Search results for "Anomaly detection"
showing 10 items of 82 documents
Semantic anomaly detection in school-aged children during natural sentence reading : A study of fixation-related brain potentials
2018
In this study, we investigated the effects of context-related semantic anomalies on the fixation-related brain potentials of 12–13-year-old Finnish children in grade 6 during sentence reading. The detection of such anomalies is typically reflected in the N400 event-related potential. We also examined whether the representation invoked by the sentence context extends to the orthographic representation level by replacing the final words of the sentence with an anomalous word neighbour of a plausible word. The eye-movement results show that the anomalous word neighbours of plausible words cause similar first-fixation and gaze duration reactions, as do other anomalous words. Similarly, we obser…
Learning Temporal Regularities of User Behavior for Anomaly Detection
2001
Fast expansion of inexpensive computers and computer networks has dramatically increased number of computer security incidents during last years. While quite many computer systems are still vulnerable to numerous attacks, intrusion detection has become vitally important as a response to constantly increasing number of threats. In this paper we discuss an approach to discover temporal and sequential regularities in user behavior. We present an algorithm that allows creating and maintaining user profiles relying not only on sequential information but taking into account temporal features, such as events' lengths and possible temporal relations between them. The constructed profiles represent …
Recent progress on Frequency Difference Electrical Impedance Tomography
2009
Although time-dierence EIT(tdEIT) has shown promise as a medical EIT imaging tech- nique such as monitoring lung function, static EIT has suered from forward computational model errors including boundary geometry and electrode positions uncertainty combined with the ill-posed and highly nonlinear nature of the corresponding inverse problem. Since 1980s, there has been great endeavor to create forward computational models with the necessary accuracy required for EIT recon- struction, but these eorts were not successful in clinical environment. This is the main reason why we consider frequency-dieren ce EIT (fdEIT) where we take advantage of frequency dependance of biological tissue by inject…
Securing AODV routing protocol against black hole attack in MANET using outlier detection scheme
2017
Imposing security in MANET is very challenging and hot topic of research science last two decades because of its wide applicability in applications like defense. Number of efforts has been made in this direction. But available security algorithms, methods, models and framework may not completely solve this problem. Motivated from various existing security methods and outlier detection, in this paper novel simple but efficient outlier detection scheme based security algorithm is proposed to protect the Ad hoc on demand distance vector (AODV) reactive routing protocol from Black hole attack in mobile ad hoc environment. Simulation results obtained from network simulator tool evident the simpl…
Classification of SD-OCT Volumes for DME Detection: An Anomaly Detection Approach
2016
International audience; Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binar…
Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains
2021
This article proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labeled training data. The proposed method combines a one-class support vector machine (SVM) based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the…
Growing Hierarchical Self-organizing Maps and Statistical Distribution Models for Online Detection of Web Attacks
2013
In modern networks, HTTP clients communicate with web servers using request messages. By manipulating these messages attackers can collect confidential information from servers or even corrupt them. In this study, the approach based on anomaly detection is considered to find such attacks. For HTTP queries, feature matrices are obtained by applying an n-gram model, and, by learning on the basis of these matrices, growing hierarchical self-organizing maps are constructed. For HTTP headers, we employ statistical distribution models based on the lengths of header values and relative frequency of symbols. New requests received by the web-server are classified by using the maps and models obtaine…
Semi-supervised deep learning-driven anomaly detection schemes for cyber-attack detection in smart grids
2022
Modern power systems are continuously exposed to malicious cyber-attacks. Analyzing industrial control system (ICS) traffic data plays a central role in detecting and defending against cyber-attacks. Detection approaches based on system modeling require effectively modeling the complex behavior of the critical infrastructures, which remains a challenge, especially for large-scale systems. Alternatively, data-driven approaches which rely on data collected from the inspected system have become appealing due to the availability of big data that supports machine learning methods to achieve outstanding performance. This chapter presents an enhanced cyber-attack detection strategy using unlabeled…
Time Series Corrections and Analyses in Thermal Remote Sensing
2013
The time span of surface thermal data bases now reaches a few decades. However, studies using surface thermal time series are seldom, due to the difficulty of obtaining temporally coherent estimations for this parameter. Applications for surface thermal multitemporal analysis range from climate change studies and modeling to anomaly detection for natural or industrial hazard detection. This chapter presents methods to improve the temporal coherence of temperature time series, through data reconstruction of atmospheric and cloud contaminated observations, and through the correction of the orbital drift effect which hinders the use of the longest data sets. Then, methods for the analysis of t…
Detection of Anomalous HTTP Requests Based on Advanced N-gram Model and Clustering Techniques
2013
Nowadays HTTP servers and applications are some of the most popular targets for network attacks. In this research, we consider an algorithm for HTTP intrusions detection based on simple clustering algorithms and advanced processing of HTTP requests which allows the analysis of all queries at once and does not separate them by resource. The method proposed allows detection of HTTP intrusions in case of continuously updated web-applications and does not require a set of HTTP requests free of attacks to build the normal user behaviour model. The algorithm is tested using logs acquired from a large real-life web service and, as a result, all attacks from these logs are detected, while the numbe…