• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.101-111
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• 2023

Many spoofing detection studies have been conducted to cope with the most difficult types of deception among various disturbances of GPS, such as jamming, spoofing, and meaconing. In this paper, we propose a spoofing detection scheme based on elevation masked-relative received power between GPS L1 and L2 signals in a system using a multi-band array antenna. The proposed scheme focuses on enabling spoofing to be normally detected and minimizes the possibility of false detection in an environment where false alarms may occur due to pattern distortion among elements of an array antenna. The pattern distortion weakens the GPS signal strength at low elevation. It becomes confusing to detect a spoofing signal based on the relative power difference between GPS L1 and L2, especially when GPS L2 has weak signal strength. We propose design parameters for the relative power threshold including beamforming gain, the minimum received power difference between L1 and L2, and the patch antenna gain difference between L1 and L2. In addition, in order to eliminate the weak signal strength of GPS L2 in the spoofing detection process, we propose a rotation matrix that sets the elevation mask based on platform coordinates. Array antennas generally do not have high usefulness in commercial areas where receivers are operated alone, but are considered essential in military areas where GPS receivers are used together with signal processing for beamforming in the direction of GPS satellites. Through laboratory and live sky tests using the device under test, the proposed scheme with an elevation mask detects spoofing signals well and reduces the probability of false detection relative to that without the elevation mask.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.87-90
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• 2002

This paper has been studied a new approach using neural network to detect false laser marks. In the proposed approach, input images are segmented into R, G and B colors and implements mask areas respectively. And then average and variation values of the each mask area are extracted for the learning process to minimize input nodes. Using this technique, the new input data is obtained and implemented to the back-propagation algorithm using multi layer perception. This paper reduces the computational complexity necessary and shows better effectiveness to inspect false laser marks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10A
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• pp.775-779
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• 2003

A spatio-temporal searcher structure for 3rd generation W-CDMA systems is proposed to enhance the detection capability of the multi-path searcher for the desired signal. This searcher employs the spatio-temporal signal structure to search for newly emerging multipath signals. The proposed multi-path searcher provides better detection capability andthus reduces the mean acquisition time. The detection and false alarm probabilities of new and conventional schemes are calculated and numerical examples of mean acquisition time are given thereafter.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1073-1080
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• 2011

In this study, a target detection algorithm was proposed for using hyperspectral imagery. The proposed algorithm is designed to have minimal processing time, low false alarm rate, and flexible threshold selection. The target detection procedure can be divided into two steps. Initially, candidates of target pixel are extracted using matching ratio of spectral pattern that can be calculated by spectral derivation. Secondly, spectral distance is computed only for those candidates using Euclidean distance. The proposed two-step method showed lower false alarm rate than the Euclidean distance detector applied over the whole image. It also showed much lower processing time as compared to the Mahalanobis distance detector.

• ETRI Journal
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• v.43 no.1
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• pp.31-39
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• 2021

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.67-74
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• 2022

One type of network security breach is the availability breach, which deprives legitimate users of their right to access services. The Denial of Service (DoS) attack is one way to have this breach, whereas using the Intrusion Detection System (IDS) is the trending way to detect a DoS attack. However, building IDS has two challenges: reducing the false alert and picking up the right dataset to train the IDS model. The survey concluded, in the end, that using a real dataset such as MAWILab or some tools like ID2T that give the researcher the ability to create a custom dataset may enhance the IDS model to handle the network threats, including DoS attacks. In addition to minimizing the rate of the false alert.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• The Journal of the Korea Contents Association
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• v.3 no.1
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• pp.31-38
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• 2003

In this paper, We propose detection mood generation system using learning to generate automatically detection model. It is improved manpower, efficiency in time. Proposed detection model generator system is consisted of agent system and manager system. Model generation can do existing standardization by genetic algorithm because do model generation and apply by new detection model. according to experiment results, detection model generation using learning proposed sees more efficiently than existing intrusion detection system. When intrusion of new type occur by implemented system and decrease of the False-Positive rate, improve performance of existing intrusion detection system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1043-1057
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• 2015

In order to recognize intelligent threats quickly and detect and respond to them actively, major public bodies and private institutions operate and administer an Intrusion Detection Systems (IDS), which plays a very important role in finding and detecting attacks. However, most IDS alerts have a problem that they generate false positives. In addition, in order to detect unknown malicious codes and recognize and respond to their threats in advance, APT response solutions or actions based systems are introduced and operated. These execute malicious codes directly using virtual technology and detect abnormal activities in virtual environments or unknown attacks with other methods. However, these, too, have weaknesses such as the avoidance of the virtual environments, the problem of performance about total inspection of traffic and errors in policy. Accordingly, for the effective detection of intrusion, it is very important to enhance security monitoring, consequentially. This study discusses a plan for the reduction of false positives as a plan for the enhancement of security monitoring. As a result of an experiment based on the empirical data of G, rules were drawn in three types and 11 kinds. As a result of a test following these rules, it was verified that the overall detection rate decreased by 30% to 50%, and the performance was improved by over 30%.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.77-88
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• 2004

This study aims at developing the model that is able to detect the compression wave, which is included as a similar situation in incidents, that causes false applicable to the similar character such as incidents in the incident detection model for expressways. In this study, it has been checked whether the number of false alarms is decreased or not by modularizing this model for being able to applicable to other models such as DES and DELOS, etc. which do not perform the compression wave test based on the compression wave test process of APID model which has been being used in the expressway traffic management system currently. The evaluation in this study focuses on the sensitivity of the model and the results analysis is performed classified by each polling cycle. And how well these models are working is evaluated by each polling cycle. In addition to this, the detection rate, the false alarm rate and the average detection time in both the existing models and the model in this study are calcuated. As a result of appling the model in this study, it is found that the false alarm rate is improved through the reasonable decrease in the number of false alarm frequencies and there are not remarkable changes concerning the detection rate and the average detection time. To sum up, it is expected that a good number of improvement effects will be occurred when this model is applied to the actual expressway traffic management system.