An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various irrelevant and redundant features and results in increased processing time and low detection rate. Therefo...

Computers, networks, programs, and data are protected from attacks unauthorized access, revision, or destruction through a set of technologies processes known as cybersecurity. Due to the prevalence sensitive information such credit card numbers, ATM leg so on, cyber security is now major concern in software industry. got addressed pasted into publicly accessible websites. As result, we selecte...

Anomaly detection is a significant research area in data science. used to find unusual points or uncommon events streams. It gaining popularity not only the business world but also different of other fields, such as cyber security, fraud for financial systems, and healthcare. Detecting anomalies could be useful new knowledge data. This study aims build an effective model protect from these anom...

Information assurance and security has been a major issue of serious global concern in the wake of rapid expansion of computer systems. Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal activities happening in a computer system. Different soft-computing based methods have been proposed in recent years for the development of intrusion detection sys...

Fuzzy Gaussian mixture modeling method is proposed in this paper for network anomaly detection. A mixture of Gaussian distributions was used to represent the network data in multi-dimensional feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the KDD Cup data set. Experimental results have shown that the proposed method is more effective...

Visualization of learning-based intrusion detection methods is a challenging problem. In this paper we propose a novel method for visualization of anomaly detection and feature selection, based on prediction sensitivity. The method allows an expert to discover informative features for separation of normal and attack instances. Experiments performed on the KDD Cup dataset show that explanations ...

Principal Component Analysis (PCA) is a ubiquitous tool with many applications in machine learning including feature construction, subspace embedding, and outlier detection. In this paper, we present an algorithm for computing the top principal components of a dataset with a large number of rows (examples) and columns (features). Our algorithm leverages both structured and unstructured random p...

People have remarkably diverse tastes in music, which reflect diversity in personalities, cultures and age groups. Recently Yahoo! Music offers a wealth of information and services related to many aspects of music, such as user ratings, which can be utilized to analyze the encoded information on how songs are grouped, which artists complement each other, and which songs users would like to list...

Supervised machine learning techniques are commonly used in many areas like finance, education, healthcare, engineering, etc. because of their ability to learn from past data. However, such can be very slow if the dataset is high-dimensional, and also irrelevant features may reduce classification success. Therefore, feature selection or reduction overcome mentioned issues. On other hand, inform...