Biblio

Fast and high-quality network flow hashing is an essential operation in many high-speed network systems such as network monitoring probes. We propose a multi-objective evolutionary design method capable of evolving hash functions for IPv4 and IPv6 flow hashing. Our approach combines Cartesian genetic programming (CGP) with Non-dominated sorting genetic algorithm II (NSGA-II) and aims to optimize not only the quality of hashing, but also the execution time of the hash function. The evolved hash functions are evaluated on real data sets collected in computer network and compared against other evolved and conventionally created hash functions.

Lin et al.'s scheme is a hash function Message Authentication Codes (MAC) block cipher based scheme that's composed of the compression function. Fixed point messages have been found on SMALLPRESENT-[s] algorithm. The vulnerability of block cipher algorithm against fixed point attacks can affect the vulnerability of block cipher based hash function schemes. This paper applies fixed point attack against Lin et al.'s modified scheme based on SMALLPRESENT-[8] algorithm. Fixed point attack was done using fixed point message from SMALLPRESENT-[8] algorithm which used as Initial Value (IV) on the scheme branch. The attack result shows that eight fixed point messages are successfully discovered on the B1 branch. The fixed point messages discovery on B1 and B2 branches form 18 fixed point messages on Lin et al.'s modified scheme with different IVs and keys. The discovery of fixed point messages shows that Lin et al.'s modified scheme is vulnerable to fixed point attack.

Apriori Algorithm is the most Classic Association Rule Mining Algorithm, which has unique advantages, but it also has some disadvantages such as high overhead. This paper first describes Apriori Algorithm, points out its shortcomings, introduces related concepts, and then proposes a method based on Hash technology and compressed combination item set technology to improve APRIORI algorithm. This paper introduces the basic idea and the concrete process of the improvement in detail, analyzes the efficiency of the improved algorithm by the experiment, and advances the application of the improved algorithm in the library personalized service.

Today, the huge amount of data was using in organizations around the world. This huge amount of data needs to process so that we can acquire useful information. Consequently, a number of industry enterprises discovered great information from shopper purchases found in any respect times. In data mining, the most important algorithms for find frequent item sets from large database is Apriori algorithm and discover the knowledge using the association rule. Apriori algorithm was wasted times for scanning the whole database and searching the frequent item sets and inefficient of memory requirement when large numbers of transactions are in consideration. The improved Apriori algorithm is adding and calculating third threshold may increase the overhead. So, in the aims of proposed research, Improved Apriori algorithm with LinkedList and hash tabled is used to mine frequent item sets from the transaction large amount of database. This method includes database is scanning with Improved Apriori algorithm and frequent 1-item sets counts with using the hash table. Then, in the linked list saved the next frequent item sets and scanning the database. The hash table used to produce the frequent 2-item sets Therefore, the database scans the only two times and necessary less processing time and memory space.

This paper proposes a stream cipher algorithm. Its main principle is conducting the binary random dynamic hash with the help of key. At the same time of calculating the hash mapping address of plaintext, change the value of plaintext through bits scrambling, and then map it to the ciphertext space. This encryption method has strong randomness, and the design of hash functions and bits scrambling is flexible and diverse, which can constitute a set of encryption and decryption methods. After testing, the code evenness of the ciphertext obtained using this method is higher than that of the traditional method under some extreme conditions..

This article presents a hardware implementation review of a popular family of hash algorithms: Secure Hash Algorithm 2 (SHA2). It presents various schematic solutions and their assessments for 28 nm CMOS technology. Using this paper we can estimate the expected performance of the hardware hash accelerator based on the IC.

Hash message authentication is a fundamental building block of many networking security protocols such as SSL, TLS, FTP, and even HTTPS. The sponge-based SHA-3 hashing algorithm is the most recently developed hashing function as a result of a NIST competition to find a new hashing standard after SHA-1 and SHA-2 were found to have collisions, and thus were considered broken. We used Xilinx High-Level Synthesis to develop an optimized and pipelined version of the post-quantum-secure SHA-3 hash message authentication code (HMAC) which is capable of computing a HMAC every 280 clock-cycles with an overall throughput of 604 Mbps. We cover the general security of sponge functions in both a classical and quantum computing standpoint for hash functions, and offer a general architecture for HMAC computation when sponge functions are used.

The problem of fast items retrieval from a fixed collection is often encountered in most computer science areas, from operating system components to databases and user interfaces. We present an approach based on hash tables that focuses on both minimizing the number of comparisons performed during the search and minimizing the total collection size. The standard open-addressing double-hashing approach is improved with a non-linear transformation that can be parametrized in order to ensure a uniform distribution of the data in the hash table. The optimal parameter is determined using a genetic algorithm. The paper results show that near-perfect hashing is faster than binary search, yet uses less memory than perfect hashing, being a good choice for memory-constrained applications where search time is also critical.

Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an organization plays a vital part in attack mitigation, and it has become an integral part of a secure organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.

Multi-tag identification technique has been applied widely in the RFID system to increase flexibility of the system. However, it also brings serious tags collision issues, which demands the efficient anti-collision schemes. In this paper, we propose a Multi-target tags assignment slots algorithm based on Hash function (MTSH) for efficient multi-tag identification. The proposed algorithm can estimate the number of tags and dynamically adjust the frame length. Specifically, according to the number of tags, the proposed algorithm is composed of two cases. when the number of tags is small, a hash function is constructed to map the tags into corresponding slots. When the number of tags is large, the tags are grouped and randomly mapped into slots. During the tag identification, tags will be paired with a certain matching rate and then some tags will exit to improve the efficiency of the system. The simulation results indicate that the proposed algorithm outperforms the traditional anti-collision algorithms in terms of the system throughput, stability and identification efficiency.

Recently, hashing has attracted considerable attention for nearest neighbor search due to its fast query speed and low storage cost. However, existing unsupervised hashing algorithms have two problems in common. Firstly, the widely utilized anchor graph construction algorithm has inherent limitations in local weight estimation. Secondly, the locally linear structure in the original feature space is seldom taken into account for binary encoding. Therefore, in this paper, we propose a novel unsupervised hashing method, dubbed “discrete locally-linear preserving hashing”, which effectively calculates the adjacent matrix while preserving the locally linear structure in the obtained hash space. Specifically, a novel local anchor embedding algorithm is adopted to construct the approximate adjacent matrix. After that, we directly minimize the reconstruction error with the discrete constrain to learn the binary codes. Experimental results on two typical image datasets indicate that the proposed method significantly outperforms the state-of-the-art unsupervised methods.

Efficient monitoring of high speed computer networks operating with a 100 Gigabit per second (Gbps) data throughput requires a suitable hardware acceleration of its key components. We present a platform capable of automated designing of hash functions suitable for network flow hashing. The platform employs a multi-objective linear genetic programming developed for the hash function design. We evolved high-quality hash functions and implemented them in a field programmable gate array (FPGA). Several evolved hash functions were combined together in order to form a new reconfigurable hash function. The proposed reconfigurable design significantly reduces the area on a chip while the maximum operation frequency remains very close to the fastest hash functions. Properties of evolved hash functions were compared with the state-of-the-art hash functions in terms of the quality of hashing, area and operation frequency in the FPGA.

This paper attempts to introduce the enhanced SHA-1 algorithm which features a simple quadratic function that will control the selection of primitive function and constant used per round of SHA-1. The message digest for this enhancement is designed for 512 hashed value that will answer the possible occurrence of hash collisions. Moreover, this features the architecture of 8 registers of A, B, C, D, E, F, G, and H which consists of 64 bits out of the total 512 bits. The testing of frequency for Q15 and Q0 will prove that the selection of primitive function and the constant used are not equally distributed. Implementation of extended bits for hash message will provide additional resources for dictionary attacks and the extension of its hash outputs will provide an extended time for providing a permutation of 512 hash bits.

In order to study the application of improved image hashing algorithm in image tampering detection, based on compressed sensing and ring segmentation, a new image hashing technique is studied. The image hash algorithm based on compressed sensing and ring segmentation is proposed. First, the algorithm preprocesses the input image. Then, the ring segment is used to extract the set of pixels in each ring region. These aggregate data are separately performed compressed sensing measurements. Finally, the hash value is constructed by calculating the inner product of the measurement vector and the random vector. The results show that the algorithm has good perceived robustness, uniqueness and security. Finally, the ROC curve is used to analyze the classification performance. The comparison of ROC curves shows that the performance of the proposed algorithm is better than FM-CS, GF-LVQ and RT-DCT.

The storage efficiency of hash codes and their application in the fast approximate nearest neighbor search, along with the explosion in the size of available labeled image datasets caused an intensive interest in developing learning based hash algorithms recently. In this paper, we present a learning based hash algorithm that utilize ordinal information of feature vectors. We have proposed a novel mathematically differentiable approximation of argmax function for this hash algorithm. It has enabled seamless integration of hash function with deep neural network architecture which can exploit the rich feature vectors generated by convolutional neural networks. We have also proposed a loss function for the case that the hash code is not binary and its entries are digits of arbitrary k-ary base. The resultant model comprised of feature vector generation and hashing layer is amenable to end-to-end training using gradient descent methods. In contrast to the majority of current hashing algorithms that are either not learning based or use hand-crafted feature vectors as input, simultaneous training of the components of our system results in better optimization. Extensive evaluations on NUS-WIDE, CIFAR-10 and MIRFlickr benchmarks show that the proposed algorithm outperforms state-of-art and classical data agnostic, unsupervised and supervised hashing methods by 2.6% to 19.8% mean average precision under various settings.

An approach for effective regression test selection is proposed, which minimizes the resource usage and amount of time required for complete testing of new features. Provided are the details of the analysis of hashing algorithms used during implementation in-depth review of the software, together with the results achieved during the testing process.

Investigation of hash join algorithm on multi-core and many-core platforms showed that carefully tuned hash join implementations could outperform simple hash joins on most multi-core servers. However, hardware-oblivious hash join has shown competitive performance on many-core platforms. Knights Landing (KNL) has received attention in the field of parallel computing for its massively data-parallel nature and high memory bandwidth, but both hardware-oblivious and hardware-conscious hash join algorithms have not been systematically discussed and evaluated for KNL's characteristics (high bandwidth, cluster mode, etc.). In this paper, we present the design and implementation of the state-of-the-art hardware-oblivious and hardware-conscious hash joins that are tuned to exploit various KNL hardware characteristics. Using a thorough evaluation, we show that:1) Memory allocation strategies based on KNL's architecture are effective for both hardware-oblivious and hardware-conscious hash join algorithms; 2) In order to improve the efficiency of the hash join algorithms, hardware architecture features are still non-negligible factors.

The methods are based on injecting unpredictability into means and objects of protection are called stochastic methods of information security. The effective protection can be done only by using stochastic methods against an active opponent. The effectiveness of stochastic protection methods is defined by the quality of the used pseudo-random number generators and hash functions. The proposed hashing algorithm DOZENHASH is based on the using of 3D stochastic transformations of DOZEN family. The principal feature of the algorithm is that all input and output data blocks as well as intermediate results of calculations are represented as three-dimensional array of bytes with 4 bytes in each dimension. Thus, the resulting transformation has a high degree of parallelism at the level of elementary operations, in other words, it is focused on the implementation using heterogeneous supercomputer technologies.

Warnings happen a lot in real transmission networks. These warnings can affect people's lives. It is significant to analyze the alarm association rules in the network. Many algorithms can help solve this problem but not considering the actual physical significance. Therefore, in this study, we mine the association rules in warning weblogs based on a sequential mining algorithm (GSP) with topology structure. We define a topology constraint from network physical connection data. Under the topology constraint, network nodes have topology relation if they are directly connected or have a common adjacency node. In addition, due to the large amount of data, we implement the hash-tree search method to improve the mining efficiency. The theoretical solution is feasible and the simulation results verify our method. In simulation, the topology constraint improves the accuracy for 86%-96% and decreases the run time greatly at the same time. The hash-tree based mining results show that hash tree efficiency improvements are in 3-30% while the number of patterns remains unchanged. In conclusion, using our method can mine association rules efficiently and accurately in warning weblogs.

Nearest neighbor search (NNS) is one of the current popular research directions, which widely used in machine learning, pattern recognition, image detection and so on. In the low dimension data, based on tree search method can get good results. But when the data dimension goes up, that will produce a curse of dimensional. The proposed Locality-Sensitive Hashing algorithm (LSH) greatly improves the efficiency of nearest neighbor query for high dimensional data. But the algorithm relies on the building a large number of hash table, which makes the space complexity very high. C2LSH based on dynamic collision improves the disadvantage of LSH, but its disadvantage is that it needs to detect the collision times of a large number of data points which Increased query time. Therefore, Based on LSH algorithm, later researchers put forward many improved algorithms, but still not ideal.In this paper, we put forward Locality-Sensitive Hashing Scheme Based on Heap Sort of Hash Bucket (HSLSH) algorithm aiming at the shortcomings of LSH and C2LSH. Its main idea is to take advantage of the efficiency of heapsort in massive data sorting to improve the efficiency of nearest neighbor query. It only needs to rely on a small number of hash functions can not only overcome the shortcoming of LSH need to build a large number of hash table, and avoids defects of C2LSH. Experiments show that our algorithm is more than 20% better than C2LSH in query accuracy and 40% percent lower in query time.

The use of information systems is a solutions to support the operations of the institution. In order to access information systems in accordance with their access rights, usually the user will enter a username and password as the authentication process. However, this has a weakness if the other side is cheating by sniffing or tapping user passwords. This makes the password unsafe to use for access information systems. If the username and password if it is stolen, abuse will occur for the crime or theft of the owner's identity accounts like name, email, telephone number, biological mother's name, account number and others. One solution is to apply two factor authentication method which is Time-Based One Time Password (TOTP) and Secure Algorithm Hash Algorithm 1 (SHA1). With this method, the system Authentication of a website or site does not only depend on the username and password to enter the account user but the user will get a token or code which is used to log in to the user's account. After testing hundred times, the authentication process who use Two Factor Authentication can tackle possible attacks on abuse o user access rights. Time Based Application One Time Password and Secure Hash Algorithm 1 Generate code that can't be the same because of the code it can only be used once with a time limit certain so it is difficult to guess. SHA1 with long input different strings will produce output with a fixed length string of 160 bits. Test results are obtained the results that 30 seconds is enough to prevent hackers log in and take over the account without permission and also prove that two-factor authentication can increase the security of the authentication process well. The time above is the result of testing the process user authentication until the hacker sniffing against tokens to try to take over the account.

This paper introduces a security analysis scheme for the most famous secure hash algorithms SHA-1 and SHA-2. Both algorithms follow Merkle Damgård structure to compute the corresponding hash function. The randomness of the output hash reflects the strength and security of the generated hash. Therefore, the randomness of the internal rounds of the SHA-1 and SHA-2 hash functions is analyzed using Bayesian and odd ratio tests. Moreover, a proper replacement for both algorithms is proposed, which produces a hash output with more randomness level. The experiments were conducted using a high performance computing testbed and CUDA parallel computing platform.

The emergence of integrated space-ground network (ISGN), with more complex network conditions compared with tradition network, requires packet classification to achieve high performance. Packet classification plays an important role in the field of network security. Although several existing classification schemes have been proposed recently to improve classification performance, the performance of these schemes is unable to meet the high-speed packet classification requirement in ISGN. To tackle this problem, a hybrid packet classification algorithm based on hash table and geometric space partition (HGSP) is proposed in this paper. HGSP falls into two sections: geometric space partition and hash matching. To improve the classification speed under the same accuracy, a parallel structure of hash table is designed to match the huge packets for classifying. The experimental results demonstrate that the matching time of HGSP algorithm is reduced by 40%-70% compared with traditional Hicuts algorithm. Particularly, with the growth of ruleset, the advantage of HGSP algorithm will become more obvious.

Cryptographic hash functions play a crucial role in information security. Cryptographic hash functions are used in various cryptographic applications to verify the message authenticity and integrity. In this paper we propose a Cellular Automata Based Hashing Algorithm (CABHA) for generating strong cryptographic hash function. The proposed CABHA algorithm uses the cellular automata rules and a custom transformation function to create a strong hash from an input message and a key.

In this paper, the application of the enhanced secure hash algorithm-512 is implemented on web applications specifically in password hashing. In addition to the enhancement of hash function, hill cipher is included for the salt generation to increase the complexity of generating hash tables that may be used as an attack on the algorithm. The testing of same passwords saved on the database is used to create hash collisions that will result to salt generation to produce a new hash message. The matrix encryption key provides five matrices to be selected upon based on the length of concatenated username, password, and concatenated characters from the username. In this process, same password will result to a different hash message that will to make it more secured from future attacks.