No abstract

The internet of things (IoT) revolution has been sparked by the exponential increase in connected devices caused by recent advances in wireless technology. These embedded devices gather, analyze, and send vast data via the network. Data transmission security is a primary problem in IoT networks. Several lightweight encryption/decryption algorithms have been developed for such resource-constraint devices. One of most effective and fast lightweight encryption algorithms for IoT applications is the Tiny Encryption Algorithm (TEA). TEA has few lines source of codes to implement and based on Feistel structure to provide cryptographic primitive confusion and diffusion features in order to hide statistical aspects of plaintext. However, it is vulnerable to assaults using equivalent and related key attacks. This study suggested modifying TEA by employing a new proposed generating keys function using two Linear Feedback Shift Registers (LFSRs) as a combination to address the security flaw caused by utilizing different keys for each round function. The key sensitivity, Avalanche effect, and a completeness test were used to evaluate its security performance. The key sensitivity of the proposed modified TEA outperforms original TEA by 50.18 % to 44.88 %. The modified TEA avalanche effect outperforms TEA by 52.57 % to 47.69 %, and its completeness test outperforms TEA by 51.75 % to 48.36 %. Experimental results indicates that, the encryption performance of proposed modified TEA is better than original TEA.

Wireless Sensor Network (WSN) is emerging as a dominant technology with its applications in areas like agriculture, communication, environment monitoring, and surveillance. The inherited vulnerability and resource-constrained nature of sensor nodes led researchers to propose many lightweight cryptographic protocols for WSN. These sensors are low-cost, low energy, have low processing capability and have low storage restrictions. WSN suffers from many risks because of these unique constraints. This paper proposes a new lightweight security framework for WSNs and covers different lightweight cryptographic schemes for WSN applications. The aim is to provide cryptographic primitives for integrity, confidentiality, and protection from the man-in-the-middle and reply attacks. The work is based solely on symmetric cryptography and it has four phases; Network Initialization, Node Initialization, Nodes Communication, and Node Authentication. This work adopts the Low-Energy Adaptive Clustering Hierarchy (LEACH) framework, which deploys random rotation to distribute the energy among a group of nodes. The probability of attacking in LEACH is higher at cluster head and member nodes. Therefore, data transmission among communicated nodes is encrypted over multiple levels of protection by dynamic session keys to provide a high level of security. In addition, an authentication ticket is provided by a cluster head for each authenticated node to identify another node. The session keys are dynamically generated and updated during the communication to prevent compromising or capturing the keys. Through simulation and evaluation of the system, the results showed less energy consumption and efficient cryptographic primitive were compared with existing schemes

Text mining aims to understand texts correctly by utilising several phases to collect those features of Arabic words which are valuable and important to the applications mentioned above in making a correct decision. The technology then builds a strong system that relies on AI techniques, such as neural networks, to collect words in accordance with those features. An ANN is a collection of connected nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron is one that receives a signal then processes it and can signal to neurons connected to it. The current study is concerned with building a system for analysing words in the Arabic language. This system can be included in any application to address the Arabic language, becoming part of it. The system generates strings for all names and pronouns appearing in the entered text and depends mainly on the automatic assembly of a set features by using neural networks. We implemented the system, with its two phases, on the documents in succession. The results were encouraging, ranging between 83% and 96%.

With the advancement of digital storing and capturing technologies in recent years, an image retrieval system has been widely known for Internet usage. Several image retrieval methods have been proposed to find similar images from a collection of digital images to a specified query image. Content-based image retrieval (CBIR) is a subfield of image retrieval techniques that extracts features and descriptions content such as color, texture, and shapes from a huge database of images. This paper proposes a two-tier image retrieval approach, a coarse matching phase, and a fine-matching phase. The first phase is used to extract spatial features, and the second phase extracts texture features based on the Slantlet transform. The findings of this study revealed that texture features are reliable and capable of producing excellent results and unsusceptible to low resolution and proved that the SLT-based texture feature is the perfect mate. The proposed method's experimental results have outperformed the benchmark results with precision gaps of 28.0 % for the Caltech 101 dataset. The results demonstrate that the two-tier strategy performed well with the successive phase (fine-matching) and the preceding phase (coarse matching) working hand in hand harmoniously.