In computing, a network generally denotes devices, often referred to as nodes, connected by links. Networks that are modeled with diagrams consist of hundreds of symbols, images, pictures, and icons, such as a computer, a server rack, or a cloud-based storage system. Network representations provide valuable insights into understanding the systems' underlying structures and mechanisms. Nevertheless, this unusually large number of superficial symbols and icons reflects a need for more systematic representations of the interiority of nodes. To give uniformity to this cascade of notions of basic units of nodes in network diagrams, the authors propose adoption of a new modeling methodology, called a thinging (abstract) machine (TM) (abstract machine of things) that represents all notions as a single diagrammatic machine. Because of the large number of network types, in this paper, they specifically and without loss of generality focus on IP telephone (internet protocol telephone) networks to exemplify communication networks. A real case study of IP telephone networks is modeled using TM.

Data <span>encryption process and key generation techniques protect sensitive data against any various attacks. This paper focuses on generating secured cipher keys to raise the level of security and the speed of the data integrity checking by using the MinHash function. The methodology is based on applying the cryptographic algorithms rivest-shamir-adleman (RSA) and advanced encryption standard (AES) to generate the cipher keys. These keys are used in the encryption/decryption process by utilizing the Pearson Hash and the MinHash techniques. The data is divided into shingles that are used in the Hash function to generate integers and in the MinHash function to generate the public and the private keys. MinHash technique is used to check the data integrity by comparing the sender’s and the receiver’s encrypted digest. The experimental results show that the RSA and AES algorithms based on the MinHash function have less encryption time compared to the normal hash functions by 17.35% and 43.93%, respectively. The data integrity between two large sets is improved by 100% against the original algorithm in terms of completion time, and 77% for small/medium data and 100% for large set data in terms of memory utilization.</span>