Search results for " Detection"
showing 10 items of 1676 documents
Projected WIMP sensitivity of the XENONnT dark matter experiment
2020
XENONnT is a dark matter direct detection experiment, utilizing 5.9 t of instrumented liquid xenon, located at the INFN Laboratori Nazionali del Gran Sasso. In this work, we predict the experimental background and project the sensitivity of XENONnT to the detection of weakly interacting massive particles (WIMPs). The expected average differential background rate in the energy region of interest, corresponding to (1, 13) keV and (4, 50) keV for electronic and nuclear recoils, amounts to 12.3 ± 0.6 (keV t y)-1 and (2.2± 0.5)× 10−3 (keV t y)-1, respectively, in a 4 t fiducial mass. We compute unified confidence intervals using the profile construction method, in order to ensure proper coverage…
Shell Model Description of Spin-Dependent Elastic and Inelastic WIMP Scattering off 119Sn and 121Sb
2022
In this work, we calculate the spin structure functions for spin-dependent elastic and inelastic WIMP scattering off 119Sn and 121Sb. Estimates for detection rates are also given. 119Sn and 121Sb are amenable to nuclear structure calculations using the nuclear shell model (NSM). With the possible exception of 201Hg, they are the only such nuclei still unexplored theoretically for their potential of inelastic WIMP scattering to a very low excited state. The present calculations were conducted using a state-of-the-art WIMP–nucleus scattering formalism, and the available effective NSM two-body interactions describe the spectroscopic properties of these nuclei reasonably well. Structure functio…
Image transmission through dynamic scattering media by single-pixel photodetection
2014
Smart control of light propagation through highly scattering media is a much desired goal with major technological implications. Since interaction of light with highly scattering media results in partial or complete depletion of ballistic photons, it is in principle impossible to transmit images through distances longer than the extinction length. Nevertheless, different methods for image transmission, focusing, and imaging through scattering media by means of wavefront control have been published over the past few years. In this paper we show that single-pixel optical systems, based on compressive detection, can also overcome the fundamental limitation imposed by multiple scattering to suc…
Recent progress in optical and electrochemical biosensors for sensing of Clostridium botulinum neurotoxin
2018
Abstract Botulinum toxin is a neurotoxic protein which produced from Clostridium botulinum and related species and it block acetylcholine release from presynaptic nerve terminals at the neuromuscular junctions. This toxin is life threatening for millions of people and growing menace to society since causing human botulism. Enzymatic activity of Botulinum neurotoxin within the cell made it hazardous and lead to flaccid paralysis. However, there isn't any reliable and precise remedy for this toxin. Therefore, there is an urgent need for early detection of this toxin in a fast and meticulous way with a robust and cost-effective relationship for real-time monitoring of Botulinum neurotoxin. Sev…
Anomaly Detection from Network Logs Using Diffusion Maps
2011
The goal of this study is to detect anomalous queries from network logs using a dimensionality reduction framework. The fequencies of 2-grams in queries are extracted to a feature matrix. Dimensionality reduction is done by applying diffusion maps. The method is adaptive and thus does not need training before analysis. We tested the method with data that includes normal and intrusive traffic to a web server. This approach finds all intrusions in the dataset. peerReviewed
Online Web Bot Detection Using a Sequential Classification Approach
2019
A significant problem nowadays is detection of Web traffic generated by automatic software agents (Web bots). Some studies have dealt with this task by proposing various approaches to Web traffic classification in order to distinguish the traffic stemming from human users' visits from that generated by bots. Most of previous works addressed the problem of offline bot recognition, based on available information on user sessions completed on a Web server. Very few approaches, however, have been proposed to recognize bots online, before the session completes. This paper proposes a novel approach to binary classification of a multivariate data stream incoming on a Web server, in order to recogn…
Efficient on-the-fly Web bot detection
2021
Abstract A large fraction of traffic on present-day Web servers is generated by bots — intelligent agents able to traverse the Web and execute various advanced tasks. Since bots’ activity may raise concerns about server security and performance, many studies have investigated traffic features discriminating bots from human visitors and developed methods for automated traffic classification. Very few previous works, however, aim at identifying bots on-the-fly, trying to classify active sessions as early as possible. This paper proposes a novel method for binary classification of streams of Web server requests in order to label each active session as “bot” or “human”. A machine learning appro…
Data Stream Clustering for Application-Layer DDoS Detection in Encrypted Traffic
2018
Application-layer distributed denial-of-service attacks have become a serious threat to modern high-speed computer networks and systems. Unlike network-layer attacks, application-layer attacks can be performed using legitimate requests from legitimately connected network machines that make these attacks undetectable by signature-based intrusion detection systems. Moreover, the attacks may utilize protocols that encrypt the data of network connections in the application layer, making it even harder to detect an attacker’s activity without decrypting users’ network traffic, and therefore violating their privacy. In this paper, we present a method that allows us to detect various application-l…
Time series clustering with different distance measures to tell Web bots and humans apart
2022
The paper deals with the problem of differentiating Web sessions of bots and human users by observing some characteristics of their traffic at the Web server input. We propose an approach to cluster bots’ and humans’ sessions represented as time series. First, sessions are expressed as sequences of HTTP requests coming to the server at specific timestamps; then, they are pre-preprocessed to form time series of limited length. Time series are clustered and the clustering performance is evaluated in terms of the ability to partition bots and humans into separate clusters. The proposed approach is applied to real server log data and validated with the use of different time series distance meas…
Identifying legitimate Web users and bots with different traffic profiles — an Information Bottleneck approach
2020
Abstract Recent studies reported that about half of Web users nowadays are intelligent agents (Web bots). Many bots are impersonators operating at a very high sophistication level, trying to emulate navigational behaviors of legitimate users (humans). Moreover, bot technology continues to evolve which makes bot detection even harder. To deal with this problem, many advanced methods for differentiating bots from humans have been proposed, a large part of which relies on supervised machine learning techniques. In this paper, we propose a novel approach to identify various profiles of bots and humans which combines feature selection and unsupervised learning of HTTP-level traffic patterns to d…