An Anomaly Detector for Keystroke Dynamics Based on Medians Vector Proximity (original) (raw)
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Detection of anomalies in the data has now emerged as a hot topic among diverse areas for researchers, especially in the cyber security field. Also, it is one of the important aspects of risk management strategies. A broad range of strategies for protecting against these anomalies have been developed in the literature, however certain gaps remain, necessitating the creation of robust detection systems. This article provides a compendium of some anomaly detection techniques explored by the researchers.
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Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and m...
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In our world of growing machine intelligence and increasing security risks, there is a dire need for authentication to be liberated from password dependency and restrictions. This paper discusses the implementation of keystroke biometrics to enhance security using machine-learning algorithms on both Windows and Android. Our research analyzes a user's behavior for authorization purposes by capturing the user's typing pattern. The system extracts several features from the user's typing pattern to apply unary classification for user behavior analysis so that we can detect unauthorized users. Our system implements machine learning on tap dynamics in Android, allowing both training and prediction and overcoming its computational restrictions.
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into diAEerent categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to diAEerentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the eAEectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the diAEerent existing techniques in that category are variants of the basic technique. This template provides an easier and succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the diAEerent directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
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An outlier is a data observation that is considerably irregular from the rest of the dataset. The outlier present in the dataset may cause the integrity of the dataset. Implementing machine learning techniques in various real-world applications and applying those techniques to the healthcare-related dataset will completely change the particular field's present scenario. These applications can highlight the physiological data having anomalous behavior, which can ultimately lead to a fast and necessary response and help to gather more critical knowledge about the particular area. However, a broad amount of study is available about the performance of anomaly detection techniques applied to popular public datasets. But then again, have a minimal amount of analytical work on various supervised and unsupervised methods considering any physiological datasets. The breast cancer dataset is both a universal and numeric dataset. This paper utilized and analyzed four machine learning techniques and their capacity to distinguish anomalies in the breast cancer dataset.
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Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
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The day by day targeted network attacks is steadily increasing and evolving, forcing businesses to revamp their network security systems due to possible data and capital losses. Intrusion Detection Systems is a very important element for almost any security system. The key feature of IDS is the active detection of unauthorized access that tries to compromise the confidentiality, availability and Integrity of computer or computer networks. Many researchers have already developed security and advanced techniques to explore technologies to detect cyber attacks with all DARPA 1998 dataset for Intrusion Detection and improved versions of this KDD Cup'99, NSL-KDD Cup and GureKDDcup data set. In this research, we evaluate the use of five ML classification algorithm to deal with the attack classification problem. They are SVM, Naive Bayes, KNN and the Decision Tree based C4.5 (J48) and Random Forest Algorithm. The project objective is to compare if some of the newer dataset and the most...