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A novel cyber intrusion detection model based on improved hybrid sampling

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第15期45卷 2903-2913页

作者： Qian, Hong Zhang, Xiantao Zhang, Chaofan Jiang, Cheng Shanghai Univ Elect Power Sch Automat Engn Shanghai 200090 Peoples R China Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China Agr Bank China Data Ctr Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

The class imbalance of samples of network traffic will cause the poor classification performance of intrusion detection models based on machine learning. To solve this problem, this paper researches sampling algorithm and deep learning for intrusion detection in imbalanced network traffic. This paper proposes a deep recurrent neural network (delayed long short-term memory (DLSTM)) intrusion detection model based on the balanced samples. First, an improved hybrid sampling (IHS) method based on chaotic particle swarm optimization (CPSO) algorithm is proposed as the sampling algorithm to balance the imbalanced samples. Next, a DLSTM with long short-term memory (LSTM) function is proposed to realize high-precision classification of intrusion behaviours. Finally, the method is validated on the standard network traffic dataset. The experimental results show that the DLSTM intrusion detection model based on the IHS method outperforms other comparative models at accuracy. The model is available to the computer network information security defence system.

关键词： Intrusion detection sample imbalance improved hybrid sampling deep learning classification algorithm

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Identification of a Hidden movement by Using LLCPT algorithm

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NATIONAL ACADEMY SCIENCE LETTERS-INDIA 2025年第3期48卷 333-335页

作者： Sharma, Pharindra Kumar Sahu, Neeraj Sahu, Swatantra Kumar Jain, Neha Res Scientist PRAA Robot Res & Dev Labs Gwalior Madhya Pradesh India G H Raisoni Univ Dept Comp Sci & Engn Amravati Maharashtra India Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyala Dept Phys Sci Satna Madhya Pradesh India

In this paper, proposed an algorithm with the name LLCPT, that will trace out the offender/offenders related to a crime. The idea used in this algorithm takes into consideration that, almost everybody uses mobile phones now a days be it basic phone or smart phone. This leads us to notice that, offender/s too may use or carry a mobile phone at or near the site of crime. The LLCPT algorithm works on the principle of earth position and calculate path of mobile.

关键词： Latitude Longitude GPS Mobile technology classification algorithm

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Leveraging Artificial Intelligence to Reduce Human-Animal Conflict in India

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COMMUNICATIONS OF THE ASSOCIATION FOR INFORMATION SYSTEMS 2024年 55卷 336-350页

作者： Pathak, Atul Arun Kaushik, Kapil Gawande, Abhishek Indian Inst Management Strategy & Entrepreneurship Nagpur India Indian Inst Management Decis Sci & Informat Syst Nagpur India

The case focuses on the use of artificial intelligence (AI) and computer vision (CV) technologies to solve a socially complex issue of human-animal conflict in the geographical regions surrounding the Tadoba Andhari Tiger Reserve (TATR) in Maharashtra state in central India. Due to an increasing tiger population, the villages surrounding the TATR often encountered tigers that strayed outside the reserve, sometimes leading to injuries or fatalities either to the humans or the wild animals involved. Valiance, a technology company, collaborated with the Forest department and other local stakeholders to create a computer vision based AI virtual wall system as a pilot project. The technology solution monitored the location 24hrs a day 7 days a week and had the capability of identifying tigers, leopards, and other predators, and sending out automated, real time alerts to help prevent human-animal conflict situations. Based on the outcome of the pilot, the solution could be refined and then a wider rollout could be planned.

关键词： Artificial Intelligence Human-Animal Conflict Mitigation Real-time Mobile Alerts Computer Vision classification algorithm

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DBRF: Random Forest Optimization algorithm Based on DBSCAN

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第9期15卷 354-362页

作者： Zhuo, Wang Ahmad, Azlin Univ Teknol MARA Coll Comp Informat & Math Sch Comp Sci Shah Alam Malaysia

correlation and redundancy of features will directly affect the quality of randomly selected features, weakening the convergence of random forests (RF) and reducing the performance of random forest models. This paper introduces an improved random forest algorithm-A Random Forest algorithm Based on DBSCAN (DBRF). The algorithm utilizes the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to improve the feature extraction process, to extract a more efficient feature set. The algorithm first uses DBSCAN to group all features based on their relevance and then selects features from each group in proportion to construct a feature subset for each decision tree, repeating this process until the random forest is built. The algorithm ensures the diversity of features in the random forest while eliminating the correlation and redundancy among features to some extent, thereby improving the quality of random feature selection. In the experimental verification, the classification prediction results of CART, RF, and DBRF, three different classifiers, were compared through ten-fold cross-validation on six different-sized datasets using accuracy, precision, recall, F1, and running time as validation indicators. Through experimental verification, it was found that DBRF algorithm outperformed RF, and the prediction performance was improved, especially in terms of time complexity. This algorithm is suitable for various fields and can effectively improve the classification prediction performance at a lower complexity level.

关键词： Random forest DBSCAN feature selection feature redundancy classification algorithm

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Classifying for interval and applying for image based on the extracted texture feature

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GRANULAR COMPUTING 2024年第2期9卷 1-16页

作者： Nguyen-Thihong, Dan Vo-Van, Tai Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HCMUT Fac Appl Sci Ho Chi Minh City Vietnam Can Tho Univ Coll Nat Sci Can Tho Vietnam

This study develops a classification algorithm designed to handle interval data and to apply effectively in image processing. The proposed algorithm utilizes an innovative measure called overlap distance to assess the similarity between two intervals within multidimensional space. In addition, it integrates an improved method for determining prior probabilities by employing a fuzzy clustering technique. Furthermore, the study introduces a classification rule based on the quasi-Bayes method specifically tailored for interval data. According to this rule, an interval is assigned to a particular group if it holds the highest prior probability and the minimum distance to that group. The proposed algorithm is systematically presented in a step-by-step manner, elucidated by a numerical example, and executed using a well-established Matlab procedure. Another significant contribution of this study is its application to images, wherein texture features are extracted and represented as two-dimensional intervals. The effectiveness and superiority of the proposed algorithm are demonstrated through its application to various sets of medical images.

关键词： Interval data classification algorithm Prior probability Texture feature of image

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Performance analysis of the water quality index model for predicting water state using machine learning techniques

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 169卷 808-828页

作者： Uddin, Md Galal Nash, Stephen Rahman, Azizur Olbert, Agnieszka I. Univ Galway Sch Engn Galway Ireland Univ Galway Ryan Inst Galway Ireland Univ Galway MaREI Res Ctr Galway Ireland Charles Sturt Univ Sch Comp Math & Engn Wagga Wagga Australia Charles Sturt Univ Gulbali Inst Agr Water & Environm Wagga Wagga Australia Univ Galway Coll Sci & Engn Civil Engn Galway Ireland

Existing water quality index (WQI) models assess water quality using a range of classification schemes. Conse-quently, different methods provide a number of interpretations for the same water properties that contribute to a considerable amount of uncertainty in the correct classification of water quality. The aims of this study were to evaluate the performance of the water quality index (WQI) model in order to classify coastal water quality correctly using a completely new classification scheme. Cork Harbour water quality data was used in this study, which was collected by Ireland's environmental protection agency (EPA). In the present study, four machine -learning classifier algorithms, including support vector machines (SVM), Naive Bayes (NB), random forest (RF), k-nearest neighbour (KNN), and gradient boosting (XGBoost), were utilized to identify the best classifier for predicting water quality classes using widely used seven WQI models, whereas three models are completely new and recently proposed by the authors. The KNN (100% correct and 0% wrong) and XGBoost (99.9% correct and 0.1% wrong) algorithms were outperformed in predicting the water quality accurately for seven WQI models. The model validation results indicate that the XGBoost classifier outperformed, including accuracy (1.0), pre-cision (0.99), sensitivity (0.99), specificity (1.0), and F1 (0.99) score, in order to predict the correct classification of water quality. Moreover, compared to WQI models, higher prediction accuracy, precision, sensitivity, speci-ficity, and F1 score were found for the weighted quadratic mean (WQM) and unweighted root mean square (RMS) WQI models, respectively, for each class. The findings of this study showed that the WQM and RMS models could be effective and reliable for assessing coastal water quality in terms of correct classification. Therefore, this study could be helpful in providing accurate water quality information to researchers, policy -makers, and water researc

关键词： Water quality index Coastal water quality classification Model uncertainty classification algorithm Cork Harbour

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A recursive method for the construction and enumeration of self-orthogonal and self-dual codes over the quasi-Galois ring F2r[u]/<u^e >

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DESIGNS CODES AND CRYPTOGRAPHY 2023年第5期91卷 1973-2003页

作者： Yadav, Monika Sharma, Anuradha IIIT Delhi Dept Math New Delhi 110020 India

In this paper, we provide a recursive method to construct self-orthogonal and self-dual codes of the type {k(1), k(2), ... , k(e)} and length n over the quasi-Galois ring F(2)r[u]/ < u(e) > from a self-orthogonal code of the same length n and dimension k(1) + k(2) + middot middot middot + k([e/2])over F(2)r and vice versa, where F2r is the finite field of order 2r, n > 1, e > 2 are integers, [e2] is the smallest integer greater than or equal to e/2, and k(1), k(2), . . . , k([e/2])are non-negative integers satisfying k(1) <= n - (k(1) + k(2) + middot middot middot + k(e)) and k(i) = k(e-i+2) for 2 <= i <= e. We further apply this recursive method to provide explicit enumeration formulae for self-orthogonal and self-dual codes of an arbitrary length over the ring F(2)r[u]/ < u(e) >. With the help of these enumeration formulae and by carrying out computations in the Magma Computational Algebra system, we classify all self-orthogonal and self-dual codes of lengths 2, 3, 4, 5 over the ring F-2[u]/ < u(3) > and of lengths 2, 3, 4 over the ring F-4[u]/ < u(2) >.

关键词： Linear codes Witt decomposition classification algorithm Chain rings

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Water quality prediction and classification based on principal component regression and gradient boosting classifier approach

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第8期34卷 4773-4781页

作者： Khan, Md. Saikat Islam Islam, Nazrul Uddin, Jia Islam, Sifatul Nasir, Mostofa Kamal Dept Comp Sci & Engn Santosh 1902 Tangail Bangladesh Dept Informat & Commun & Technol Santosh 1902 Tangail Bangladesh Woosong Univ Endicott Coll Dept Technol Studies Daejeon South Korea Mawlana Bhashani Sci & Technol Univ Santosh 1902 Tangail Bangladesh Mawlana Bhashani Sci & Technol Univ Dept Informat & Commun & Technol Santosh 1902 Tangail Bangladesh

Estimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression tech-nique. Firstly, the water quality index (WQI) is calculated using the weighted arithmetic index method. Secondly, the principal component analysis (PCA) is applied to the dataset, and the most dominant WQI parameters have been extracted. Thirdly, to predict the WQI, different regression algorithms are used to the PCA output. Finally, the Gradient Boosting Classifier is utilized to classify the water quality status. The proposed system is experimentally evaluated on a Gulshan Lake-related dataset. The results demonstrate 95% prediction accuracy for the principal component regression method and 100% classification accuracy for the Gradient Boosting Classifier method, which show credible performance compared with the state -of-art models. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Water quality index Principal component regression classification algorithm Boxplot analysis

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Effective classification algorithms and Feature Selection for Bio-Medical Data using IoT 7

Effective Classification Algorithms and Feature Selection fo...

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7th International Conference on Information Technology Trends (ITT)

作者： Masood, Junaid Shahzad, Muhammad Khan, Zahoor Ali Akre, Vishesh Rajan, Amala Ahmed, Sheeran Masood, Fawad Iqra Natl Univ Peshawar Peshawar Pakistan MNS Univ Engn & Technol Multan Pakistan Career Dynam Res Ctr Peshawar Pakistan Higher Coll Technol CIS Fujairah U Arab Emirates Higher Coll Technol Dubai U Arab Emirates Inst Space Technol Islamabad Pakistan

ISBN: (纸本)9781728183787

Data mining is the process to predict future trends. Data mining involving searching of patterns whose general purpose is to extract information using intelligent methods from a set of data and information turn it into an understandable structure for later use. Data mining using IoT becomes one of the leading providers applicable in several areas. The method of extracting usable data from larger set of arbitrary raw data is called data mining. This includes the analysis of sample data in large amounts of data with one or more programs. Concentrate on large amounts of data and databases to analyze, we use predictive analysis in the field of medicine. In this study, we examine the performance of algorithms alongwith the use of IoT that analyses large amounts of scattered information to make sense of it and turn it into knowledge. For this we used feature selection and classification algorithm on hepatitis data to get best result with minimum error rate and compare feature selection with classification algorithm so that to check which method is best according to results.

关键词： Data Mining classification algorithm IoT Feature Selection algorithm Error rate

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Color Naming for Describing Object in Image using Different classification algorithms and Color Spaces 2020

Color Naming for Describing Object in Image using Different ...

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12th International Conference on Computer and Automation Engineering (ICCAE)

作者： Sainui, Janya Tongsamrit, Mananya Prince Songkla Univ Fac Sci Dept Comp Sci Hat Yai Thailand

ISBN: (纸本)9781450376785

In this paper, we study to name a color of an object in the given image, where the number of color names is 11 English basic color names, including pink, purple, red, orange, yellow, green, blue, gray, brown, black, and white. Our goal is to apply machine learning algorithms for assigning a color name to a principal object in images. Here, we evaluate three basic classification algorithms, namely Support Vector Machine (SVM), naive Bayes, and k-Nearest Neighbor (k-NN). The feature vector of the object in an image is the pixel intensities represented in different color spaces including RGB, HSV, Lab and CYMK. We randomly collect the training pixel intensities with their corresponding color names from the images obtained from the Internet. We then evaluate three classification algorithms using the e-Bay dataset. The experimental results show that the linear SVM with Lab color space is the best choice for solving this task.

关键词： Color naming classification algorithm support vector machine k-nearest neighbor naive Bayes color spaces

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