• Journal of Communications and Networks
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• v.11 no.6
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• pp.544-555
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• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.378-384
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• 2015

Indoor localization is becoming one of the most important technologies for smart mobile applications with different requirements from conventional outdoor location estimation algorithms. Fingerprinting location estimation techniques based on neural networks have gained increasing attention from academia due to their good generalization properties. In this paper, we propose a novel location estimation algorithm based on an ensemble of multiple neural networks. The neural network ensemble has drawn much attention in various areas where one neural network fails to resolve and classify the given data due to its' inaccuracy, incompleteness, and ambiguity. To the best of our knowledge, this work is the first to enhance the location estimation accuracy in indoor wireless environments based on a neural network ensemble using fingerprinting training data. To evaluate the effectiveness of our proposed location estimation method, we conduct the numerical experiments using the TGn channel model that was developed by the 802.11n task group for evaluating high capacity WLAN technologies in indoor environments with multiple transmit and multiple receive antennas. The numerical results show that the proposed method based on the NNE technique outperforms the conventional methods and achieves very accurate estimation results even in environments with a low number of APs.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.620-629
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• 2003

Fingerprinting schemes are techniques applied to protect the copyright on digital goods. These enable the merchants to identify the source of illegal redistribution. Let us assume the following situations connectedly happen: As a beginning, buyer who bought digital goods illegally distributed it, next the merchant who found it revealed identity of the buyer/traitor, then the goods is illegally distributed again. After this, we describe it as“The second illegal redistribution”. In most of anonymous fingerprinting, upon finding a redistributed copy, a merchant extracts the buyer's secret information from the copy and identifies a traitor using it. Thus the merchant can know the traitor's secret information (digital fingerprints) after identification step. The problem of the second illegal distribution is that there is a possibility of the merchant's fraud and the buyer's abuse: that is a dishonest employee of the merchant might just as well have redistributed the copy as by the buyer, or the merchant as such may want to gain money by wrongly claiming that the buyer illegally distributed it once more. The buyer also can illegally redistribute the copy again. Thus if the copy turns up, one cannot really assign responsibility to one of them. In this paper, we suggest solution of this problem using two-level fingerprinting. As a result, our scheme protects the buyer and the merchant under any conditions in sense that (1) the merchant can obtain means to prove to a third party that the buyer redistributed the copy. (2) the buyer cannot worry about being branded with infamy as a traitor again later if he never distribute it.

• Mycobiology
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• v.36 no.3
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• pp.161-166
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• 2008

The retrotransposon marY1 is a gypsy family retroelement, which is detected ubiquitously within the fungal taxonomic groups in which mushrooms are included. To utilize marY1 as a molecular marker for the DNA fingerprinting of mushrooms, oligonucleotides marY1-LTR-L and marY1-LTR-R were designed on the basis of highly conserved regions from the multiple sequence alignment of 30 marY1 sequences retrieved from a nucleotide sequence database. In accordance with $\underline{Re}trotransposon$ $\underline{M}icrosatellite$ $\underline{A}mplified$ $\underline{P}olymorphism$ (REMAP) fingerprinting methodology, the two oligonucleotides were utilized together with the short sequence repeat primers UBC807 and UBC818 for polymerase chain reaction using templates from different mushroom genomic DNAs. Among the tested oligonucleotides, the marY1-LTR-L and UBC807 primer set yielded the greatest amount of abundance and variation in terms of DNA band numbers and patterns. This method was successfully applied to 10 mushroom species, and the primer set successfully discriminated between different commercial mushroom cultivars of the same strains of 14 Pleurotus ostreatus and 16 P. eryngii. REMAP reproducibility was superior to other popular DNA fingerprinting methodologies including the random amplified polymorphic DNA method.

• Food Science of Animal Resources
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• v.26 no.3
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• pp.394-401
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• 2006

Forty Clostridium perfringens isolates were obtained from twelve animal products, following the examination of eighty six beef, pork, broiler chicken and salami meat products, and eleven milk powder products. There were 21 isolates from salami stored at $25^{\circ}C$, 3 isolates from pork, 4 isolates from beef, 9 isolates from broiler chicken, and 3 isolates from milk powder. Only the cpa gene encoding a toxin among the 5 toxin genes tested (cpa, cpb, etx, iap, and cpe) was detected in all forty isolates, suggesting contamination with C. perfringens type A. DNA fingerprinting analysis using PCR of the tRNA intergenic spacer (tDNA-PCR) and the 16S-23S internal transcribed spacer (ITS-PCR), and randomly amplified polymorphic DNA (RAPD) analysis were attempted to differentiate the isolates. RAPD analysis was the most discriminating method among the three PCR analyses. Isolates from the same products tended to show similar RAPD patterns. Antimicrobial susceptibility tests showed that some isolates from broiler chickens had the same antibiogram with multiple resistance to streptomycin, colistin, and ciprofloxacin. Antibiograms were similar between isolates from the same livestock products, but differed considerably between the products.

• Journal of Periodontal and Implant Science
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• v.25 no.1
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• pp.153-166
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• 1995

54 clinical isolates of Actinobacillus actinomycetemcomitans showed distinct hybrdization patern(DAN fingerprinting patterns) when the bacterial DNA were hybridized with randomly cloned 4.7 - kb DNA probe. The frequency of the genotypic distribution demonstrated that type C was the most prevalent genotype, the next being D, NT, A, B, and E in the descending order. The most prevalent serotype was serotype c, the next being a, nd, and b in the descending order. It was noted that the one serotype can represent more than two different genotypes and that multiple genotypic variants can also exist in the periodontal pockets within the sam subject.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.1-9
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• 2012

The popularity of digital music and smart phones led to develope noise-robust real-time audio fingerprinting system in various ways. In particular, The Multiple Hashing(MLH) of fingerprint algorithms is robust to noise and has an elaborate structure. In this paper, we propose a filter engine based on MLH to achieve better performance. In this approach, we compose a energy-intensive filter to improve the accuracy of Q/R from music database and a statistic filter to remove continuity and redundancy. The energy-intensive filter uses the Discrite Cosine Transform(DCT)'s feature gathering energy to low-order bits and the statistic filters use the correlation between searched fingerprint's information. Experimental results show that the superiority of proposed algorithm consists of the energy and statistical filtering in noise environment. It is found that the proposed filter engine achieves more robust to noise than Philips Robust Hash(PRH), and a more compact way than MLH.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1179-1195
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• 2018

Recently, a variety of attacks on web services have been occurring through a multiple access environment such as PC, tablet, and smartphone. These attacks are causing various subsequent damages such as online fraud transactions, takeovers and theft of accounts, fraudulent logins, and information leakage through web service vulnerabilities. Creating a new fake account for Fraud attacks, hijacking accounts, and bypassing IP while using other usernames or email addresses is a relatively easy attack method, but it is not easy to detect and block these attacks. In this paper, we have studied a method to detect online fraud transaction and obsession by identifying and managing devices accessing web service using web-based device fingerprinting. In particular, it has been proposed to identify devices and to manage them by scoring process. In order to secure the validity of the proposed scheme, we analyzed the application cases and proved that they can effectively defend against various attacks because they actively cope with online fraud and obtain visibility of user accounts.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.125-133
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So intuitive fuzzy c-means(IFCM) clustering algorithm is applied to improve KNN, which is the KNN/IFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of IFCM based on signal to noise ratio(SNR). Then, the k RPs are classified into different clusters through IFCM based on SNR. Experimental results indicate that the proposed KNN/IFCM hybrid algorithm generally outperforms KNN, KNN/FCM, KNN/PFCM algorithm when the locations error is less than 2m.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.109-115
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• 2011

As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So artificial neural network(ANN) clustering algorithm is applied to improve KNN, which is the KNN/ANN hybrid algorithm presented in this paper. For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of ANN based on SNR. Then, the k RPs are classified into different clusters through ANN based on SNR. Experimental results indicate that the proposed KNN/ANN hybrid algorithm generally outperforms KNN algorithm when the locations error is less than 2m.