• Title/Summary/Keyword: Detection Rate

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Face Detection Using Pixel Direction Code and Look-Up Table Classifier (픽셀 방향코드와 룩업테이블 분류기를 이용한 얼굴 검출)

  • Lim, Kil-Taek;Kang, Hyunwoo;Han, Byung-Gil;Lee, Jong Taek
    • IEMEK Journal of Embedded Systems and Applications
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    • v.9 no.5
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    • pp.261-268
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    • 2014
  • Face detection is essential to the full automation of face image processing application system such as face recognition, facial expression recognition, age estimation and gender identification. It is found that local image features which includes Haar-like, LBP, and MCT and the Adaboost algorithm for classifier combination are very effective for real time face detection. In this paper, we present a face detection method using local pixel direction code(PDC) feature and lookup table classifiers. The proposed PDC feature is much more effective to dectect the faces than the existing local binary structural features such as MCT and LBP. We found that our method's classification rate as well as detection rate under equal false positive rate are higher than conventional one.

Evolutionary Learning of Neural Networks Classifiers for Credit Card Fraud Detection (신용카드 사기 검출을 위한 신경망 분류기의 진화 학습)

  • 박래정
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.5
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    • pp.400-405
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    • 2001
  • This paper addresses an effective approach of training neural networks classifiers for credit card fraud detection. The proposed approach uses evolutionary programming to trails the neural networks classifiers based on maximization of the detection rate of fraudulent usages on some ranges of the rejection rate, loot minimization of mean square error(MSE) that Is a common criterion for neural networks learning. This approach enables us to get classifier of satisfactory performance and to offer a directive method of handling various conditions and performance measures that are required for real fraud detection applications in the classifier training step. The experimental results on "real"credit card transaction data indicate that the proposed classifiers produces classifiers of high quality in terms of a relative profit as well as detection rate and efficiency.

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Local Binary Feature and Adaptive Neuro-Fuzzy based Defect Detection in Solar Wafer Surface (지역적 이진 특징과 적응 뉴로-퍼지 기반의 솔라 웨이퍼 표면 불량 검출)

  • Ko, JinSeok;Rheem, JaeYeol
    • Journal of the Semiconductor & Display Technology
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    • v.12 no.2
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    • pp.57-61
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    • 2013
  • This paper presents adaptive neuro-fuzzy inference based defect detection method for various defect types, such as micro-crack, fingerprint and contamination, in heterogeneously textured surface of polycrystalline solar wafers. Polycrystalline solar wafer consists of various crystals so the surface of solar wafer shows heterogeneously textures. Because of this property the visual inspection of defects is very difficult. In the proposed method, we use local binary feature and fuzzy reasoning for defect detection. Experimental results show that our proposed method achieves a detection rate of 80%~100%, a missing rate of 0%~20% and an over detection (overkill) rate of 9%~21%.

A Experimental Study on the Heat Release Rate to activate Fire Detection Sensor (화재감지 센서 작동시간 및 열방출률에 대한 실험연구)

  • Hong, Sung-Ho
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.9
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    • pp.1358-1361
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    • 2012
  • This paper presents a study on the analysis for activation time and threshold value of heat detection sensor using HRR(Heat Release Rate). And it is represented to quantity of heat to activate heat detection sensor. The experiment is conducted to measure activation time and HRR of fire detection sensor burning alcohol and n-heptane. In order to burn the alcohol and n-heptane using $43.5cm(L){\times}43.5cm(W){\times}5cm(D)$ and $33cm(L){\times}33cm(L){\times}5cm(D)$ steel pan and the quantity of alcohol and n-heptane are 2.5 L and 650 g, respectively. The results show that peak HRR are in case of alcohol 66.13 kW and in case of n-heptane 151.64 kW, respectively. Total heat releases of heat detection sensor are in case of alcohol approximately 20.7 MJ and in case of n-heptane approximately 18 MJ, respectively.

A Study on Real-time Face Detection in Video (동영상에서 실시간 얼굴검출에 관한 연구)

  • Kim, Hyeong-Gyun;Bae, Yong-Guen
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.2
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    • pp.47-53
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    • 2010
  • This paper proposed Residual Image detection and Color Info using the face detection technique. The proposed technique was fast processing speed and high rate of face detection on the video. In addition, this technique is to detection error rate reduced through the calibration tasks for tilted face image. The first process is to extract target image from the transmitted video images. Next, extracted image processed by window rotated algorithm for detection of tilted face image. Feature extraction for face detection was used for AdaBoost algorithm.

A Design of SWAD-KNH Scheme for Sensor Network Security (센서 네트워크 보안을 위한 SWAD-KNH 기법 설계)

  • Jeong, Eun-Hee;Lee, Byung-Kwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.6
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    • pp.1462-1470
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    • 2013
  • This paper proposes an SWAD-KNH(Sybil & Wormhole Attack Detection using Key, Neighbor list and Hop count) technique which consists of an SWAD(Sybil & Wormhole Attack Detection) module detecting an Worm attack and a KGDC(Key Generation and Distribution based on Cluster) module generating and an sense node key and a Group key by the cluster and distributing them. The KGDC module generates a group key and an sense node key by using an ECDH algorithm, a hash function, and a key-chain technique and distributes them safely. An SWAD module strengthens the detection of an Sybil attack by accomplishing 2-step key acknowledgement procedure and detects a Wormhole attack by using the number of the common neighbor nodes and hop counts of an source and destination node. As the result of the SWAD-KNH technique shows an Sybil attack detection rate is 91.2% and its average FPR 3.82%, a Wormhole attack detection rate is 90%, and its average FPR 4.64%, Sybil and wormhole attack detection rate and its reliability are improved.

DGA-based Botnet Detection Technology using N-gram (N-gram을 활용한 DGA 기반의 봇넷 탐지 방안)

  • Jung Il Ok;Shin Deok Ha;Kim Su Chul;Lee Rock Seok
    • Convergence Security Journal
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    • v.22 no.5
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    • pp.145-154
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    • 2022
  • Recently, the widespread proliferation and high sophistication of botnets are having serious consequences not only for enterprises and users, but also for cyber warfare between countries. Therefore, research to detect botnets is steadily progressing. However, the DGA-based botnet has a high detection rate with the existing signature and statistics-based technology, but also has a high limit in the false positive rate. Therefore, in this paper, we propose a detection model using text-based n-gram to detect DGA-based botnets. Through the proposed model, the detection rate, which is the limit of the existing detection technology, can be increased and the false positive rate can also be minimized. Through experiments on large-scale domain datasets and normal domains used in various DGA botnets, it was confirmed that the performance was superior to that of the existing model. It was confirmed that the false positive rate of the proposed model is less than 2 to 4%, and the overall detection accuracy and F1 score are both 97.5%. As such, it is expected that the detection and response capabilities of DGA-based botnets will be improved through the model proposed in this paper.

Evaluation of Giarydia lumblia Detection Method in Stool Specimens Fixed with SAF Solution (SAF 고정변에서 람블편모충(Giardia lamblia)시스트 검출을 위한 검사방법의 평가)

  • Kim, Yeong-Chang;Kim, Jae-Jin;Lee, Geun-Tae
    • Parasites, Hosts and Diseases
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    • v.23 no.2
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    • pp.285-292
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    • 1985
  • The present study has been designed as a basic study on laboratory diagnosis of giardiasis and to demonstrate a more effective method for the detection of Giardia lamblia cyst with the inherent advantages of minimizing both the number of stool examinations required and the interval of stool collections for estimating the real state of prevalence in the shortest time possible. There were 3 subject groups of 75 children each currently residing in an orphanage in Gunsan city, Jeonbuk province from which stool specimens were collected every day, every other day, and every 3 days. The procedure is as follows: 1) resuspend the InKed sample after fixation with SAF solution 2) centrifuge the sediment for 1 min. at 2, 000 rpm after straining through gauze into a tube 3) divide the sediment into 3 parts and use them for direct fecal smear, formalin-ether concentration (MGL) and zinc sulfate ($ZnSO_4$) floatation techniques. The results are summarized as follows: 1. Overall infection rate after 10 trials showed a 60% positive indication. The positive rate among children under 4 years old was significantly higher than the rate in children over 4 years old. No significant difference in rate by sex was observed. 2. The results of eBaminations by direct fecal smear and MGL techniques appeared more accurate than that obtained by $ZnSO_4$ ftoatation method as indicated by a higher positive rate. Of all three methods concerned, combinations of two demonstrated a higher positive rate than that shown by any one alone. 3. In three consecutive examinations under varying conditions such as different days, the cyst detection rate by MGL technique indicated 83%. In 5 examinations under the same varying conditions, the indicated rate was 94%. 4. The interval of stool collection proved to be insignificant for the cyst detection rate. In conclusion, both MGL method and modified fecal direct smear can provide a good cyst detection rate of G. lamblia provided that more than 3 consecutive examinations of stool under varying conditions are carried out.

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Assessment of Collaborative Source-Side DDoS Attack Detection using Statistical Weight (통계적 가중치를 이용한 협력형 소스측 DDoS 공격 탐지 기법 성능 평가)

  • Yeom, Sungwoong;Kim, Kyungbaek
    • KNOM Review
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    • v.23 no.1
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    • pp.10-17
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    • 2020
  • As the threat of Distributed Denial-of-Service attacks that exploit weakly secure IoT devices has spread, research on source-side Denial-of-Service attack detection is being activated to quickly detect the attack and the location of attacker. In addition, a collaborative source-side attack detection technique that shares detection results of source-side networks located at individual sites is also being activated to overcome regional limitations of source-side detection. In this paper, we evaluate the performance of a collaborative source-side DDoS attack detection using statistical weights. The statistical weight is calculated based on the detection rate and false positive rate corresponding to the time zone of the individual source-side network. By calculating weighted sum of the source-side DoS attack detection results from various sites, the proposed method determines whether a DDoS attack happens. As a result of the experiment based on actual DNS request to traffic, it was confirmed that the proposed technique reduces false positive rate 2% while maintaining a high attack detection rate.

Intrusion Detection: Supervised Machine Learning

  • Fares, Ahmed H.;Sharawy, Mohamed I.;Zayed, Hala H.
    • Journal of Computing Science and Engineering
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    • v.5 no.4
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    • pp.305-313
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    • 2011
  • Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.