Earticle

Interactive hashing은 Naor, Ostrovsky, Venkatesan, Yung〔1〕에 의해 소개된 프로토콜로 주어진 스트링 크기 t비트에 대해 t-1번의 라운드 복잡도(round complexity)와 $t^2$-1 비트의 전송 복잡도(communication complexity)를 가진다. 본 논문은 t를 나누는 m에 대해서 t/m-1번의 라운드 복잡도와 $t^2$/m - m 비트의 전송 복잡도를 갖는 NOVY 프로토콜보다 효율적으로 확장된 Interactive hashing 프로토콜을 제안하고 그 안전성을 증명한다.

Interactive hashing is a protocol introduced by Naor, Ostrovsk Venkatesan, $Yung^{[1]}$ with t-1 round complexity and $t^2$ - 1 bits communication complexity for given t bits string. In this paper, we propose more efficiently extended interactive hashing protocol with t/m- 1 round complexity and $t^2$/m - m bits communication complexity than NOVY protocol when m is a divisor of t, and prove the security of this.

Machine learning can detect variant malware files that can evade signature-based detection. Feature hashing is used to convert features into a fixed-length vector. In this paper, we study the appropriate vector size for feature hashing for a large dataset of malware files. Through exhaustive experiments on more than 280,000 real malware and benign files, we find for the first time that the default vector size of current feature hashing practices is unnecessarily large. We experimentally explore the appropriate vector size, which not only reduces memory space by 70% but also increases the detection accuracy, compared with the state-of-the-art scheme.

This study proposes a robust hashing method using scale-invariant feature transform (SIFT) features points and discrete wavelet transform (DWT) approximation coefficients for image authentication. Initially, the invariant feature points are computed using SIFT from the component of color image. Next, n distinct SIFT feature points are utilized to extract image content from the component. Then, DWT is applied to extracted content in order to compute approximation coefficients. Finally, the approximation coefficients are normalized to form a binary hash. Experimental results show that the proposed method is robust to various content-preserving operations such as compression, scaling, filtering, additive noise, brightness, and contrast adjustment. In addition, the performance of the proposed method is compared to existing methods using a receiver operating characteristics curve. The comparison results show that the proposed method performs better than the existing methods.

API(Application Program Interface) 콜 시퀀스 분석은 분석 대상 프로그램에서 API 콜 정보를 추출한 후 분석하는 기법으로 다른 기법들에 비해 대상의 행위를 특징할 수 있는 장점이 있다. 하지만 기존의 API 콜 시퀀스 분석기법은 동일한 기능을 수행하는 함수를 상이한 함수로 잘못 식별하여 분석을 수행하는 문제점이 존재한다. 본 연구에서는 API 각각을 추상화시키는 방식을 추가하여 기존의 식별 문제를 해결하고 분석 성능을 향상시키고자 한다. 그 후 분석 대상들에서 획득한 추상화된 API 콜 시퀀스에 LSH(Locality Sensitive Hashing) 기법을 적용하여 각 분석 대상들 간의 유사도를 계산하고 유사한 유형끼리 클러스터를 형성하는 과정을 수행하였다. 본 연구는 악성코드 분석 시 악성코드의 유형을 파악하는 데 요긴하게 사용할 수 있으며, 최종적으로는 해당 유형 정보를 기반으로 악성코드 분석의 정확도를 향상시키는 데 기여할 수 있다.

API call sequence analysis is a kind of analysis using API call information extracted in target program. Compared to other techniques, this is advantageous as it can characterize the behavior of the target. However, existing API call sequence analysis has an issue of identifying same characteristics to different function during the analysis. To resolve the identification issue and improve performance of analysis, this study includes the method of API abstraction technique in addition to existing analysis. From there on, similarity between target programs is computed and clustered into similar types by applying LSH to abstracted API call sequence from analyzed target. Thus, this study can attribute in improving the accuracy of the malware analysis based on discovered information on the types of malware identified.

To effectively track the illegal use of digital images and maintain the security of digital image communication on the Internet, this paper proposes a reversible multipurpose image watermarking algorithm based on a deep residual network (ResNet) and perceptual hashing (also called MWR). The algorithm first combines perceptual image hashing to generate a digital fingerprint that depends on the user's identity information and image characteristics. Then it embeds the removable visible watermark and digital fingerprint in two different regions of the orthogonal separation of the image. The embedding strength of the digital fingerprint is computed using ResNet. Because of the embedding of the removable visible watermark, the conflict between the copyright notice and the user's browsing is balanced. Moreover, image authentication and traitor tracking are realized through digital fingerprint insertion. The experiments show that the scheme has good visual transparency and watermark visibility. The use of chaotic mapping in the visible watermark insertion process enhances the security of the multipurpose watermark scheme, and unauthorized users without correct keys cannot effectively remove the visible watermark.

리눅스는 초창기부터 사용자 패스워드의 암호화를 위해 해시 알고리즘인 MD-5를 사용해 왔다. 최근 보안성이 강화된 패스워드 관리가 요구되면서 엔터프라이즈 리눅스 시스템에서는 MD-5보다 더욱 높은 신뢰성을 보이는 SHA-512 알고리즘을 사용하고 있다. 본 논문에서는 해시 및 암호화 알고리즘의 특징에 대해 비교 분석하고, 리눅스 사용자 정보의 관리 체제에 대해 알아본다. 이러한 분석을 기반으로 사용자 패스워드에 적용된 해시 알고리즘의 보안성에 대해 분석하고, 추가적으로 Apache, PHP, MySQL과 같은 공개 소프트웨어 파일 검증에 사용되는 해시 알고리즘 적용 사례를 분석한다. 마지막으로 관련 보안 도구인 John The Ripper를 분석하여 사용자 패스워드 관리를 통한 시스템 보안 강화 방법을 제시한다.

MD-5 has been the hash algorithm to encrypt the user's password on Linux from the beginning. Recently the more reliable password management was demanded and SHA-512 algorithm became the hash algorithm on the recent Enterprise Linux which is more reliable than MD-5. This paper researching the characteristics of the hashing and encryption algorithms and find out about Linux User information management. Based on this analysis, and analysis of the security of the hashing algorithm is applied to the user password. In addition, analyzes the cases used hash algorithm applied to the validation of Open Source Software file, such as Apache, PHP, MySQL. Finally, by analyzing the security tool John The Ripper this paper suggests the enhanced security with the administrative management of passwords.

Vehicular ad-hoc networks (VANETs) technology has come out as an important research field over the last few years. VANETs are the likely an influencing approach to provide safety of driver and other applications for the traffic conditions as well as passengers. Being dynamic in nature, it establishes the network, according to the situation and need of the users and provides reliable communication among the vehicles. Due to its great benefits, it is highly vulnerable to various attacks and security in VANET should be taken into consideration. This paper presented the security attacks between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Many research works have been done to improve the performance and security of this network. The main aim of this paper is the security using hashing and techniques to calculate the trust in VANETs.

The main idea behind any hash function is to find a one to one correspondence between a key value and an index in the hash table where the key value can be placed. In closed hashing, it is very difficult to handle the situation of table overflow in a satisfactory manner. Key values are haphazardly placed and generally majority of the keys are placed far away from their hash location. Thus, the number of probes is greatly increased which degrades the overall performance. To resolve this problem another hashing method known as open hashing (separate chaining) is employed. But this type of hashing is still not efficient in case of searching because here all the elements that have the same hash function are inserted in a sequential order. Due to this, traversal of all the previously inserted elements is required when we are searching for the last element. So the overall complexity will be O (n). In this paper, we propose a hybrid chaining model which is a combination of Binary Search Tree and AVL Tree to achieve a complexity of O (log n).

본 논문에서는 한글 단어 단위의 음성 인식 처리 기술을 제안한다. 음성 인식은 마이크와 같은 센서를 사용하 여 얻은 음향학적 신호를 단어나 문장으로 변환시키는 기술이다. 대부분의 외국어들은 음성 인식에 있어서 어려움이 적은 편이다. 그에 반면, 한글의 모음과 받침 자음 구성이어서 음성 합성 시스템으로부터 얻은 문자를 그대로 사용하 기에는 부적절하다. 기존 구조의 음성 인식 기술을 개선해야만 보다 정확하게 단어를 인식할 수 있다. 이러한 문제를 해결하기 위해 기존 방식의 음성 인식구조에 새로운 알고리즘을 추가하여 음성 인식률을 높이게 하였다. 먼저 입력된 단어를 전처리 과정을 수행한 후 결과를 토큰 처리한다. 레벤스테인 거리 알고리즘과 해싱 알고리즘에서 처리된 결과 값을 조합한 후 자음 비교 알고리즘을 거쳐 표준 단어를 출력한다. 최종 결과 단어를 표준화 테이블과 비교하여 존재 하면 출력하고 존재하지 않으면 테이블에 등록하도록 하였다. 실험 환경은 스마트폰 응용 프로그램을 개발하여 사용 하였다. 본 논문에서 제안된 구조는 기존 방식에 비해 인식률의 성능이 표준어는 2%, 방언은 7% 정도 향상되었음을 보였다.

In this paper, we propose a technique for processing of speech recognition in korean words. Speech recognition is a technology that converts acoustic signals from sensors such as microphones into words or sentences. Most foreign languages have less difficulty in speech recognition. On the other hand, korean consists of vowels and bottom consonants, so it is inappropriate to use the letters obtained from the voice synthesis system. That improving the conventional structure speech recognition can the correct words recognition. In order to solve this problem, a new algorithm was added to the existing speech recognition structure to increase the speech recognition rate. Perform the preprocessing process of the word and then token the results. After combining the result processed in the Levenshtein distance algorithm and the hashing algorithm, the normalized words is output through the consonant comparison algorithm. The final result word is compared with the standardized table and output if it exists, registered in the table dose not exists. The experimental environment was developed by using a smartphone application. The proposed structure shows that the recognition rate is improved by 2% in standard language and 7% in dialect.

Wireless communication is known as key to global development and has become main stream of our daily business. Protecting information and communication speed is an issue which we need to focus on the wireless networks. Universal mobile telecommunication system (UMTS) is evolution of third generation mobile communication system. It was built on the success of global system for mobile communication (GSM), UMTS security is also built upon GSM, so there is lot of security challenges such as the user identity, confidentiality. The international mobile subscriber identity travels through radio interface in free text format without any encryption. Due to this, the user identity and location can be theft which is serious problem. In this paper, we have proposed and emulated a model which protects user identity through IMSI hashing, which will provide anonymity to user. We have compared different hashing techniques and the results are given at the end.

In this paper, fast image search with efficient additive kernels and kernel locality-sensitive hashing has been proposed. As to hold the kernel functions, recent work has probed methods to create locality-sensitive hashing , which guarantee our approach’s linear time, however existing methods still do not solve the problem of locality-sensitive hashing (LSH) and indirectly sacrifice the loss in accuracy of search results in order to allow fast queries. To improve the search accuracy, we show how to apply explicit feature maps into the homogeneous kernels, which help in feature transformation and combine it with kernel locality-sensitive hashing. We prove our method on several large datasets, and illustrate that it improve the accuracy relative to commonly used methods and make the task of object classification, content-based retrieval more fast and accurate.

Speech perceptual hashing authentication algorithm is an efficient method for content integrity authentication and identity authentication. But the algorithm becomes transparent under the principle of Kerckhoffs which makes the algorithm unsafe. In this paper, the algorithm is encrypted under the principle of Kerckhoffs to protect the security of the algorithm. Then the specific link that won’t affect the performance of the algorithm is identified through the experiments. Next, this paper analyzes the security of the encrypted algorithm based on the concept of Shannon unicity distance. The unicity distance is figured out finally based on the experiments. That is to say, the algorithm loses its security even though the algorithm has been encrypted. Under this circumstances, the most important thing is to continue keeping the safety of the algorithm. Therefore, this paper proposed an efficient random secret key method to guarantee the safety of the algorithm after the unicity distance is figured out.

Sensor networks have a significant potential in diverse applications, and some have already been deployed in monitoring system of living environment. With the increasing complexity of application logic, difficulties in monitoring sensor networks have become a barrier to the adoption of these networks. The difficulties are due not only to their inherently distributed nature but also to the need for mechanisms to address their harsh operating conditions such as unreliable communications, faulty nodes, and extremely constrained resources. Living Environment monitoring is composed mainly of sensor data on air, water, and ecotourism quality. Wireless sensor networks(WSNs) entail a substantial loss of energy because there is a need for some mechanisms that can select multiple communications in single communications. This kind of merging is called data aggregation. This paper presents a secure and authentication-based approach to the data aggregation of living environment. User authentication is performed using a secure hash algorithm. This paper also introduces a neural network to check bad packet communications over the network. The results indicate that the proposed approach is more reliable and efficient than existing ones.

A hash table is a fundamental data structure implementing an associative memory that maps a key to its associative value. Due to its very fast mapping operation of O(1), it has been widely used in various areas such as databases, bioinformatics, and distributed computing. Besides, the paradigm of micro-architecture design of CPUs is shifting away from faster uniprocessors toward slower chip multiprocessors. In order to fully exploit the performance of such modern computer architectures, the data structures and algorithms considering parallelism become more important than ever. This paper implements three cache-conscious hashing methods, linear hashing and chained hashing, and also, a modern hashing method, hopscotch hashing, and analyzes their performance under Intel 32-core CPU of Nehalem microarchitecture. We implement each hashing method using state-of-the-art techniques such as lock-free data structures, especially based on compare-and-swap (CAS) operations, and refinable data structures. To the best of our knowledge, the work done by this paper is the first work analyzing the performance of three all hashing methods under the same implementation framework. Experimental results using data of 223 (i.e., about eight millions) key-value pairs shows that lock-free linear hashing is the best for insert operation among three hashing methods, and lock-free chained hashing is the best for lookup operation. Hopscotch hashing shows the second best performance of lookup operation. However, hopscotch hash table size is much bigger than other hash table size. Through experiments, we have found that the hopscotch hashing is relatively not efficient than other hash methods.

Existing speech perceptual hashing authentication algorithms generally encrypt the perceptual hash value to protect the security of the algorithms. But under the principle of Kerckhoffs, the algorithm becomes transparent and fragile. And it can be seen that the secret key may be estimated when the number of times that reuses the secret key reache to a limit through the safety analysis of Shannon unicity distance. To solve these problems, we present a novel security authentication scheme of speech perceptual hashing based on the fuzzy commitment scheme (FCS). Firstly, in sender, the randomly selected BCH code word and the extracted perceptual hash value are sent to the Cmt() function to calculate and the final secure perceptual hash value (h(c),) can be obtained. Secondly, in receiver, the perceptual hash value is calculated again from the received speech information and then the calculated perceptual hash value with the received information  (commitment) are sent to the Decmt() function to calculate to get the h(c′). Finally, the h(c) and h(c′) are matched. The experimental results show that the proposed scheme effectively avoids the probability that an attacker obtains the plaintext/ciphertext pairs without affecting the performance of original algorithm, and at the same time it ensures the security of perceptual hashing algorithm.

An image hash, a discriminative and robust summary of an image, should be robust against quality-preserving signal processing steps, while being pairwise independent for perceptually different inputs. In order to improve the hash matching performance, this paper proposes a probabilistic dissimilarity matching. Instead of extracting the binary hash from the query image, we compute the probability that the intermediate hash vector of the query image belongs to each quantization bin, which is referred to as soft quantization binning. The probability is used as a weight in comparing the binary hash of the query with that stored in a database. A performance evaluation over sets of image distortions shows that the proposed probabilistic matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.

Medical information digitization makes the medical information storage and extraction more convenient. Medical image information security and copyright protection is also gradually being taken seriously, and some medical image watermarking has been applied. According to the characteristics of three-dimensional medical images, this paper proposes a robust zero-watermarking algorithm for medical volume data based on legendre chaotic neural network and perceptual hashing. The algorithm is based on three-dimensional discrete wavelet transform frequency analysis features, which uses perceptual hashing technique to extract medical volume data itself feature vector in order to structure robust zero watermarking. And using legendre chaotic neural network to generate chaotic sequence to enhance the security of the watermarking. The algorithm achieves a combination of legendre chaotic neural network encryption and zero-watermarking technology, which can improve the medical volume data watermarking algorithm security and robustness. The simulation results show the effectiveness of the algorithm.

As the usage of the Internet grows and digital media become more diversified, it has become much easier for digital contents to be distributed and shared. This makes easier to access the desired digital contents. On the other hand, there is an increasing need to protect the copyright of digital works. There are some prevalent ways to protect ownership, but they accompany several disadvantages. Among those ways, watermarking methods have the advantage of ensuring invisibility, but they also have a disadvantage that they are vulnerable to external attacks such as a noise and signal processing. In this paper, we propose the detecting method of illegal contents that is robust against external attacks to protect digital works. We extract HSV and LBP features from images and use Euclidian-based hashing techniques to shorten the searching time on high-dimensional and near-duplicate videos. According to the results, the proposed method showed higher detection rates than that of the Watermarking techniques in terms of the images with fabrications or deformations.