Online Anomaly Detection Using KDE

Ahmed, Tarem

doi:10.1109/glocom.2009.5425504

Cited by 39 publications

(36 citation statements)

References 18 publications

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“…Next come KOAD and KEAD, the performances of both of which are observed to be comparable, with KOAD faring marginally better. It must be remembered here that the thresholds ν 1 and ν 2 must be manually set in KOAD, while KEAD incorporates autonomous setting for all important algorithm parameters [5]. The strikingly low performance of the benchmark NCD-based algorithm may be attributed to the fact that this algorithm requires a significantly longer training period, and needs to maintain a significantly larger database of images to compare new arrivals against [12].…”

Section: Resultsmentioning

confidence: 99%

“…The Kernel Estimation-based Anomaly Detection (KEAD) algorithm formally states the problem as follows [5]. Given a sequence of multidimensional data points {x i } t+L i=t−L ∈ R D , the objective is to determine if x t is a realisation of probability distribution P n,t or of probability distribution P a .…”

Section: Kernel Estimation-based Anomaly Detectionmentioning

confidence: 99%

“…The following is done in such a situation: an "Orange" alarm is raised at time t, x t is stored for the next ℓ ≪ L timesteps, and taking a firm decision on it is delayed for a further ℓ timesteps. Details regarding the KEAD algorithm are available in [5].…”

Section: Kernel Estimation-based Anomaly Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Surveillance in Distributed, Visual Networks: An Empirical Comparison of Recent Algorithms

Ahmed¹,

Ahmed²,

Pathan³

2014

IJCA

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A number of algorithms have been recently proposed for automatic intruder detection from CCTV images. Past researchers have typically tested these algorithms on centralized networks where all images are transmitted to a central control room. This paper demonstrates the applicability of a selection of such algorithms to a distributed network of wireless sensors. A distributed network of wireless visual sensors was simulated using a number of high-resolution webcams setup in the hallways of an academic building. The selected algorithms were then applied in a distributed fashion at each node. An empirical comparison of the most popular of the recent algorithms on a simulation of a wireless sensor network was thus obtained. This paper provides corroborating evidence in support of the most effective of such algorithms to the problem of automatic anomaly detection from image streams.

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Section: Resultsmentioning

confidence: 99%

Section: Kernel Estimation-based Anomaly Detectionmentioning

confidence: 99%

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Automated Surveillance in Distributed, Visual Networks: An Empirical Comparison of Recent Algorithms

Ahmed¹,

Ahmed²,

Pathan³

2014

IJCA

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“…al [11] brings out the taxonomy of data exfiltration and presents all possible exfiltration methods specifically the information communication based exfiltration using well know protocols. Tarem Ahmed [12] present on-line anomaly detection using Kernel Density Estimation technique.…”

Section: Related Workmentioning

confidence: 99%

Behavior model for detecting data exfiltration in network environment

Ramachandran

Neelakantan

Bidyarthy

2011

2011 IEEE 5th International Conference on Internet Multimedia Systems Architecture and Application

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There is a growing concern across the globe about exfiltration of sensitive data over network. This coupled with the increase in other insider threats pose greater challenge. Present day perimeter security solutions such as Intrusion detection & prevention system, firewall are not capable of detecting data-exfiltration. Also existing behavior models that can detect intrusions and worms do not incorporate mechanims to detect data-exfiltration. Devising an exclusive behavior based model is essential to detect data-exfiltration over network by utilizing parameters from both system and network.In this paper, we present a behavior approach based on Kernel Density Estimation (KDE) and co-relation co-efficient methods to detect data-exfiltration. Firstly, during the learning phase, we profile each host in a network and compute KDE values individually for system and network parameters. Secondly, during the detection phase we compute KDEs for the identified parameters and then correlate current KDE values with the learnt KDE values using Carl Pearsons correlation coefficient method to detect data-exfiltration over the network. We present our approach, analysis and the findings based on our model. Results obtained reveal that our approach detect data-exfiltration incidents over the network.Index Terms-Insider attack, data-exfiltration, network anomaly detection and host anomaly detection

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“…The algorithm then signals an anomaly upon encountering a deviation from the norm. KOAD was first presented in [3] where it was applied to anomaly detection in IP networks, and extended in [4]. Using KOAD to detect road traffic incidents was suggested in [5].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Intruder Detection in Surveillance Networks Using Adaptive Kernel Methods

Ahmed

et al. 2010

2010 IEEE International Conference on Communications

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In this paper we apply a recursive algorithm based on kernel mappings to propose an automated, real-time intruder detection mechanism for surveillance networks. Our proposed method is portable and adaptive, and does not require any expensive or sophisticated components. Through application to real images from BRAC University's closed-circuit television system and comparison with common methods based on Principle Component Analysis (PCA), we show that it is possible to obtain high detection accuracy with low complexity.

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Online Anomaly Detection Using KDE

Cited by 39 publications

References 18 publications

Automated Surveillance in Distributed, Visual Networks: An Empirical Comparison of Recent Algorithms

Automated Surveillance in Distributed, Visual Networks: An Empirical Comparison of Recent Algorithms

Behavior model for detecting data exfiltration in network environment

Real-Time Intruder Detection in Surveillance Networks Using Adaptive Kernel Methods

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