Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role in this area. In this research paper, we conduct a Systematic Literature Review (SLR) which analyzes ML models that detect anomalies in their application. Our review analyzes the models from four perspectives; the applications of anomaly detection, ML techniques, performance metrics for ML models, and the classification of anomaly detection. In our review, we have identified 290 research articles, written from 2000-2020, that discuss ML techniques for anomaly detection. After analyzing the selected research articles, we present 43 different applications of anomaly detection found in the selected research articles. Moreover, we identify 29 distinct ML models used in the identification of anomalies. Finally, we present 22 different datasets that are applied in experiments on anomaly detection, as well as many other general datasets. In addition, we observe that unsupervised anomaly detection has been adopted by researchers more than other classification anomaly detection systems. Detection of anomalies using ML models is a promising area of research, and there are a lot of ML models that have been implemented by researchers. Therefore, we provide researchers with recommendations and guidelines based on this review.

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Machine Learning for Anomaly Detection: A Systematic Review

INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The scenario of manual feeding of the machine is changed in the modern era, it will learn automatically. Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification. OBJECTIVES: In Computer Vision (CV), ML performs a significant role to extract crucial information from images. CV successfully contributes to multiple domains, surveillance system, optical character recognition, robotics, suspect detection, and many more. The direction of CV research is going toward healthcare realm, medical imaging (MI) is the emerging technology, play a vital role to enhance image quality and recognized critical features of binary medical image, covert original image into grayscale and set the threshold values for segmentation. CONTRIBUTION: This paper will address the importance of machine learning, state-of-the-art, and how ML is utilized in computer vision and image processing. This survey will provide details about the type of tools and applications, datasets, and techniques. Limitations of previous work and challenges of future work also discussed. Further, we identify and discuss a set of open issues yet to be addressed, for efficiently applying of ML in Computer vision and image process. METHODS, RESULTS, AND CONCLUSION: In this review paper, we have discussed the techniques and various types of supervised and unsupervised algorithms of ML, general overview of image processing and the results based on the impact; neural network enabled models, limitations, tools and application of CV, moreover, highlight the critical open research areas of ML in CV.

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Machine Learning in Computer Vision: A Review

Mitigating cyber threats through integration of feature selection and stacking ensemble learning: the LGBM and random forest intrusion detection perspective

https://robots.iopscience.iop.org/article/10.1088/1757-899X/590/1/012021/pdf

Effective Credit Card Forgery Prevention Using Multilevel Authentication

With the increased usage of the Internet, the need for providing security and privacy to protect computer networks is increased too. Network intrusion detection system (NIDS) is intended to observe and inspect the activities in a network. This system is highly dependent on the features of the input network data as these features describe the behaviour of the current network activities. Not only do the irrelevant and redundant network features cause the learning algorithm to build an inaccurate detection model, but they also increase the time complexity and exhaust computation resources as well. In this paper, several feature selection techniques are applied to boost the performance of the NIDS. Categories of the applied selection techniques are of the filter (Information Gain (IG), Principal Component Analysis (PCA), and Correlation Feature Selection (CFS)) and of the wrapper (Genetic Algorithm (GA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO)). Support vector machine (SVM) is utilized to classify the network connections. The benchmark network traffic NSL-KDD dataset is selected to build and test the NIDS. The impact of the applied selection approaches on enhancing the detection model performance is compared and discussed. Evaluation results stated that the wrapper approaches achieved better classification performance for the NIDS in terms of high classification accuracy, detection rate, true positive rates, and low false-positive rates than the filter approaches. Our ABC-NIDS is compared with other related NIDSs and the comparison result proved that our system achieved the best performance.

Filter Versus Wrapper Feature Selection for Network Intrusion Detection System

An intrusion detection system (IDS) is a product application or contraption that screens the framework or system practices for methodology encroachment or dangerous activities and makes reports to the organizational framework. The principal centralization of intrusion discovery and aversion frameworks (IDPS) is to perceive the possible events, information logging about them, and interruption tries to report. Furthermore, the associations are utilizing IDPS for different purposes, such as recognizing issues identified with approaches of security, recording, and keeping the people existing dangers from encroaching arrangements of security. In this paper, anomaly is identified utilizing enhanced correlation-based feature selection (CFS), which is basically a subset technique and is based upon extreme learning machine, multilayer perceptron, and feature selection. This project scope involves identification of anomalies in the early stages and to increase the accuracy of identification or detection.

Anomaly Detection by Using CFS Subset and Neural Network with WEKA Tools

One of the significant difficulties in network safety is the arrangement of a mechanized and viable digital danger's location strategy. This paper presents an AI procedure for digital dangers recognition, in light of fake neural organizations. The proposed procedure changes large number of gathered security occasions over to singular occasion profiles and utilize a profound learning-based discovery strategy for upgraded digital danger identification. This research work develops an AI-SIEM framework dependent on a blend of occasion profiling for information preprocessing and distinctive counterfeit neural organization techniques by including FCNN, CNN, and LSTM. The framework centers around separating between obvious positive and bogus positive cautions, consequently causing security examiners to quickly react to digital dangers. All trials in this investigation are performed by creators utilizing two benchmark datasets (NSLKDD and CICIDS2017) and two datasets gathered in reality. To assess the presentation correlation with existing techniques, tests are carried out by utilizing the five ordinary AI strategies (SVM, k-NN, RF, NB, and DT). Therefore, the exploratory aftereffects of this examination guarantee that our proposed techniques are fit for being utilized as learning-based models for network interruption discovery and show that despite the fact that it is utilized in reality, the exhibition beats the traditional AI strategies.

Identification of Cyber Threats and Parsing of Data

Abstract With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.

IoT forensics: What kind of personal data can be found on discarded, recycled, or re-sold IoT devices

The advancement of technology offers solution to the complex problems faced by the society and brings the wellbeing of the individuals. Smart healthcare is prominent nowadays for diagnosis, treatment and constant monitoring which reduces visitation of hospital, transport cost and waiting time. Voice pathology is a decease which affects the person vocal cord so that one who facing difficult in speech. If the decease not identified in time, it leads to permanent loss of voice for an individual. Traditionally, the decease is identified through oral examination or manual procedures. Due to the advent of smart phone, one can record the voice and send it to the cloud server for processing. Our system classifies the voice data and provides the decision to the user. This greatly reduces the transport cost, and waiting time for oral examination at medical center. The mobile phone recorded the patient voice data and it will be stored into the cloud. The voice data is synthesized to signals and with the help of deep Neural network the voice pathology can be identified. The system has been tested with the data set and the test result shows promising.

Voice Pathology Detection Based on Deep Neural Network Approach

A myocardial localized necrosis (also known as a stroke) refers to a tissue damage in the brain due to the lack of oxygen in the specified area. An ischemic stroke is caused by a lack of oxygen that arises due to a constricted blood supply; this condition might result in developing dead tissue if the blood supply is not restored in a relatively short period of time. Around 33% of the cases end up being lethal. To overcome this challenge, this research work has proposed a novel framework called Logistic relapse, an AI relapse calculation that is utilized to distinguish the myocardial localized necrosis in a patient. The calculation is prepared by utilizing an organized clinical dataset. By precisely distinguishing the myocardial localized necrosis in a patient, this effort will be remarkable in the clinical field.

S. Gowri

Papers

Anomaly Detection by Using CFS Subset and Neural Network with WEKA Tools

Identification of Cyber Threats and Parsing of Data

IoT forensics: What kind of personal data can be found on discarded, recycled, or re-sold IoT devices

Voice Pathology Detection Based on Deep Neural Network Approach

Analysis and Prediction of Myocardial Infarction using Machine Learning