This paper presents an analysis of a rules-based approach and a statistical anomaly approach to Intrusion Detection Systems (IDS). Two IDS systems are implemented. Analysis and comparisons of the systems are presented, as well as conclusions regarding the two approaches.

in this paper, an anomaly detection method in cluster-based mobile ad hoc networks with ad hoc on demand distance vector (aodv) routing protocol is proposed. in the method, the required features for describing the normal behavior of aodv are defined via step by step analysis of aodv and independent of any attack. in order to learn the normal behavior of aodv, a fuzzy averaging method is used fo...

The aim of this research is to analyze aggregate network traffic for anomaly detection. The accurate and rapid detection of network traffic anomaly is crucial to enhance the effective operation of a network. It is often difficult to detect the time when the faults occur in a network. In this paper, a new algorithm is presented to monitor the aggregate network traffic to rapidly detect the time ...

As the information technology grows interests in the intrusion detection system (IDS), which detects unauthorized usage, misuse by a local user and modification of important data, have been raised. In the field of anomaly-based IDS several artificial intelligence techniques are used to model normal behavior. However, there is no perfect detection method so that most of IDSs can detect the limit...

This paper develops parametric methods to detect network anomalies using only aggregate traffic statistics in contrast to other works requiring flow separation, even when the anomaly is a small fraction of the total traffic. By adopting simple statistical models for anomalous and background traffic in the time-domain, one can estimate model parameters in realtime, thus obviating the need for a ...

Different techniques and methods have been widely used in the subject of automatic anomaly detection in computer networks. Attacks, problems and internal failures when not detected early may badly harm an entire Network system. Thus, an autonomous anomaly detection system based on the statistical method principal component analysis (PCA) is proposed. This approach creates a network profile call...

The paper seeks to investigate the use of scalable machine learning techniques to address anomaly detection problem in a large Wi-Fi network. This was in the efforts of achieving a highly scalable preemptive monitoring tool for wireless networks. The Neural Networks, Bayesian Networks and Artificial Immune Systems were used for this experiment. Using a set of data extracted from a live network ...

Title of dissertation: Anomaly Detection in Noisy Images Xavier Gibert Serra, Ph.D. Examination, Fall 2015 Dissertation directed by: Professor Rama Chellappa Department of Electrical and Computer Engineering Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction...

Anomaly detection is the problem of identifying objects appearing to be inconstistent with the remainder of that set of data. Detecting such samples is useful on various applications such as fault detection, fraud detection and diagnostic systems. Partially supervised methods for anomaly detection are interesting because they only need data labeled as one of the classes (normal or abnormal). In...