This paper considers target detection in passive radar that employs a digital video broadcasting-terrestrial version 2 (DVB-T2) emitter as an illuminator of opportunity. The target detection problem is equivalent to i...
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ISBN:
(纸本)9781479982974
This paper considers target detection in passive radar that employs a digital video broadcasting-terrestrial version 2 (DVB-T2) emitter as an illuminator of opportunity. The target detection problem is equivalent to identifying the presence/absence of the DVB-T2 signal in the returns. A correlation-based detection strategy is proposed by exploiting a unique C-A-B structure of the P1 symbol that is ubiquitous in all DVB-T2 transmissions. The P1 symbol, originally introduced for a DVB-T2 receiver to obtain synchronization, is exploited for target detection. The performance of the proposed detector is evaluated by Monte Carlo simulations. Our results show that the proposed detector can reliably detect the target without full knowledge of the DVB-T2 signal waveform (except for the C-A-B structure).
To improve the ability of resisting the deceptive interference for radar, a multi-parameter modulated radar signal is investigated and its compressed sensing model is constructed in this paper. Unlike the traditional ...
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To improve the ability of resisting the deceptive interference for radar, a multi-parameter modulated radar signal is investigated and its compressed sensing model is constructed in this paper. Unlike the traditional frequency modulated continuous wave (FMCW) signal model, the established model has nat-ural advantages in mitigating the deceptive interference due to the agility of multiple signal parameters. Simultaneously, to reduce the impact of the deceptive interference on the target echo, the compressed recovery algorithm combined with the proposed correlation-based local detection is introduced. Before recovering the target echo, the local dictionary matrix is inspected by using the correlation to ensure that the expected echo signal is obtained merely. Simulation results show that the proposed method sig-nificantly improves the performance in mitigating the deceptive interference. By coherently integrating the signals over multiple periods, the relative pure range profile or the range-Doppler plane could be acquired. It helps radar system to extract the distance or velocity information in the interference envi-ronment.(c) 2022 Published by Elsevier B.V.
The latest generation of Botnets use HTTP protocol and port 80 as their communication medium to impersonate themselves as normal web users and avoid current security solutions. In addition, the Botmasters who control ...
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ISBN:
(纸本)9781538607251
The latest generation of Botnets use HTTP protocol and port 80 as their communication medium to impersonate themselves as normal web users and avoid current security solutions. In addition, the Botmasters who control the infected devices employ several techniques, such as encryption, code obfuscation, anti-honeypot capabilities and random communication patterns to keep their Bots undetectable as long as possible. However, Bots are designed to be a coordinated form of organized cyberattack in which they conduct the synchronized attacks in the form of groups. Thus, the similarities of cooperative group activities can be used as an effective measure to distinguish Bots from normal users. In this paper, we propose a histogram based behaviour analysis approach to identify the number of web requests and their time gap diversity posed by HTTP Bots. Finally, a correlationbased communication histogram analysis approach is designed to detect HTTP Botnets based on similarity and correlation of their group activities. The proposed correlationbased HTTP Botnet detection model was successfully able to detect the HTTP Bots with high accuracy, along with a very low rate of false positive.
Outlier detection is essential for identifying patterns that deviate from expected normal representations in data. Real -world challenges such as the lack of labeled data, noise, and high dimensionality significantly ...
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Outlier detection is essential for identifying patterns that deviate from expected normal representations in data. Real -world challenges such as the lack of labeled data, noise, and high dimensionality significantly impact the effectiveness of existing methods. Understanding the temporal or spatial correlation of normal data is crucial for detecting or forecasting anomalous patterns. In this paper, we introduce UOSC-GNN, a novel Unsupervised Outlier detection architecture by Sequential correlation analysis with Graph Neural Network. The architecture includes a deviation generation module to measure the variance between expected and actual states of sequential data. This module incorporates a Generic Feature Extraction component to extract intrinsic features from outliers and normal instances tailored to specific tasks, and an Expected State Estimator component based on Graph Neural Network to learn sequential patterns. To ensure the forecast of outliers with high confidence and improve alarm accuracy, an Outlier Probability Assessment module is introduced. This module combines a rule -based index derived from expert knowledge and a statistical index calculated based on generated deviations. Our method is evaluated on two real -world tasks: medical imaging analysis utilizing spatial -related data and early fault detection of instruments leveraging temporal -related data. The results show that our method triggers alarms about 70 min earlier than the best models on the run -to -failure bearing dataset and achieves an accuracy of 92.82% and a sensitivity of 88.51% on the wireless capsule endoscopy image dataset, outperforming traditional outlier detection algorithms consistently.
Digital image source identification primarily focuses on analyzing and detecting the machine imprints or camera fingerprints left by imaging devices during the imaging process to trace the origin of digital images. Th...
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Digital image source identification primarily focuses on analyzing and detecting the machine imprints or camera fingerprints left by imaging devices during the imaging process to trace the origin of digital images. The development of a swift search algorithm is crucial for the effective implementation of camera source identification. Despite its importance, this domain has witnessed limited research, with existing studies predominantly focusing on search efficiency while neglecting robustness, which is essential. In practical scenarios, query images often suffer from poor signal quality due to noise, and the variability in fingerprint quality across different sources presents a significant challenge. Conventional brute-force search algorithms (BFSAs) prove largely ineffective under these conditions because they lack the necessary robustness. This paper addresses the issues in digital image source identification by proposing a rapid fingerprint search algorithm based on global information. The algorithm innovatively introduces a search priority queue (SPQ), which analyzes the global correlation between the query fingerprint and all reference fingerprints in the database to construct a comprehensive priority ranking, thereby achieving the efficient retrieval of matching fingerprints. Compared to the traditional brute-force search algorithm (BFSA), our method significantly reduces computational complexity in large-scale databases, optimizing from O(nN) to O(nlogN), where n is the length of the fingerprint, and N is the number of fingerprints in the database. Additionally, the algorithm demonstrates strong robustness to noise, maintaining a high matching accuracy rate even when image quality is poor and noise interference is significant. Experimental results show that in a database containing fingerprints from 70 cameras, our algorithm is 50% faster in average search time than BFSA, and its matching accuracy rate exceeds 90% under various noise levels. This method not o
Numerous forgeries are made by precise and fast colour laser printers, and they have the ability to cause severe harm to society. To prevent such forgeries, printer identification can be employed as a countermeasure. ...
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Numerous forgeries are made by precise and fast colour laser printers, and they have the ability to cause severe harm to society. To prevent such forgeries, printer identification can be employed as a countermeasure. A new method is presented to identify colour laser printers using halftone texture fingerprints. The method uses images photographed without an additional close-up lens as input images, and halftone texture fingerprints are extracted in the curvelet transform domain. The extracted halftone texture fingerprint is used in correlation-based detection, and the colour laser printer of the most similar known halftone texture fingerprint is determined as the source colour laser printer. Experiments are performed on five colour laser printers and the performance is compared with existing methods. Experimental results show that the method overcomes the limitations of existing methods.
A well-known power spectrum condition (PSC) was derived for resisting the Wiener attack. The Wiener attack estimates the embedded watermark from a watermarked signal and subtracts it to disable the watermark detection...
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ISBN:
(纸本)0819444154
A well-known power spectrum condition (PSC) was derived for resisting the Wiener attack. The Wiener attack estimates the embedded watermark from a watermarked signal and subtracts it to disable the watermark detection. According to the PSC, the power spectral density of a watermark should be proportional to that of the original data since the watermark becomes difficult to estimate. However, PSC considers the correlation-based detection only and does not pay attention to whitening filtering before detection. In watermark detection problem, the host signal is considered as a noise and usually it has a colored power spectrum. Thus, by applying the whitening filter the watermark detection performance can be considerably improved. When using the whitening filter, if the power spectrum of watermark is like that of the original data, the gain of the whitening filter becomes one and there is little enhancement in detection performance. On the contrary, the watermark with different power spectral density from the original data can be removed by the Wiener attack, and the detection of the watermark becomes difficult. So this paper aims to design a watermark to improve the detection performance by satisfying apparently opposite conditions. The watermark optimized with calculus of variation method achieved the above objective in the experiment.
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