Intrusion Detection problem is one of the most promising research issues of Information Security. The problem provides excellent opportunities in terms of providing host and network security. Intrusion detection is divided into two categories with respect to the type of detection. Misuse detection and Anomaly detection. Intrusion detection is done using rule based, Statistical, and Soft computi...

This paper presents SmartMal--a novel service-oriented behavioral malware detection framework for vehicular and mobile devices. The highlight of SmartMal is to introduce service-oriented architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfe...

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized. This paper presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning ...

PMML is an industry-standard XML-based open format for representing statistical and data mining models. Since PMML does not yet support outlier (anomaly) detection, in this paper we propose a new outlier detection model to foster interoperability in this emerging field. Our proposal is included in the PMML RoadMap for PMML 4.4. We demonstrate the proposed format on one supervised and two unsupe...

Tools required for the security purposes are firewall, passwords, IDS, IPS for the detection of anomaly and prevent it from sending out the harmful traffic to the network. So, it is very necessary to examine the behavior of traffic that coming to the network and recognize the anomalous behavior. In this paper, statistical based chi-square method is used to detect the anomalous behavior and pred...

An anomaly detection procedure based on statistical profiles of sketches of internet traffic is proposed. To validate its statistical performance, measurement campaigns were conducted to collect regular traffic as well as traffic with anomalies, on the Renater network. Anomalies were produced using real-world DDoS tools (tfn2k, trin00). The attacks target different services (ICMP, SYN, UDP, etc...

In this paper we address the problem of global unsupervised detection, discrimination, and population estimation of anomalies of the same type, in hyperspectral images. The proposed approach, denoted as Anomaly Extraction and Discrimination Algorithm (AXDA), detects anomalies via analysis of a signalsubspace obtained by the recently developed Maximum Orthogonal Complement Algorithm (MOCA). MOCA...

In this paper we address the problem of global unsupervised detection, discrimination, and population estimation of anomalies of the same type, in hyperspectral images. The proposed approach, denoted as Anomaly Extraction and Discrimination Algorithm (AXDA), detects anomalies via analysis of a signalsubspace obtained by the recently developed Maximum Orthogonal Complement Algorithm (MOCA). MOCA...

In this paper we address the problem of global unsupervised detection, discrimination, and population estimation of anomalies of the same type, in hyperspectral images. The proposed approach, denoted as Anomaly Extraction and Discrimination Algorithm (AXDA), detects anomalies via analysis of a signalsubspace obtained by the recently developed Maximum Orthogonal Complement Algorithm (MOCA). MOCA...