18,013 Hits in 6.3 sec

On achieving good operating points on an ROC plane using stochastic anomaly score prediction

Muhammad Qasim Ali, Hassan Khan, Ali Sajjad, Syed Ali Khayam
2009 Proceedings of the 16th ACM conference on Computer and communications security - CCS '09  
In this paper, we argue that a real-time IDS' input changes considerably over time and ROC curves generated using fixed, time-invariant classification thresholds do not characterize the best accuracy that  ...  The proposed adaptive thresholding module is incorporated into six prominent network-and host-based Anomaly Detection Systems (ADSs).  ...  Anomaly Detection Systems Before describing the real-time ADSs used in this work, we reiterate that a practical threshold adaptation algorithm should not be specific to a particular ADS.  ... 
doi:10.1145/1653662.1653700 dblp:conf/ccs/AliKSK09 fatcat:xp4akadkd5eftaqzhzd7e36rb4

A Distributed and Reliable Platform for Adaptive Anomaly Detection in IP Networks [chapter]

L. Lawrence Ho, Christopher J. Macey, Ronald Hiller
1999 Lecture Notes in Computer Science  
These algorithms are implemented as a reliable and fully distributed real-time software platform called NSAD (Network/Service Anomaly Detector). IP NSAD has the following novel features.  ...  Algorithms for anomaly detection in IP networks have been developed and a real-time distributed platform for anomaly detection has been implemented.  ...  A network anomaly detector is a real-time program that adaptively analyzes performance data of managed networks to detect "abnormal" changes (relative to historical baselines or "expected" behavior) in  ... 
doi:10.1007/3-540-48100-1_3 fatcat:2lyfo452gjgbbid7dvna5tbymm

Spectral anomaly methods for aerial detection using KUT nuisance rejection

R.S. Detwiler, D.M. Pfund, M.J. Myjak, J.A. Kulisek, C.E. Seifert
2015 Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment  
The algorithm has been optimized for two 15 multi-detector systems; a NaI(Tl)-detector based system and a CsI detector array.  ...  5 This work discusses the application and optimization of a spectral anomaly method for the real-6 time detection of gamma radiation sources from an aerial helicopter platform.  ...  An example showing the importance of adapting spectral anomaly windows for the detector 1 system geometry specifications is shown below.  ... 
doi:10.1016/j.nima.2015.01.040 fatcat:ywmkxt2h5bbrnknrdsqslnv7vq

An Immune Based Patient Anomaly Detection using RFID Technology

Sri Listia Rosa, Siti Mariyam Shamsuddin, Evizal Evizal
2013 Computer Engineering and Applications Journal  
The real valued compared with the distance of data, if the distance is less than a RNSA detector distance then data classified into abnormal.  ...  To develop real time detecting and monitoring system, Radio Frequency Identification (RFID) technology has been used in this system.  ...  The detector is first trained by operating the host computer for some amount of time and a model specific to the target machine is automatically computed, the model is then deployed to a real-time detector  ... 
doi:10.18495/comengapp.v2i1.14 fatcat:g7ju5vqaxvbx3hkplkn64xy5rm

Real-Time Anomaly Detection for Streaming Analytics [article]

Subutai Ahmad, Scott Purdy
2016 arXiv   pre-print
Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions.  ...  We show results from a live application that detects anomalies in financial metrics in real-time.  ...  Let the vector x t represent the state of a real-time system at time t.  ... 
arXiv:1607.02480v1 fatcat:ift42qbgmvdabe7c6iwfjkeik4

Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark

Alexander Lavin, Subutai Ahmad
2015 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)  
The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics  ...  There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors.  ...  Scoring Real-Time Anomaly Detectors In NAB an anomaly detector accepts data input and outputs instances which it deems to be anomalous.  ... 
doi:10.1109/icmla.2015.141 dblp:conf/icmla/LavinA15 fatcat:zhscxe6vufgbjo5onooyzhnemu

Setting the threshold for high throughput detectors: A mathematical approach for ensembles of dynamic, heterogeneous, probabilistic anomaly detectors [article]

Robert A. Bridges, Jessie D. Jamieson, Joel W. Reed
2017 arXiv   pre-print
(e.g., rate, size, type) often with adaptive online models producing alerts in near real time.  ...  We provide empirical experiments showing the efficacy of the capability by regulating the alert rate of a system with ≈2,500 adaptive detectors scoring over 1.5M events in 5 hours.  ...  [ , ] detail AD systems using a fleet of dynamic models and producing near real-time alerts on high volume logging data. A.  ... 
arXiv:1710.09422v1 fatcat:u7gfabnzfbe45limiongnf2wt4

A Novel Immunity inspired approach for Anomaly Detection

Praneet Saurabh, Bhupendra Verma
2014 International Journal of Computer Applications  
NSA is competent for anomaly detection problems. From this perspective this research paper presents a Novel Immunity inspired approach for Anomaly Detection (NIIAD) with the feature of fine tuning.  ...  Artificial Immune System (AIS) over the years has caught attention of researchers of various domains for complex problem solving.  ...  Next is Adaptive Immune System, it is also termed as acquired immunity because it builds a memory over a period of time to achieve a faster response when the same threat or antigen is confronted next time  ... 
doi:10.5120/16418-6034 fatcat:xeas6undlvge5p5yzrqndkxvfa

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń, Ernesto De Matteis
2018 Engineering applications of artificial intelligence  
In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection  ...  Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data.  ...  An ideal anomaly detection system should: • be able to detect anomalies with the highest possible accuracy, • be trained in unsupervised fashion, • trigger no false alarms, • work with data in a real-time  ... 
doi:10.1016/j.engappai.2018.06.012 fatcat:lfp5lfwxsbbepgjyrbqw3z3bwe

ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series [article]

Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran
2020 arXiv   pre-print
Therefore, a lightweight and ready-to-go approach that is able to detect anomalies in real-time is highly sought-after.  ...  With these goals in mind, this paper introduces ReRe, which is a Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time series.  ...  These features enable ReRe to adapt to pattern changes in the target time series and detect anomalies in a timeefficient and real-time manner.  ... 
arXiv:2004.02319v2 fatcat:5sphwn7ysrgifaphck5diosvzy

A Cooperative Negative Selection Algorithm for Anomaly Detection

Praneet Saurabh, Bhupendra Verma
2014 International Journal of Computer Applications  
Artificial Immune System (AIS) is a convoluted and complex arrangement derived from biological immune system (BIS). It possesses the abilities of self-adapting, self-learning and selfconfiguration.  ...  This paper presents a Cooperative Negative Selection Algorithm (CNSA) for Anomaly Detection by integrating a novel detector selection strategy and voting between them to effectively identify anomaly.  ...  The adaptive immune system is mainly composed of white blood cells also termed as acquired immunity because it builds a memory over a period of time to achieve a faster response when the same threat or  ... 
doi:10.5120/16688-6809 fatcat:zlijes2ftzbkhcx6sn7dglf4ke

A Novel Multi-layered Immune Network Intrusion Detection Defense Model: MINID

Xufei Zheng, Yonghui Fang, Yanhui Zhou, Jing Zhang
2013 Journal of Networks  
In this paper, we combine the innate and adaptive immune mechanisms in BIS and map them to AIS, and propose a novel multilayered immune network intrusion detection model (MINID) which based on pattern  ...  Index Terms-network intrusion detection, innate immune, adaptive immune, pattern recognition receptor theory, artificial immune system, dual negative selection algorithm Manuscript  ...  De Castro et al. proposed a general AIS framework based on the adaptive immune system in BIS [28] .  ... 
doi:10.4304/jnw.8.3.636-644 fatcat:4b2d74lrdvaodo5vhqsivszhpm

A Neuro Fuzzy Based Intrusion Detection System for a Cloud Data Center Using Adaptive Learning

Pandeeswari Nagarajan, Ganeshkumar Perumal
2015 Cybernetics and Information Technologies  
So, the proposed scheme develops an anomaly detection system, named Hypervisor Detector at a hypervisor layer to detect the abnormalities in the virtual network.  ...  One of the successful approaches, which integrate fuzzy systems with adaptation and learning proficiencies of a neural network, such as ANFIS (Adaptive Neuro Fuzzy Inference System) model, is based on  ...  [4] have designed a virtualization based detection element called VMFence to examine the network flow and integrity of a file and also to detect the real time attacks.  ... 
doi:10.1515/cait-2015-0043 fatcat:vdqhi6lwnfemlf2gigq3uh5gw4

A Framework for Adaptive Anomaly Detection Based on Support Vector Data Description [chapter]

Min Yang, HuanGuo Zhang, JianMing Fu, Fei Yan
2004 Lecture Notes in Computer Science  
To improve the efficiency and usability of adaptive anomaly detection system, we propose a new framework based on Support Vector Data Description (SVDD) method.  ...  As a result, false positive rate is reduced from 13.43% to 4.45%.  ...  In [3] , a general adaptive model generation system to anomaly detection is presented, which uses a probability-based algorithm for building models over noisy data periodically.  ... 
doi:10.1007/978-3-540-30141-7_62 fatcat:fxvqcyqicjf7zkonismkmfsn2y

Online Self-Evolving Anomaly Detection in Cloud Computing Environments [article]

Haili Wang, Jingda Guo, Xu Ma, Song Fu, Qing Yang, Yunzhong Xu
2021 arXiv   pre-print
As a distinct advantage of our framework, cloud system administrators only need to check a small number of detected anomalies, and their decisions are leveraged to update the detector.  ...  Moreover, we design two types of detectors, one for general anomaly detection and the other for type-specific anomaly detection.  ...  This simple yet effective strategy allows the adaptive system to surpass the previously published work in real word applications, such as [9] , [10] .  ... 
arXiv:2111.08232v1 fatcat:u43drqxuvfek7mcalzrksql3im
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