Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labeled training data. This training data is 90 typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. In this paper, we have discussed anomaly based instruction detection, pros and cons of anomaly detection, supervised and...

Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support effort. This study investigates the use of a supervised machine learning model, Multi-Layer Perceptron (MLP), for anomaly detection in microservices. The covers creation microservices infrastructure, development fault injection module...

The identification of anomalies is a critical component operating complex, and possibly large-scale geo-graphically distributed cyber-physical systems. While designing anomaly detectors, it common to assume Gaussian noise models maintain tractability; however, this assumption can lead the actual false alarm rate being significantly higher than expected. Here we design distributionally robust th...

Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses...

Audit sequences have been used effectively to study process behaviors and build host-based intrusion detection models. Most sequencebased techniques make use of a pre-defined window size for scanning the sequences to model process behavior. In this paper, we propose two methods for extracting variable length patterns from audit sequences that avoid the necessity of such a pre-determined paramet...

anomaly detection (ad) has recently become an important application of target detection in hyperspectral images. the reed-xialoi (rx) is the most widely used ad algorithm that suffers from “small sample size” problem. the best solution for this problem is to use dimensionality reduction (dr) techniques as a pre-processing step for rx detector. using this method not only improves the detection p...

The exponential growth in wireless network faults, vulnerabilities, and attacks make the WLAN security management a challenging research area [29]. Data mining applied to intrusion detection is an active area of research. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require proces...

Detecting online abnormality in the video surveillance is a challenging issue due to streaming, video noise, outliers and resolution. Traditional trajectory based anomaly detection model which analyzes the video training patterns for anomaly detection. This paper aims to solve the problem of video noise and anomaly detection .In this paper, a novel filtered based ensemble clustering and classif...

Anomaly detection in networks is detection of deviations from what is considered to be normal. Performing anomaly detection is a learning approach to detect failures and intrusions in a network, intended to capture novel attacks. Anomaly Detection With fast Incremental Clustering (ADWICE) is an efficient algorithm to detect anomaly. But, since it uses distance based clustering mechanism it suff...