• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.51-58
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• 2018

Recently, various satellite sensors have been developed and it is becoming more convenient to acquire multitemporal satellite images. Therefore, various researches are being actively carried out in the field of utilizing change detection techniques such as disaster and land monitoring using multitemporal satellite images. In particular, researches related to the development of unsupervised change detection techniques capable of extracting rapidly change regions have been conducted. However, there is a disadvantage that false detection occurs due to a spectral difference such as a seasonal change. In order to overcome the disadvantages, this study aimed to reduce the false alarm detection due to seasonal effects using the direction vector generated by applying the $S^2CVA$ (Sequential Spectral Change Vector Analysis) technique, which is one of the unsupervised change detection methods. $S^2CVA$ technique was applied to RapidEye images of the same and different seasons. We analyzed whether the change direction vector of $S^2CVA$ can remove false positives due to seasonal effects. For the quantitative evaluation, the ROC (Receiver Operating Characteristic) curve and the AUC (Area Under Curve) value were calculated for the change detection results and it was confirmed that the change detection performance was improved compared with the change detection method using only the change magnitude vector.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.157-165
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• 2024

The evolution of the Distributed Denial of Service Attack(DDoS Attack) method has increased the difficulty in the detection process. One of the solutions to overcome the problems caused by the limitations of the existing victim-side detection method was the source-side detection technique. However, there was a problem of performance degradation due to network traffic irregularities. In order to solve this problem, research has been conducted to detect attacks using a collaborative network between several nodes based on artificial intelligence. Existing methods have shown limitations, especially in nonlinear traffic environments with high Burstness and jitter. To overcome this problem, this paper presents a collaborative source-side DDoS attack detection technique introduced with an attention mechanism. The proposed method aggregates detection results from multiple sources and assigns weights to each region, and through this, it is possible to effectively detect overall attacks and attacks in specific few areas. In particular, it shows a high detection rate with a low false positive of about 6% and a high detection rate of up to 4.3% in a nonlinear traffic dataset, and it can also confirm improvement in attack detection problems in a small number of regions compared to methods that showed limitations in the existing nonlinear traffic environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1799-1818
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• 2018

Malware detections continue to be a challenging task as attackers may be aware of the rules used in malware detection mechanisms and constantly generate new breeds of malware to evade the current malware detection mechanisms. Consequently, novel and innovated malware detection techniques need to be investigated to deal with this circumstance. In this paper, we propose a new secure malware detection system in which API call fragments are used to recognize potential malware instances, and these API call fragments together with the homomorphic encryption technique are used to construct a privacy-preserving Naive Bayes classifier (PP-NBC). Experimental results demonstrate that the proposed PP-NBC can successfully classify instances of malware with a hit-rate as high as 94.93%.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.13-20
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• 2004

In the real-time automatic face recognition technique, accurate face detection is essential and very important part because it has the effect to face recognition performance. In this paper, we use color information, edge information, and binary information to detect candidate regions of eyes from Input image, and then detect face candidate region using the center point of the detected eyes. We verify both eye candidate region and face candidate region using Support Vector Machines(SVM). It is possible to perform fast and reliable face detection because we can protect false detection through these verification process. From the experimental results, we confirmed the Proposed algorithm in this paper shows excellent face detection rate over 99%.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.674-684
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• 2002

Anomaly detection techniques have teen devised to address the limitations of misuse detection approach for intrusion detection. An HMM is a useful tool to model sequence information whose generation mechanism is not observable and is an optimal modeling technique to minimize false-positive error and to maximize detection rate, However, HMM has the short-coming of login training time. This paper proposes an effective HMM-based IDS that improves the modeling time and performance by only considering the events of privilege flows based on the domain knowledge of attacks. Experimental results show that training with the proposed method is significantly faster than the conventional method trained with all data, as well as no loss of recognition performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5574-5591
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• 2017

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4021-4037
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• 2018

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.276-282
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• 2013

Self-encoded spread spectrum (SESS) is a novel modulation technique that acquires its spreading code from a random information source, rather than using the traditional pseudo-random noise (PN) codes. In this paper, we present our study of the SESS system performance under pulsed-noise jamming and show that iterative detection can significantly improve the bit error rate (BER) performance. The jamming performance of the SESS with correlation detection is verified to be similar to that of the conventional direct sequence spread spectrum (DSSS) system. On the other hand, the time diversity detection of the SESS can completely mitigate the effect of jamming by exploiting the inherent temporal diversity of the SESS system. Furthermore, iterative detection with multiple iterations can not only eliminate the jamming completely but also achieve a gain of approximately 1 dB at $10^{-3}$ BER as compared with the binary phase shift keying (BPSK) system under additive white gaussian noise (AWGN) by effectively combining the correlation and time diversity detections.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.6
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• pp.417-424
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• 2010

This paper proposes a new islanding detection method for a grid-connected photovoltaic system. It is based on the fact that the equivalent harmonic components vary according to the grid connection status. The advantage of the proposed method is the reduced Non-Detection Zone and fast detection time. Also it can have the robust detection capability against grid disturbances. The theoretic analysis using grid-harmonic modeling is performed and verified by test result using 32-bit high performance DSP processor.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.860-868
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• 2008

Human detection techniques in outdoor scenes have been studied for a long time to watch suspicious movements or to keep someone from danger. However there are few methods of human detection in overhead or near-field view scenes, while lots of human detection methods in far-field view scenes have been developed. In this paper, a set of five features useful for human detection in overhead view scenes and another set of four useful features in near-field view scenes are suggested. Eight feature-candidates are first extracted by analyzing geometrically varying characteristics of moving objects in samples of video sequences. Then highly contributed features for each view scene to classifying human from other moving objects are selected among them by using a neural network learning technique. Through experiments with hundreds of moving objects, we found that each set of features is very useful for human detection and classification accuracy for overhead view and near-field view scenes was over 90%. The suggested sets of features can be used effectively in a PTZ camera based surveillance system where both the overhead and near-field view scenes appear.