Low False Alarm Probability Research Articles

Information Fusion of Passive Sensors for Detection of Moving Targets in Dynamic Environments.

This paper addresses the problem of target detection in dynamic environments in a semi-supervised data-driven setting with low-cost passive sensors. A key challenge here is to simultaneously achieve high probabilities of correct detection with low probabilities of false alarm under the constraints of limited computation and communication resources. In general, the changes in a dynamic environment may significantly affect the performance of target detection due to limited training scenarios and the assumptions made on signal behavior under a static environment. To this end, an algorithm of binary hypothesis testing is proposed based on clustering of features extracted from multiple sensors that may observe the target. First, the features are extracted individually from time-series signals of different sensors by using a recently reported feature extraction tool, called symbolic dynamic filtering. Then, these features are grouped as clusters in the feature space to evaluate homogeneity of the sensor responses. Finally, a decision for target detection is made based on the distance measurements between pairs of sensor clusters. The proposed procedure has been experimentally validated in a laboratory setting for mobile target detection. In the experiments, multiple homogeneous infrared sensors have been used with different orientations in the presence of changing ambient illumination intensities. The experimental results show that the proposed target detection procedure with feature-level sensor fusion is robust and that it outperforms those with decision-level and data-level sensor fusion.

Spectrum sensing in cognitive UWB impulse radio communication system

This paper emphasizes on the issue of spectrum sensing or awareness in cognitive UWB impulse radio (CUWB). Three spectrum sensing schemes are proposed in this paper: The first scheme is based on frame-level block processing, while the second scheme aims at real time chip-level processing. Analytical results show that scheme 1 outperforms scheme 2 in terms of the probability of detection and false-alarm, nevertheless, high detection delay may render the applicability of scheme 1 in CUWB. Employing the low duty cycle characteristics of the UWB IR signal, we propose a chip-level based sequential processing and diversity detection scheme (scheme 3). Through extensive simulations, the results demonstrate that scheme 3 has the best receiver operating characteristic (ROC) curve that can attain the design goal of high probability of detection and low probability of false alarm. Both analytical and numerical results in terms of the “mean detection delay” and the “average false alarm run length” have also been presented in this paper. It is verified that by employing the diversity combining, scheme 3 outperforms scheme 1 in that it requires much less detection time for spectrum sensing.

Extracting Target Detection Knowledge Based on Spatiotemporal Information in Wireless Sensor Networks

Wireless sensor networks (WSNs) have been deployed for many applications of target detection, such as intrusion detection and wildlife protection. In these applications, the first step is to detect whether the target is present or not. However, most of the existing work uses the “simple disk model” as signal model, which may not capture the sensing environment. In this work, we utilize a more realistic signal model to describe sensing process of sensors. On the other hand, the “majority rule” is widely used to make the final decision, which may not obtain the true judgment. To this end, we utilize a more realistic signal model and also use a probabilistic decision model to make the final decision. Moreover, we propose a probabilistic detection algorithm in which all sensors' local measurement values are fully used. This algorithm does not need any artificial threshold compared with traditional algorithms. It makes the most of spatiotemporal information to obtain the final decision. For the spatial perspective, sensors are distributed in different locations cooperating with each other. Meanwhile, for the temporal perspective, multiround subdecisions are fused. The effectiveness of the proposed method is validated by extensive simulation results, which show high detection probabilities and low false alarm probabilities.

Cooperative spectrum sensing with secondary user selection for cognitive radio networks over Nakagami‐ m fading channels

This paper investigates cooperative spectrum sensing (CSS) in cognitive wireless radio networks (CWRNs). A practical system is considered where all channels experience Nakagami-$m$ fading and suffer from background noise. The realisation of the CSS can follow two approaches where the final spectrum decision is based on either only the global decision at fusion centre (FC) or both decisions from the FC and secondary user (SU). By deriving closed-form expressions and bounds of missed detection probability (MDP) and false alarm probability (FAP), we are able to not only demonstrate the impacts of the $m$-parameter on the sensing performance but also evaluate and compare the effectiveness of the two CSS schemes with respect to various fading parameters and the number of SUs. It is interestingly noticed that a smaller number of SUs could be selected to achieve the lower bound of the MDP rather using all the available SUs while still maintaining a low FAP. As a second contribution, we propose a secondary user selection algorithm for the CSS to find the optimised number of SUs for lower complexity and reduced power consumption. Finally, numerical results are provided to demonstrate the findings.

Web Application Vulnerability Prediction Using Hybrid Program Analysis and Machine Learning

Due to limited time and resources, web software engineers need support in identifying vulnerable code. A practical approach to predicting vulnerable code would enable them to prioritize security auditing efforts. In this paper, we propose using a set of hybrid (static+dynamic) code attributes that characterize input validation and input sanitization code patterns and are expected to be significant indicators of web application vulnerabilities. Because static and dynamic program analyses complement each other, both techniques are used to extract the proposed attributes in an accurate and scalable way. Current vulnerability prediction techniques rely on the availability of data labeled with vulnerability information for training. For many real world applications, past vulnerability data is often not available or at least not complete. Hence, to address both situations where labeled past data is fully available or not, we apply both supervised and semi-supervised learning when building vulnerability predictors based on hybrid code attributes. Given that semi-supervised learning is entirely unexplored in this domain, we describe how to use this learning scheme effectively for vulnerability prediction. We performed empirical case studies on seven open source projects where we built and evaluated supervised and semi-supervised models. When cross validated with fully available labeled data, the supervised models achieve an average of 77 percent recall and 5 percent probability of false alarm for predicting SQL injection, cross site scripting, remote code execution and file inclusion vulnerabilities. With a low amount of labeled data, when compared to the supervised model, the semi-supervised model showed an average improvement of 24 percent higher recall and 3 percent lower probability of false alarm, thus suggesting semi-supervised learning may be a preferable solution for many real world applications where vulnerability data is missing.

Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

항만 감시를 위한 수중 강자성 표적 탐지에 관한 연구

Many countries have been developing and operating an underwater surveillance system in order to protect their oceanic environment from infiltrating hostile marine forces which intend to lay mines, conduct reconnaissance and destroy friendly ships anchored at the harbor. One of the most efficient methods to detect unidentified submarine approaching harbor is sensing variation of magnetism of target by magnetic sensors. This measurement system has an advantage of high possibility of detection and low probability of false alarm, compared to acoustic sensors, although it has relatively decreased detection range. The contents of this paper mainly cover the analysis of possible effectiveness of magnetic sensors. First of all, environmental characteristics of surveillance area and magnetic information of simulated targets has been analyzed. Subsequently, a signal processing method of separating target from geomagnetic field and methods of estimating target location has been proposed.

Higher Order Statistical Approach for Performance Evaluation of different Spectrum Sensing Techniques in Cognitive Radio Network

radio is an intelligent wireless technology that increases the spectrum efficiency for its usage in applications. CR enriches wireless technology by utilizing the spectrum holes in order to provide high order quality service to users and to minimize the interference that can occur in the network. The work that has to be proposed we will uses two Spectrum Sensing techniques for Cognitive radio network which include Cyclostationary detection and Energy detection techniques. In this paper, the parameter used for Cyclostationary signal is Spectral Correlation function(SCF). The detection capability of this (SCF) with different windows is used to check the periodicity of the signal using different-different windows. Due to the periodicity of the baseband signal, SCF would be able to detect the primary user signal at very low SNR. We also analyzed in our proposed work that capability of periodicity of the signal of SCF is not only limited to noise affected signal, it also able to detect the attenuated signal. We have also simulated Energy detection over MIMO fading channel as it models both Rician fading channel and Rayleigh fading channel. The performance in terms of Bit error rate by providing low probability of false alarm and high probability of detection is analyzed. The Statistical test based comparison is made between the two sensing techniques to evaluate the performance in terms of signal to noise ratio. In the proposed work An extensive set of simulations have been conducted in MATLAB . Keywordsradio, Energy Detection, Cyclostationary Detection

False alarm suppression of multipulsed laser ranging system with Geiger-mode detector.

The false alarm probability is of great concern when designing and evaluating the performance of a multipulsed laser ranging system with a Geiger-mode avalanche photodiode. In this paper, based on the statistical distribution difference of the arrival time of the echo photons and noise in the time histogram, a false alarm suppression algorithm is presented. According to the data-processing method of the algorithm, the theoretical model of target detection and false alarm probability with a Poisson statistic and the system working at long dead time is established. With typical system design parameters, the target detection probability under different echo intensity and detection number is analyzed, and the influence of four main factors, namely, detection number, echo intensity, noise, and echo position, on the false alarm probability is investigated. The results show that multipulsed detection can improve the target detection probability, and using this developed algorithm, the false alarm probability can be effectively suppressed, to obtain an appropriate false alarm probability; it is suitable that the detection number is selected as 8; and stronger echo intensity, lower noise level, and a more frontal echo position can result in a lower false alarm probability.

Maximizing Throughput with Wireless Spectrum Sensing Network Assisted Cognitive Radios

In a cognitive radio network (CRN), secondary users (SUs) utilize primary users (PUs) licensed spectrum in an opportunistic manner. Spectrum sensing is of the utmost importance in CRN to find and use the available spectrum without harmful interference to the PUs. Conventionally, to implement spectrum utilization, SUs are required to sense the primary spectrum first and then transmit data on the available spectrum. In this paper, we propose a dedicated wireless spectrum sensing network (WSSN), eliminating sensing overhead from SUs with the aim of improving achievable throughput. With WSSN assistance, we eliminate sensing time from the SUs frame, hence increasing the transmission time, which maximizes the achievable throughput. Additionally, the sensing duration is increased by deploying a dedicated WSSN, decreasing the probability of false alarm and achieving a targeted high probability of detection. A low probability of false alarm increases the spectrum utilization, improving the achievable throughput, while a high detection probability ensures PUs protection. Moreover, the proposed technique also addresses hidden and exposed terminal problems along with smooth spectrum mobility. Finally, we provide simulation results to demonstrate the proposed techniques, effectiveness. In the results, we have compared the achievable throughput of the proposed scheme with that of conventional CRN.

Maximum detection range limitation of pulse laser radar with Geiger-mode avalanche photodiode array

When designing and evaluating the performance of laser radar system, maximum detection range achievable is an essential parameter. The purpose of this paper is to propose a theoretical model of maximum detection range for simulating the Geiger-mode laser radar’s ranging performance. Based on the laser radar equation and the requirement of the minimum acceptable detection probability, and assuming the primary electrons triggered by the echo photons obey Poisson statistics, the maximum range theoretical model is established. By using the system design parameters, the influence of five main factors, namely emitted pulse energy, noise, echo position, atmospheric attenuation coefficient, and target reflectivity on the maximum detection range are investigated. The results show that stronger emitted pulse energy, lower noise level, more front echo position in the range gate, higher atmospheric attenuation coefficient, and higher target reflectivity can result in greater maximum detection range. It is also shown that it’s important to select the minimum acceptable detection probability, which is equivalent to the system signal-to-noise ratio for producing greater maximum detection range and lower false-alarm probability.

VANDER: Efficient Cooperative Watchdog Monitoring for Lossy Wireless Network Coding

Network coding achieves multicast network capacity by allowing intermediate nodes to mix information in packets. However, due to the mixing operation, network coding is vulnerable to pollution attacks. Different from conventional cryptographic solutions, watchdog-based solutions, which have been recently proposed for network coding, rely on trusted nodes to monitor and verify behaviors of transmission nodes. However, the impact of lossy wireless communications has not been considered. Since false alarm and misdetection depend on the loss rate, these false results can happen with high probability when packet loss happens. In this paper, we propose VANDER, which is a novel cooperative watchdog scheme in heterogeneous wireless networks, where multiple watchdogs collaborate and efficiently detect pollution attacks in a lossy wireless environment. The novelty of our approach is that, when lossy overhearing happens, watchdogs work cooperatively to share the packet information, where no extra overhead is introduced to normal transmission nodes, and rather than retransmitting all lost packets among watchdogs, watchdogs use randomly generated Vandermonde hashes to detect corrupted packets. Moreover, VANDER is capable of detecting successive colluded adversaries. In addition to the low false alarm and misdetection probabilities, VANDER also achieves low computational complexity and communication overhead. Numerical experiments are provided to support the theoretical analysis of VANDER.

Diagnosis of CO Pollution in HTPEM Fuel Cell using Statistical Change Detection

The fuel cell technologies are advancing and maturing for commercial markets. However proper diagnostic tools needs to be developed in order to insure reliability and durability of fuel cell systems. This paper presents a design of a data driven method to detect CO content in the anode gas of a high temperature fuel cell. In this work the fuel cell characterization is based on an experimental equivalent electrical circuit, where model parameters are mapped as a function of the load current. The designed general likelihood ratio test detection scheme detects whether a equivalent electrical circuit parameter differ from the non-faulty operation. It is proven that the general likelihood ratio test detection scheme, with a very low probability of false alarm, can detect CO content in the anode gas of the fuel cell.

Robust Spectrum Sensing Demonstration Using a Low-Cost Front-End Receiver

Spectrum Sensing (SS) is an important function in Cognitive Radio (CR) to detect primary users. The design of SS algorithms is one of the most challenging tasks in CR and requires innovative hardware and software solutions to enhance detection probability and minimize low false alarm probability. Although several SS algorithms have been developed in the specialized literature, limited work has been done to practically demonstrate the feasibility of this function on platforms with significant computational and hardware constraints. In this paper, SS is demonstrated using a low cost TV tuner as agile front-end for sensing a large portion of the Ultra-High Frequency (UHF) spectrum. The problems encountered and the limitations imposed by the front-end are analysed along with the solutions adopted. Finally, the spectrum sensor developed is implemented on an Android device and SS implementation is demonstrated using a smartphone.

Spectrum Sensing Techniques for Cognitive Radio-A Review

Cognitive Radio (CR) users need to sense the environment or channel at regular time interval for sharing the spectrum band of the primary users (PUs). Once find the spectrum idle, CR users start their transmission through it. Even while transmitting, they need to continue the sensing process so that they can leave the spectrum immediately whenever find a PU wanting to use the band. Therefore, detecting PUs is one of the main functions of cognitive radio before transmission and higher the detection probability ensures better protection to the primary users. However, it is not possible to attain a high detection probability (or a low miss detection probability) and low false alarm probability simultaneously as there is a tradeoff between false alarm probability (P fa ) and the probability of detection (P d ). In this paper, the author has provided a comprehensive study on different sensing techniques and discussed their advantages and disadvantages. Moreover, it is expected that, with this article, readers can have a through understanding of sensing techniques in CR and the current research trends in this area.

On design of physical random access channel in high-speed railway LTE systems

For the uplink in the long-term evolution (LTE) systems, the physical random access channel (PRACH) adopts Zadoff–Chu (ZC) sequences as preamble sequences, and each user can choose a unique cyclic shift for user identification. In high-mobility scenario, the Doppler frequency shift raises up the false alarm probability by inducing the correlation peak offsetting at the receiver side. Traditional detection method utilizes multiple search windows for searching the potential uplink users. When the Doppler frequency shift is larger than the subcarrier spacing, the energy of correlation peak leaks outside the search window and increases the false alarm probability. In this paper, we present a novel design for PRACH preamble sequence for high-speed railway scenario. Through numerical observation, we find out that the offset of the correlation peaks only depends on the initial root sequence number and the length of ZC sequences. Therefore, the root number of ZC sequences should be carefully designed to generate a preamble sequence to fight against the Doppler frequency shift. Consequently, those offsets of the correlation peaks are concentrated within a single search window, resulting in lower complexity of detection and lower probability of false alarm for random access in LTE uplink.

Blind spectrum sensing using symmetry property of cyclic autocorrelation function: from theory to practice

Abstract Spectrum sensing has been identified as the key step of the cognition cycle and the most important function for the establishment of cognitive radio. In this paper, a blind cyclostationary feature detector, which is based on the symmetry property of cyclic autocorrelation function (SP-CAF), is implemented and tested using universal software radio peripheral platform and GNU Radio open-source software development toolkit. Performance of the SP-CAF is compared to the classical energy detector via various tests conducted in real scenarios where both detection algorithms are employed to blindly sense the spectrum for opportunistic access. This study shows that the blind cyclostationary feature detector outperforms the classical energy detector while guaranteeing acceptable complexity and low sensing time. Moreover, different experimental results indicate that the blind sensing detector can achieve high detection probability at a low false alarm probability under real channel conditions and low signal-to-noise ratio.

Characterization and classification of malicious Web traffic

Web systems commonly face unique set of vulnerabilities and security threats due to their high exposure, access by browsers, and integration with databases. This study is focused on characterization and classification of malicious cyber activities aimed at Web systems. The empirical analysis is based on three datasets, each in duration of four to five months, collected by high-interaction honeypots which ran fully functional three-tier Web systems. We first explore the types and prevalence of malicious scans and attacks to Web systems, and the extent to which these malicious activities differ in different periods of time or on Web servers running different services. In addition to descriptive statistical analysis, we include an inferential statistical analysis of the malicious session attributes, such as duration, number of requests and bytes transferred in a session. Then, we use supervised machine learning methods to classify attacker activities to two classes: vulnerability scans and attacks. Our main observations include the following: (1) Some characteristics of the malicious Web traffic were invariant across different servers and time periods, such as for example the dominant use of the search-based strategy for attacking the servers and the heavy-tailed behavior of session attributes. (2) On the other side, servers running different services experienced almost complementary profiles of vulnerability scan and attack types. (3) Supervised learning methods efficiently distinguished attack sessions from vulnerability scan sessions, with high probability of detection and very low probability of false alarms. (4) Decision tree based methods J48 and PART performed better than SVM across all datasets. (5) Attacks differed from vulnerability scans only in a small number of session attributes; depending on the dataset, classification of malicious activities can be performed using from four to six features without significantly affecting learners' performance compared to when all 43 features were used.

Quickest Detection of Multi-channel Based on STFT and Compressed Sensing

This paper proposes a multi-channel quickest detection method based on compressed sensing and short-time Fourier transform. Quickest detection performs a statistical test to obtain the minimal detection delay subject to given false alarm constrains. Short-time Fourier transform, which reflects the time---frequency information, implements the multi-channel quickest detection. Compressed sensing reduces the sampling rate at first. Compared with single-channel spectrum sensing, this method substantially improves the spectrum access opportunity in time and frequency domain. The relationship between the detection delay and other parameters, such as the probability of false alarm, SNR, sparsity, and sampling rate, verifies the validity of the method. While simulation results show that this method can perform spectrum sensing in high detection probability and low probability of false alarm.

Trend analysis of medium- and coarse-resolution time series image data for burned area mapping in a Mediterranean ecosystem

In this study, the Breaks for Additive Seasonal and Trend (BFAST), a recently introduced trend analysis technique, was employed for the detection of fire-induced changes in a Mediterranean ecosystem. BFAST enables the decomposition of time series into trend, seasonal and noise components, resulting in the detection of gradual and rapid land cover changes. Normalised Difference Vegetation Index (NDVI) time series derived from the MODIS and VEGETATION (VGT) standard products were analysed. The time series decomposition resulted in the mapping of the burned area and the demonstration of the post-fire vegetation recovery trend. The observed gradual changes revealed an increase of NDVI values over time, indicating post-fire vegetation recovery. Spatial validation of the generated burned area maps with a higher resolution reference map was performed and probability statistics were derived. Both maps achieved a high probability of detection – 0.90 for MODIS and 0.87 for VGT – and a low probability of false alarms, 0.01 for MODIS and 0.02 for VGT. In addition, the Pareto boundary theory was implemented to account for the low-resolution bias of the maps. BFAST facilitated detection of fire-induced changes using image time series, without having to set thresholds, select specific seasons or adjust to certain land cover types. Further evaluation of the approach should focus on a more comprehensive assessment across regions and time.