Search results for " Detection"
showing 10 items of 1676 documents
Privacy Violation Classification of Snort Ruleset
2010
Published version of a paper presented at the 2010 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Paper also available from the publisher:http://dx.doi.org/10.1109/PDP.2010.87 It is important to analyse the privacy impact of Intrusion Detection System (IDS) rules, in order to understand a…
A roadmap towards improving managed security services from a privacy perspective
2014
Published version of an article in the journal: Ethics and Information Technology. Also available from the publisher at: http://dx.doi.org/10.1007/s10676-014-9348-3 This paper proposes a roadmap for how privacy leakages from outsourced managed security services using intrusion detection systems can be controlled. The paper first analyses the risk of leaking private or confidential information from signature-based intrusion detection systems. It then discusses how the situation can be improved by developing adequate privacy enforcement methods and privacy leakage metrics in order to control and reduce the leakage of private and confidential information over time. Such metrics should allow fo…
A linear matrix inequality approach to robust fault detection filter design of linear systems with mixed time-varying delays and nonlinear perturbati…
2010
Accepted version of an article in the journal: Journal of the Franklin Institute-Engineering and Applied Mathematics. The definitive version can be found on Sciverse: http://dx.doi.org/10.1016/j.jfranklin.2010.03.004 In this paper, the problem of robust fault detection filter (RFDF) design for a class of linear systems with some nonlinear perturbations and mixed neutral and discrete time-varying delays is investigated. By using a descriptor technique, Lyapunov-Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities (LMIs) to synthesize the residual generation scheme. Based on the Luen…
Robust synchronization and fault detection of uncertain master-slave systems with mixed time-varying delays and nonlinear perturbations
2011
Publiahed version of an article in the journal: International Journal of Control, Automation and Systems. Also available from the publisher on SpringerLink: http://dx.doi.org/10.1007/s12555-011-0408-8 In this paper, the problem of robust synchronization and fault detection for a class of master-slave systems subjected to some nonlinear perturbations and mixed neutral and discrete time-varying delays is investigated based on an H ∞ performance condition. By introducing a descriptor technique, using Lyapunov-Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities to synthesize the resid…
Mixed l-/l1 fault detection observer design for positive switched systems with time-varying delay via delta operator approach
2014
Published version of an article in the journal: International Journal of Control, Automation and Systems. Also available from the publisher at: http://dx.doi.org/10.1007/s12555-013-0466-1 This paper investigates the problem of fault detection observer design for positive switched systems with time-varying delay via delta operator approach. A new fault sensitivity measure, called l-index, is proposed. The l- fault detection observer design and multi-objective l -/l1 fault detection observer design problems are addressed. Based on the average dwell time approach and the piecewise copositive type Lyapunov-Krasovskii functional method in delta domain, sufficient conditions for the existence of …
Anomaly Detection in Traffic Surveillance Videos Using Deep Learning
2022
In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abno…
Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Int…
2022
Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining…
Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian b…
2023
Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth anal- ysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data…
Malware Detection in Internet of Things (IoT) Devices Using Deep Learning
2022
Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is prep…
Automatic defect localization in VLSI circuits: A fusion approach based on the Dempster-Shafer theory
2017
Defect localization in Very Large Integration Cir-cuits (VLSI) requires to use multi-sensor information such aselectrical waveforms, emission microscopy images and frequencymapping in order to detect, localize and identify the failure. Eachsensor provides a specific kind of feature modeling the evidence.Thus, the defect localization in VLSI can be summarized asa problem of data fusion with heterogeneous and impreciseinformation. This study illustrates how to reproduce the humandecision for modeling and fusing the different multi-sensorfeatures by using the Demspter-Shafer theory. We propose notonly an automatic decision rule for mass functions computingbut also confidence intervals to quantif…