Search results for " detection"
showing 10 items of 1676 documents
Edge Computing-enabled Intrusion Detection for C-V2X Networks using Federated Learning
2022
Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across thes…
Development of microextraction techniques in combination with GC-MS/MS for the determination of mycotoxins and metabolites in human urine.
2017
Simple and highly efficient sample preparation procedures, namely, dispersive liquid–liquid microextraction and salting-out liquid–liquid extraction for the analysis of ten Fusarium mycotoxins and metabolites in human urine were compared. Various parameters affecting extraction efficiency were carefully evaluated. Under optimal extraction conditions, salting-out liquid–liquid extraction showed a better accuracy (84–96%) and precision (<14%) than dispersive liquid–liquid microextraction. Hence, a multibiomarker method based on salting-out liquid–liquid extraction followed by gas chromatography with tandem mass spectrometry was proposed. Satisfactory results in terms of validation were achiev…
Determination of free formaldehyde in cosmetics containing formaldehyde-releasing preservatives by reversed-phase dispersive liquid-liquid microextra…
2017
Abstract An analytical method for the determination of traces of formaldehyde in cosmetic products containing formaldehyde-releasing preservatives has been developed. The method is based on reversed-phase dispersive liquid–liquid microextraction (RP-DLLME), that allows the extraction of highly polar compounds, followed by liquid chromatography–ultraviolet/visible (LC–UV/vis) determination with post-column derivatization. The variables involved in the RP-DLLME process were studied to provide the best enrichment factors. Under the selected conditions, a mixture of 500 μL of acetonitrile (disperser solvent) and 50 μL of water (extraction solvent) was rapidly injected into 5 mL of toluene sampl…
A capillary liquid chromatography method for benzalkonium chloride determination as a component or contaminant in mixtures of biocides
2015
A method for quantifying benzalkonium chloride (BAK), an alkyl dimethyl benzyl ammonium compound, in several biocides formulations is proposed. A tertiary amine like N-(3-aminopropyl)-N-dodecyl-1,3-propanediamine (TA) and a straight-chain alkyl ammonium compound like trimethyl-tetradecyl ammonium chloride (TMTDAC), have been employed as trade surfactants besides BAK. Two capillary analytical columns with different polarities are tested: inertsil CN-3 capillary column (150mm×0.5mm i.d., 3μm particle diameter) and a non endcapped Zorbax C18 capillary column (35mm×0.5mm i.d., 5μm particle diameter). This latter column provided the best separation of the BAK homologues in less than 12min using …
A comparison among different techniques for human ERG signals processing and classification
2014
A comparison among different techniques for human ERG signals processing and classification ( Articles not published yet, but available online Article in press About articles in press (opens in a new window) ) Barraco, R.a, Persano Adorno, D.a , Brai, M.a, Tranchina, L.b a Dipartimento di Fisica e Chimica, Università di Palermo and CNISM, Viale delle Scienze, Ed. 18, I-90128 Palermo, Italy b Laboratorio di Fisica e Tecnologie Relative - UniNetLab, Università di Palermo, Viale delle Scienze, Ed. 18, I-90128 Palermo, Italy Abstract Feature detection in biomedical signals is crucial for deepening our knowledge about the involved physiological processes. To achieve this aim, many analytic appro…
Measurement of acoustic attenuation in South Pole ice
2010
Using the South Pole Acoustic Test Setup (SPATS) and a retrievable transmitter deployed in holes drilled for the IceCube experiment, we have measured the attenuation of acoustic signals by South Pole ice at depths between 190 m and 500 m. Three data sets, using different acoustic sources, have been analyzed and give consistent results. The method with the smallest systematic uncertainties yields an amplitude attenuation coefficient alpha = 3.20 \pm 0.57 km^(-1) between 10 and 30 kHz, considerably larger than previous theoretical estimates. Expressed as an attenuation length, the analyses give a consistent result for lambda = 1/alpha of ~1/300 m with 20% uncertainty. No significant depth or …
Knowledge Discovery from Network Logs
2015
Modern communications networks are complex systems, which facilitates malicious behavior. Dynamic web services are vulnerable to unknown intrusions, but traditional cyber security measures are based on fingerprinting. Anomaly detection differs from fingerprinting in that it finds events that differ from the baseline traffic. The anomaly detection methodology can be modelled with the knowledge discovery process. Knowledge discovery is a high-level term for the whole process of deriving actionable knowledge from databases. This article presents the theory behind this approach, and showcases research that has produced network log analysis tools and methods. peerReviewed
Discovering single classes in remote sensing images with active learning
2012
When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. Moreover, the efficiency of the search is also an issue, since the samples labeled as background are not used by target detectors such as the support vector data description (SVDD). In this work we propose a competitive and effective approach to identify the most relevant training samples for one-class classification based on the use of an active learning strategy. The SVDD classifier is first trained with insufficient target examples. It is t…
A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living
2015
This research aims to describe pattern recognition models for detecting behavioural and health-related changes in a patient who is monitored continuously in an assisted living environment. The early anticipation of anomalies can improve the rate of disease prevention. Here we present different learning techniques for predicting abnormalities and behavioural trends in various user contexts. In this paper we described a Hidden Markov Model based approach for detecting abnormalities in daily activities, a process of identifying irregularity in routine behaviours from statistical histories and an exponential smoothing technique to predict future changes in various vital signs. The outcomes of t…
A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks
2018
International audience; Unmanned aerial vehicles (UAVs) networks have not yet received considerable research attention. Specifically, security issues are a major concern because such networks, which carry vital information, are prone to various attacks. In this paper, we design and implement a novel intrusion detection and response scheme, which operates at the UAV and ground station levels, to detect malicious anomalies that threaten the network. In this scheme, a set of detection and response techniques are proposed to monitor the UAV behaviors and categorize them into the appropriate list (normal, abnormal, suspect, and malicious) according to the detected cyber-attack. We focus on the m…