Permutation tests for nonparametric detection

In this paper, the authors provide a methodology to design nonparametric permutation tests and, in particular, nonparametric rank tests for applications in detection. In the first part of the paper, the authors develop the optimization theory of both permutation and rank tests in the Neyman?Pearson sense; in the second part of the paper, they carry out a comparative performance analysis of the permutation and rank tests (detectors) against the parametric ones in radar applications. First, a brief review of some contributions on nonparametric tests is realized. Then, the optimum permutation and rank tests are derived. Finally, a performance analysis is realized by Monte-Carlo simulations for the corresponding detectors, and the results are shown in curves of detection probability versus signal-to-noise rati

Model Order Selection Rules For Covariance Structure Classification

The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules

The acquisition of direct sequence spread spectrum communication systems

This Paper surveys different techniques of acquiring Direct Sequence Spread Spectrum Systems. It classifies different systems, indicates the strongpoints and weakness of each, along with some applications. One method, The Single Dwell Serial PN Acquisition System is then focused on in detail. The detail includes analysis of standard version, derivation of the mean time to acquire, the variance, the probability of detection and the probability of a false alarm. In the last section of the paper the analytical results of the Single Dwell Serial PN Acquisition System shall be confirmed by computer simulation

Probabilistic Models for Anomaly Detection Based on Usage of Network Traffic

Recent advances in intrusions and attacks reflect vulnerabilities in computer networks. Innovative methods and tools can help attack defenses, prevent attack propagations, detect and respond to such attacks in a timely manner. Intrusion detection and prevention systems search for unauthorized use, recognize anomalous behavior, and prevent attempts to deny services.  These systems gather and analyze information from the network, identify possible breaches of the security profile, as well as misuses. We have been experimenting with methods for introducing important concepts related to intrusion detection and improving undergraduate research experiences and education. To achieve this goal, probabilistic models are introduced to students in computer, information system and network security courses. This article presents a set of probabilistic methods and statistical models for network traffic anomaly detection. It also describes some prospects and how models have ripened from theories to big data analysis applications. Keywords: Intrusion, conditional probability, network system, regression, data analysi