Vol. 13, Issue 1, 2023 · pp. 1–11 Read article
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Information Technology: An Arising Concept in Agriculture Sector
Abstract: AbstractThe agriculture sector plays predominant role in economy of India. Information Technology is an important branch in the field of agriculture as it provides support to the farming community. Agriculture is the backbone of agro industries. IT contributing one third to the national GDP in the Indian economy. Under the changing dynamics of economical and industrial growth, agriculture has to experience changes with new approaches. The agricultural sector has to …
Published in Journal of Computer Technology & Applications · Vol. 4, Issue 1, 2013 · pp. 23–27 Read article
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Research Survey of Data Mining with Processing in Different fields for Next Research Directions
Abstract: Data mining (DM) is a method used for mines the different category of related data in different research fields. This is a set of different techniques like classification, clustering, prediction, outlier analysis; decision making, regression etc used for gain the knowledge for intelligence computing. In this review, explain the different research papers, architecture with components of DM, processing of DM and applications of DM in various fields for next research …
Published in Journal of Computer Technology & Applications · Vol. 11, Issue 2, 2020 · pp. 5–9 Read article
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Towards a Machine Learning Oriented Expert System for Intrusion Detection Model
Abstract: AbstractIn this paper, we describe the possibility of using machine learning and expert system for constructing a new intrusion detection model. The first main idea is to construct the classifier on the KDD intrusion data using a machine learning algorithm. For constructing the classifier J48 decision tree machine learning algorithm is used. The other key idea is to collect domain expert knowledge, and building an expert system to interpret and …
Published in Journal Of Network security · Vol. 8, Issue 3, 2020 · pp. 24–30 Read article
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Design and Implementation of Security System for Medical Test Data Mining
Abstract: The growth of data mining process began when company data was first stored on computers, unrelenting with improvements in data access. Data mining deals with bulky database which may hold insightful information. Medical test security system is a major factor in our society especially this system is used for special purpose like inspection of many healthy man powers for a specific organization. In this project software is implement which is …
Published in Journal of Advanced Database Management & Systems · Vol. 1, Issue 1, 2014 · pp. 9–12 Read article
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Data Mining Techniques Used in Prediction of Heart Diseases
Abstract: AbstractHeart disease is a big life minatory ailment that cause to demise. It has deep long-term incompetence. There is a huge amount of data available within our system. Nevertheless, we are unable to find hidden relationship and prosperity in data. KDD (knowledge discovery in database) process, data mining consists of various techniques to convert these mounds of data into useful decision-making information. Data mining takes less time for the prediction …
Published in Journal of Advanced Database Management & Systems · Vol. 6, Issue 3, 2019 · pp. 9–11 Read article
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Voting Classification Method for Network Traffic Prediction
Abstract: Prediction analysis (PA) is a data mining-based technique. The futuristic outcomes based on present data can be predicted using this technique. As the dataset is large and complex, network traffic classification is a big concern in prediction analysis. Three phases are included in network traffic strategies. The data collection is obtained in the first step of pre-processing, and it is analysed to delete incomplete and obsolete values. The relationship between …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 8, Issue 1, 2021 · pp. 23–30 Read article