Biblio

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.

Random numbers are important tools for generating secret keys, encrypting messages, or masking the content of certain protocols with a random sequence that can be deterministically generated. The lack of assurance about the random numbers generated can cause serious damage to cryptographic protocols, prompting vulnerabilities to be exploited by the attackers. In this paper, a new pseudo - random number generator algorithm that uses dynamic system clock converted to Epoch Timestamp as PRNG seed was developed. The algorithm uses a Linear Congruential Generator (LCG) algorithm that produces a sequence of pseudo - randomized numbers that performs mathematical operations to transform numbers that appears to be unrelated to the Seed. Simulation result shows that the new PRNG algorithm does not generate repeated random numbers based on the frequency of iteration, a good indicator that the key for random numbers is secured. Numerical analysis using NIST Test Suite results concerning to random sequences generated random numbers has a total average of 0.342 P-value. For a p-value ≥ 0.001, a sequence would be considered to be random with a confidence of 99.9%. This shows that robustness and unpredictability were achieved. Hence, It is highly deterministic in nature and has a good quality of Pseudo-Random Numbers. It is therefore a good source of a session key generation for encryption, reciprocal in the authentication schemes and other cryptographic algorithm parameters that improve and secure data from any type of security attack.

Cryptography is one of the important aspect of data and information security. The security strength of cryptographic algorithms rely on the secrecy and randomness of keys. In this study, bitwise operations, Fisher-Yates shuffling algorithm, and cipher text mapping are integrated in the proposed TRNG key generator for One-Time Pad cryptography. Frequency monobit, frequency within a block, and runs tests are performed to evaluate the key randomness. The proposed method is also evaluated in terms of avalanche effect and brute force attack. Tests results indicate that the proposed method generates more random keys and has a higher level of security compared with the usual OTP using PRNG and TRNGs that do not undergo a refining phase.

Network steganography is a branch of steganography that hides information through packet header manipulation and uses protocols as carriers to hide secret information. Many techniques were already developed using the Transmission Control Protocol (TCP) headers. Among the schemes in hiding information in the TCP header, the Initial Sequence Number (ISN) field is the most difficult to be detected since this field can have arbitrary values within the requirements of the standard. In this paper, a more undetectable scheme is proposed by increasing the complexity of hiding data in the TCP ISN using dynamic identifiers. The experimental results have shown that using Bayes Net, the proposed scheme outperforms the existing scheme with a low detection accuracy of 0.52%.

In cryptography, one-time pad (OTP) is claimed to be the perfect secrecy algorithm in several works if all of its features are applied correctly. Its secrecy depends mostly on random keys, which must be truly random and unpredictable. Random number generators are used in key generation. In Psuedo Random Number Generator (PRNG), the possibility of producing numbers that are predictable and repeated exists. In this study, a proposed method using True Random Number Generator (TRNG) and Fisher-Yates shuffling algorithm are implemented to generate random keys for OTP. Frequency (monobit) test, frequency test within a block, and runs tests are performed and showed that the proposed method produces more random keys. Sufficient confusion and diffusion properties are obtained using Pearson correlation analysis.

Text Mining is widely used in many areas transforming unstructured text data from all sources such as patients' record, social media network, insurance data, and news, among others into an invaluable source of information. The Bag Of Words (BoW) representation is a means of extracting features from text data for use in modeling. In text classification, a word in a document is assigned a weight according to its frequency and frequency between different documents; therefore, words together with their weights form the BoW. One way to solve the issue of voluminous data is to use the feature hashing method or hashing trick. However, collision is inevitable and might change the result of the whole process of feature generation and selection. Using the vector data structure, the lookup performance is improved while resolving collision and the memory usage is also efficient.

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.

The integration of subset sum in the verifiable secret sharing scheme provides added security measure for a multiparty computation such as immediate identification of and removal of an imposter, avoidance or discourages man-in-the-middle attack and lattice-based attack, and lessens dealer's burden on processing monitoring the integrity of shareholders. This study focuses on the security assessment of a brute-force attack on the subset sum-based verifiable secret sharing scheme. With the simulation done using a generator of all possible fixed-length partition (which is k=3 as the least possible) summing up to the sum of the original subset generated by the dealer, it shows that it will already took 11,408 years to brute-force all possible values even on a small 32-bit-length value and 3.8455 years for a 128-bit length value thus proving that the resiliency on brute attack on the subset sum based VSSS can be discounted despite simplicity of the implementation. Zero knowledge on the number of threshold will also multiply to the impossibility of the brute force attack.

The power of artificial neural networks to form predictive models for phenomenon that exhibit non-linear relationships is a given fact. Despite this advantage, artificial neural networks are known to suffer drawbacks such as long training times and computational intensity. The researchers propose a two-tiered approach to enhance the learning performance of artificial neural networks for phenomenon with time series where data exhibits predictable changes that occur every calendar year. This paper focuses on the initial results of the first phase of the proposed algorithm which incorporates clustering and classification prior to application of the backpropagation algorithm. The 2016–2017 zonal load data of France is used as the data set. K-means is chosen as the clustering algorithm and a comparison is made between Naïve Bayes and k-Nearest Neighbors to determine the better classifier for this data set. The initial results show that electrical load behavior is not necessarily reflective of calendar clustering even without using the min-max temperature recorded during the inclusive months. Simulating the day-type classification process using one cluster, initial results show that the k-nearest neighbors outperforms the Naïve Bayes classifier for this data set and that the best feature to be used for classification into day type is the daily min-max load. These classified load data is expected to reduce training time and improve the overall performance of short-term load demand predictive models in a future paper.