The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
J. M. Barker - One of the best experts on this subject based on the ideXlab platform.
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INFOCOM - Detecting Spam Zombies by Monitoring Outgoing Messages
IEEE INFOCOM 2009 - The 28th Conference on Computer Communications, 2009Co-Authors: Zhenhai Duan, Peng Chen, Fernando Sanchez, Yingfei Dong, M. Stephenson, J. M. BarkerAbstract:Compromised machines are one of the key security threats on the Internet; they are often used to launch various security attacks such as DDoS, spamming, and identity theft. In this paper we address this issue by investigating effective solutions to automatically identify compromised machines in a network. Given that spamming provides a key economic incentive for attackers to recruit the large number of compromised machines, we focus on the subset of compromised machines that are involved in the spamming activities, commonly known as spam zombies. We develop an effective spam zombie detection system named SPOT by monitoring outgoing messages of a network. SPOT is designed based on a powerful statistical tool called Sequential Probability Ratio Test, which has bounded False positive and False Negative Error rates. Our evaluation studies based on a two- month email trace collected in a large U.S. campus network show that SPOT is an effective and efficient system in automatically detecting compromised machines in a network. For example, among the 440 internal IP addresses observed in the email trace, SPOT identifies 132 of them as being associated with compromised machines. Out of the 132 IP addresses identified by SPOT, 126 can be either independently confirmed (110) or highly likely (16) to be compromised. Moreover, only 7 internal IP addresses associated with compromised machines in the trace are missed by SPOT.
Mauro Barni - One of the best experts on this subject based on the ideXlab platform.
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The source identification game: an informationtheoretic perspective
2013Co-Authors: Mauro Barni, Benedetta TondiAbstract:Abstract—We introduce a theoretical framework in which to cast the source identification problem. Thanks to the adoption of a game-theoretic approach, the proposed framework permits us to derive the ultimate achievable performance of the forensic analysis in the presence of an adversary aiming at deceiving it. The asymptotic Nash equilibrium of the source identification game is derived under an assumption on the resources on which the forensic analyst may rely. The payoff at the equilibrium is an-alyzed, deriving the conditions under which a successful forensic analysis is possible and the Error exponent of the False-Negative Error probability in such a case. The difficulty of deriving a closed-form solution for general instances of the game is alleviated by the introduction of an efficient numerical procedure for the derivation of the optimum attacking strategy. The numerical analysis is applied to a case study to show the kind of information it can provide. Index Terms—Multimedia forensics, source identification, counter-forensics, game theory, hypothesis testing, adversarial signal processing. I
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ICASSP - A game theoretic approach to source identification with known statistics
2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012Co-Authors: Mauro BarniAbstract:In the attempt to lay the basis for the construction of a theoretical framework to cast forensics and anti-forensics techniques in, we introduce a game-theoretic model for the source-identification problem with known statistics. The framework is used to derive the Nash equilibrium for an asymptotic version of the game, in which the players' strategies and the payoff are defined in terms of the Error exponents of the False positive and False Negative probabilities. The payoff at the equilibrium is evaluated and the conditions under which the False Negative Error probability tends to zero derived.
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Asymptotically Optimum Universal Watermark Embedding and Detection in the High-SNR Regime
IEEE Transactions on Information Theory, 2010Co-Authors: Pedro Comesaa, Neri Merhav, Mauro BarniAbstract:The problem of optimum watermark embedding and detection was addressed in a recent paper by Merhav and Sabbag, where the optimality criterion was the maximum False-Negative Error exponent subject to a guaranteed False-positive Error exponent. In particular, Merhav and Sabbag derived universal asymptotically optimum embedding and detection rules under the assumption that the detector relies solely on second-order joint empirical statistics of the received signal and the watermark. In the case of a Gaussian host signal and a Gaussian attack, however, closed-form expressions for the optimum embedding strategy and the False-Negative Error exponent were not obtained in that work. In this paper, we derive the False-Negative Error exponent for any given embedding strategy and use such a result to show that in general the optimum embedding rule depends on the variance of the host sequence and the variance of the attack noise. We then focus on high signal-to-noise ratio (SNR) regime, deriving the optimum embedding strategy for such a setup. In this case, a universally optimum embedding rule turns out to exist and to be very simple with an intuitively appealing geometrical interpretation. The effectiveness of the newly proposed embedding strategy is evaluated numerically.
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Asymptotically Optimum Universal One-Bit Watermarking for Gaussian Covertexts and Gaussian Attacks
arXiv: Information Theory, 2008Co-Authors: Pedro Comesaña, Neri Merhav, Mauro BarniAbstract:The problem of optimum watermark embedding and detection was addressed in a recent paper by Merhav and Sabbag, where the optimality criterion was the maximum False‐Negative Error exponent subject to a guaranteed False‐ positive Error exponent. In particular, Merhav and Sabbag derived universal asymptotically optimum embedding and detection rules under the assumption that the detector reli es solely on second order joint empirical statistics of the received signal and the watermark. In the case of a Gaussian host signal and a Gaussian attack, however, closed‐form expressions for the optimum embedding strategy and the False‐Negative Error exponent were not obtained in that work. In this paper, we derive such expressions, again, under the universality assumption that neither the host variance nor the attack power are known to either the embedder or the detector. The optimum embedding rule turns out to be very simple and with an intuitively‐appealing geometrical interpretation. The improvement with respect to existing sub‐optimum schemes is demonstrated by displaying the optimum False‐Negative Error exponent as a function of the guaranteed False‐positive Error exponent.
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Security, Forensics, Steganography, and Watermarking of Multimedia Contents - Asymptotically optimum embedding strategy for one-bit watermarking under Gaussian attacks
Security Forensics Steganography and Watermarking of Multimedia Contents X, 2008Co-Authors: Pedro Comesaña, Neri Merhav, Mauro BarniAbstract:The problem of asymptotically optimum watermark detection and embedding has been addressed in a recent paper by Merhav and Sabbag where the optimality criterion corresponds to the maximization of the False Negative Error exponent for a fixed False positive Error exponent. In particular Merhav and Sabbag derive the optimum detection rule under the assumption that the detector relies on the second order statistics of the received signal (universal detection under limited resources), however the optimum embedding strategy in the presence of attacks and a closed formula for the Negative Error exponents are not available. In this paper we extend the analysis by Merhav and Sabbag, by deriving the optimum embedding strategy under Gaussian attacks and the corresponding False Negative Error exponent. The improvement with respect to previously proposed embedders are shown by means of plots.
Jean-louis Dillenseger - One of the best experts on this subject based on the ideXlab platform.
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Fast and Accurate Segmentation Method of Active Shape Model with Rayleigh Mixture Model Clustering for Prostate Ultrasound Images
Computer Methods and Programs in Biomedicine, 2020Co-Authors: Yibo Jiang, Hui Tang, Guanyu Yang, Huazhong Shu, Jean-louis DillensegerAbstract:Background and Objective: The prostate cancer interventions, which need an accurate prostate segmentation, are performed under ultrasound imaging guidance. However, prostate ultrasound segmentation is facing two challenges. The first is the low signal-to-noise ratio and inhomogeneity of the ultrasound image. The second is the non-standardized shape and size of the prostate. Methods: For prostate ultrasound image segmentation, this paper proposed an accurate and efficient method of Active Shape Model (ASM) with Rayleigh Mixture Model Clustering (ASM-RMMC). Firstly, Rayleigh Mixture Model (RMM) is adopted for clustering the image regions which present similar speckle distributions. These content-based clustered images are then used to initialize and guide the deformation of an ASM model. Results: The performance of the proposed method is assessed on 30 prostate ultrasound images using four metrics as Mean Average Distance (MAD), Dice Similarity Coefficient (DSC), False Positive Error (FPE) and False Negative Error (FNE). The proposed ASM-RMMC reaches high seg-mentation accuracy with 95 ± 1% for DSC, 1.87 ± 0.51 pixels for MAD, 2.10% ± 0.36% for FPE and 2.78% ± 0.7% for FNE, respectively. Moreover, 1 the average segmentation time is less than 8 s when treating a single prostate ultrasound image through ASM-RMMC. Conclusions: This paper presents a method for prostate ultrasound image segmentation, which achieves high accuracy with less computational complexity and meets the clinical requirements.
Hugh P. Possingham - One of the best experts on this subject based on the ideXlab platform.
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improving precision and reducing bias in biological surveys estimating False Negative Error rates
Ecological Applications, 2003Co-Authors: Andrew J. Tyre, Brigitte Tenhumberg, Scott A. Field, Darren Niejalke, Kirsten M. Parris, Hugh P. PossinghamAbstract:The use of presence/absence data in wildlife management and biological surveys is widespread. There is a growing interest in quantifying the sources of Error associated with these data. We show that False-Negative Errors (failure to record a species when in fact it is present) can have a significant impact on statistical estimation of habitat models using simulated data. Then we introduce an extension of logistic modeling, the zero-inflated binomial (ZIB) model that permits the estimation of the rate of False-Negative Errors and the correction of estimates of the probability of occurrence for False-Negative Errors by using repeated visits to the same site. Our simulations show that even relatively low rates of False Negatives bias statistical estimates of habitat effects. The method with three repeated visits eliminates the bias, but estimates are relatively imprecise. Six repeated visits improve precision of estimates to levels comparable to that achieved with conventional statistics in the absence of False-Negative Errors. In general, when Error rates are ≤50% greater efficiency is gained by adding more sites, whereas when Error rates are >50% it is better to increase the number of repeated visits. We highlight the flexibility of the method with three case studies, clearly demonstrating the effect of False-Negative Errors for a range of commonly used survey methods.
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IMPROVING PRECISION AND REDUCING BIAS IN BIOLOGICAL SURVEYS: ESTIMATING False‐Negative Error RATES
Ecological Applications, 2003Co-Authors: Andrew J. Tyre, Brigitte Tenhumberg, Scott A. Field, Darren Niejalke, Kirsten M. Parris, Hugh P. PossinghamAbstract:The use of presence/absence data in wildlife management and biological surveys is widespread. There is a growing interest in quantifying the sources of Error associated with these data. We show that False-Negative Errors (failure to record a species when in fact it is present) can have a significant impact on statistical estimation of habitat models using simulated data. Then we introduce an extension of logistic modeling, the zero-inflated binomial (ZIB) model that permits the estimation of the rate of False-Negative Errors and the correction of estimates of the probability of occurrence for False-Negative Errors by using repeated visits to the same site. Our simulations show that even relatively low rates of False Negatives bias statistical estimates of habitat effects. The method with three repeated visits eliminates the bias, but estimates are relatively imprecise. Six repeated visits improve precision of estimates to levels comparable to that achieved with conventional statistics in the absence of False-Negative Errors. In general, when Error rates are ≤50% greater efficiency is gained by adding more sites, whereas when Error rates are >50% it is better to increase the number of repeated visits. We highlight the flexibility of the method with three case studies, clearly demonstrating the effect of False-Negative Errors for a range of commonly used survey methods.
Zhenhai Duan - One of the best experts on this subject based on the ideXlab platform.
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INFOCOM - Detecting Spam Zombies by Monitoring Outgoing Messages
IEEE INFOCOM 2009 - The 28th Conference on Computer Communications, 2009Co-Authors: Zhenhai Duan, Peng Chen, Fernando Sanchez, Yingfei Dong, M. Stephenson, J. M. BarkerAbstract:Compromised machines are one of the key security threats on the Internet; they are often used to launch various security attacks such as DDoS, spamming, and identity theft. In this paper we address this issue by investigating effective solutions to automatically identify compromised machines in a network. Given that spamming provides a key economic incentive for attackers to recruit the large number of compromised machines, we focus on the subset of compromised machines that are involved in the spamming activities, commonly known as spam zombies. We develop an effective spam zombie detection system named SPOT by monitoring outgoing messages of a network. SPOT is designed based on a powerful statistical tool called Sequential Probability Ratio Test, which has bounded False positive and False Negative Error rates. Our evaluation studies based on a two- month email trace collected in a large U.S. campus network show that SPOT is an effective and efficient system in automatically detecting compromised machines in a network. For example, among the 440 internal IP addresses observed in the email trace, SPOT identifies 132 of them as being associated with compromised machines. Out of the 132 IP addresses identified by SPOT, 126 can be either independently confirmed (110) or highly likely (16) to be compromised. Moreover, only 7 internal IP addresses associated with compromised machines in the trace are missed by SPOT.