RC4 stream cipher is an encryption algorithm that is used in two domains of security realized for IEEE 802.11 LANs: one of them is wired equivalent confidentiality and the other is WI-FI Protected Access protocol. It is symmetric encryption, fast, and simple algorithm. It has different weaknesses such as the bias that occurs in the key stream bytes. Key scheduling phase is more intricate. It was prepared to be unnecessarily easy. The initial few bytes of keystream that are generated by pseudo random generation algorithm (PRGA) are biased to several bytes of the private key. Thus, the byte analysis makes it probable for attacking RC4 in some methods of operations. This paper presents a new stream cipher algorithm as a development for RC4, named double RC4 key generation, which is submitted to solve interconnection problems in the output of the inner state and solve the problem of weak keys. The new idea allows random access to the key stream. The keystream depends on all preceding state bytes. RC4 and the proposed algorithm are analyzed and proved that the new algorithm has no single or double byte bias in the key stream while RC4 has proved the same bias that is shown in the literature. The proposed algorithm introduces high resistance against different attacks that are applied to RC4 and the generated key stream has high randomness, large complexity, and good statistical properties.

Abstract-The problem of discovering and removing duplicated records is one of the main problems in the wide area of data cleaning and data quality in the data warehouse. In this paper, researchers try to find a similar data from a set of data records. A similarity grade is assigned to the data records in relation to other data records based on a similarity between tokens of the data records. Data records whose similarity score with respect to each other is greater than a threshold from one or more groups of data records. In this system, a key is created for each record in the database, as shown in suggested algorithms, where this key is input to Q-grams similarity algorithm that calculates the percentage of similarity between each key and another. We have identified the percentage threshold to be 0.68. If the similarity threshold between the key values is exceeded, it enters to the Neural Network algorithm that works with two-phases training data and testing. The suggested approach is tested through several different data warehouse for the evaluating the efficiency. The accuracy acquired from multi DW has been found to be 96.94%.