Sapiee Jamel

A cryptographic algorithm based on hybrid cubes

Cryptographic algorithms are important to ensure the security of data during transmission or storage. Many algorithms have been proposed based on various transformation and manipulation of data. One of which is using magic cube. However, the existing approaches are based on a transformation of magic cube’s face values. In this paper, we propose a new cryptographic algorithm based on combinations of hybrid magic cubes which are generated from a magic square and two orthogonal Latin squares. Using two random functions, i.e., random selection of thirteen magic cubes and random key selection from layers of hybrid, the generated chipertexts are free from any predicted pattern which might be used by cryptanalyst to decipher the original message.

Diffusive primitives in THE design of modern cryptographic algorithms

The importance of data confidentiality, integrity and availability in any data storage and transmission is undeniable especially with the development of highly integrated communication mediums such as the Internet. Disclosure of confidential or secret data to unauthorised users has become an important issue and indirectly give opportunity for ongoing research and development for reliable and strong cryptographic algorithms. Every modern cryptographic algorithm must have the elements of confusion and diffusion in it design. Research on finding effective and efficient diffusive element is still ongoing and highly discussed amongst researchers in the area of information security. In this paper, we investigate the diffusive property of three cryptographic algorithms: Rijndael, Twofish and Safer+ using a simple test vectors. Binary and decimal representation will be used to show the characteristic of each diffusive that play an important role as it will ensure any ciphertext generated from cryptographic algorithm are random and free from any predicted pattern which might be used by cryptanalyst to decipher the original message.

A Survey on the Cryptographic Encryption Algorithms

Security is the major concern when the sensitive information is stored and transferred across the internet where the information is no longer protected by physical boundaries. Cryptography is an essential, effective and efficient component to ensure the secure communication between the different entities by transferring unintelligible information and only the authorized recipient can be able to access the information. The right selection of cryptographic algorithm is important for secure communication that provides more security, accuracy and efficiency. In this paper, we examine the security aspects and processes involved in the design and implementation of most widely used symmetric encryption algorithms such as Data Encryption Standard (DES), Triple Data Encryption Standard (3DES), Blowfish, Advanced Encryption Standard (AES) and Hybrid Cubes Encryption Algorithm (HiSea). Furthermore, this paper evaluated and compared the performance of these encryption algorithms based on encryption and decryption time, throughput, key size, avalanche effect, memory, correlation assessment and entropy. Thus, amongst the existing cryptographic algorithm, we choose a suitable encryption algorithm based on different parameters that are best fit to the user requirements.

The Hybrid Cubes Encryption Algorithm (HiSea)

Hybrid cubes are generated from a combination and permutation of integers as shown in Latin squares and orthogonal Latin squares. In this paper we extend our earlier non binary block cipher using all possible combination of hybrid cubes layers as the source for the encryption and decryption keys. The overall security of the cipher is improved with the inclusion of substitution box (SBOX) and diffusion functions. The experimental results indicate that the proposed generator matrices from hybrid cubes layers are suitable as candidate for a key schedule algorithm. Large key space will make brute force attack on the key space difficult and time consuming.

Classification of JPEG Files by Using Extreme Learning Machine

Recovery of data files when their system information missing is a challenging research issue. The recovery process entails methods that analyze the structure and contents of each individual file clusters. A primary and important process of files’ recovery is determining the files’ types including JPEG, DOC or HTML. This paper proposes an Extreme Learning Machine (ELM) algorithm to assign a class label of JPEG or Non-JPEG image for files in a continuous series of data clusters. The algorithm automatically classifies the files based on evaluation measures of three methods Entropy, Byte Frequency Distribution and Rate of Change. The ELM algorithm is applied to RABEI-2017 and DFRWS-2006 datasets. The experimental results show that the ELM algorithm is able to identify JPEG files of fragmented clusters with high accuracy rate. The classification accuracy of the RABEI-2017 dataset is 90.15% and the DFRWS-2006 is 93.46%. The DFRWS-2006 has more classes than the RABEI-2017 which improves the ELM classifier fitting.

An Improved Low Contrast Image in Normalization Process for Iris Recognition System

Iris recognition system is one of the most predominant methods used for personal identification in the modern days. Low quality iris image such as low contrast and poor illumination presents a setback for iris recognition as the acceptance or rejection rates of verified user depend solely on the image quality. This paper presents a new method for improving histogram equalization technique to obtained high contrast in normalization process thereby reducing False Rejection Rate (FRR) and False Acceptance Rate (FAR). The proposed technique is developed using C++ and tested using four datasets CASIA, UBIRIS, MMU and ICE 2005. The experimental results show that the proposed technique has an accuracy of 95%, as compared to the existing techniques: CLAHE, AHE, MAHE and HE which have an accuracy of a 93.0, 85.7, 92.8 and 90.71% respectively. Hence it can be concluded that the proposed technique is a better enhancement technique compared to the existing techniques for image enhancement.

All-or-Nothing Key Derivation Function Based on Quasigroup String Transformation

Traditionally, Cryptographic ciphers (Block and Stream) uses Key Derivation Function (KDF) to generate cryptographic keys for encryption purpose. These KDFs are usually designed based on existing Hash functions and ciphers as primitives, to achieve better security. However, this method of construction can be costly to resources- constrain environments. The main function of KDFs is to generate random and unpredictable secret keys. Therefore, the use of predefined public string increases the predictability level and provides some partial knowledge of the key to cryptanalyst, thus jeopardies the security. This paper proposed a new algorithm to minimize the use of cryptographic Hash function and ciphers as a key derivation function and to optimally mitigate the use of predefined public string in KDF. The proposed KDF is entirely key-dependent and cryptanalyst has to correctly predict all the elements in the key string otherwise he got nothing. To achieve that, a new definition of Quasigroup string transformation, a Quasigroup- based expansion function, and key-metadata expansion function as well as reduction function are integrated together in the design of the proposed KDF. The proposed algorithm will be evaluated using statistical test for Randomness developed and recommended by the National Institute of Standard and Technology (NIST), Avalanche, Brute Force and Correlation Assessment test. The proposed algorithm will ensure not only confidentiality of information but integrity as well.