Authorship verification is the task of determining if a given text is written by a candidate author or not. In this paper, we present a first study on using an anomaly detection method for the authorship verification task. We have considered a weakly supervised probabilistic model based on a multivariate Gaussian distribution. To evaluate the effectiveness of the proposed method, we conducted e...

Statistically resolving the underlying haplotype pair for a genotype measurement is an important intermediate step in gene mapping studies, and has received much attention recently. Consequently, a variety of methods for this problem have been developed. Different methods employ different statistical models, and thus implicitly encode different assumptions about the nature of the underlying hap...

Outlier detection is a primary step in many data-mining applications. We present several methods for outlier detection, while distinguishing between univariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for data mining is often based on dist...

We present a novel approach to the problem of detecting multivariate statistical differences across groups of data. The need to compare data in a multivariate manner arises naturally in observational studies, randomized trials, comparative effectiveness research, abnormality and anomaly detection scenarios, and other application areas. In such comparisons, it is of interest to identify statisti...

Anomaly detection is a challenging problem that has been researched within a variety of application domains such as image processing, fault isolation, fraud detection, and network intrusion detection. In network intrusion detection, anomaly based techniques are particularly attractive because of their ability to identify previously unknown attacks without the need to be programmed with the spec...

Image anomaly detection is the process of extracting a small number of clustered pixels which are different from the background. The type of image, its characteristics and the type of anomalies depend on the application at hand. In this paper, we introduce a new statistical model called noncausal autoregressive–autoregressive conditional heteroscedasticity (AR-ARCH) model for background in sona...

We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at α-false alarm level if the predicted score is in the α-percentile. The resulting an...

MAHADIK, VINAY ASHOK. Detection of Denial of QoS Attacks on DiffServ Networks. (Under the direction of Dr. Douglas S. Reeves.) In this work, we describe a method of detecting denial of Quality of Service (QoS) attacks on Differentiated Services (DiffServ) networks. Our approach focusses on real time and quick detection, scalability to large networks, and a negligible false alarm generation rate...

Problem statement: Wireless LAN IEEE 802.11 protocols are growing rapidly and security has always been a concern with the security of wired network. Wireless networks encountered threats from unauthorized access to network resources, installation of access points and illegal sniffing (refer as classical intrusion threats). In its current hybrid wired and wireless network attacks on the generall...

Information and infrastructure security is a serious issue of global concern. As the last line of defense for security infrastructure, intrusion detection techniques are paid more and more attention. In this paper, one anomalybased intrusion detection technique (ScanAID: Statistical ChAracteristics of N-grams for Anomaly-based Intrusion Detection) is proposed to detect intrusive behaviors in a ...