A Chaotic System and Count Tracking Mechanism-based Dynamic S-Box and Secret Key Generation

In cryptography, Block ciphers use S-Boxes to perform substitution and permutation operations on a data block. S-Boxes provide non-linearity and confusion of bits to the cryptographic algorithms. In addition, secret keys are critical security aspects for encrypting and decrypting messages. The uncertainty and randomness of the secret key and S-boxes used in the algorithm determine the extent of security against any cryptanalysis attack. This paper proposes a new mechanism to dynamically generate a secret key and S-Box each time while sending and receiving the message. These dynamically generated S-Boxes and keys depend on mutually decided security parameters and message transfer history. Furthermore, a new counter-based mechanism is introduced in this paper. These enhancement techniques are applied to the serpent cipher algorithm, and a data transfer simulation is performed to validate the efficacy of the proposed method. We observe that the dynamically generated S-box follows the strict avalanche criteria. We further validate that the encrypted message shows higher sensitivity to the S-box and the secret key in enhanced serpent cipher than the original version. However, to validate our proposed method, we test and analyze the improvements in the Serpent Cipher Algorithm

Tunicate Swarm Algorithm with Deep Learning Based Land Use and Cover Change Detection in Nallamalla Forest India

Every biological system on the planet is severely impacted by environmental change, and its primary driver is deforestation. Meanwhile, quantitative analysis of changes in Land Use and Land Cover (LULC) is one of the prominent ways to manage and understand land transformation; thus, it is essential to inspect the performance of various techniques for LULC mapping to recognize the better classifier to more applications of earth observation. This article develops a Tunicate Swarm Algorithm with Deep Learning Enabled Land Use and Land Cover Change Detection (TSADL-LULCCD) technique in Nallamalla Forest, India. The presented TSADL-LULCCD technique mainly focuses on the identification and classification of land use in the Nallamalla forest using LANDSAT images. To accomplish this, the presented TSADL-LULCCD technique employs a dense EfficientNet model for feature extraction. In addition, the Adam optimizer is applied for the optimal hyper parameter tuning of the dense EfficientNet approach. For land cover classification, the TSADL-LULCCD technique exploits the Deep Belief Network (DBN) approach. To tune the hyper parameters related to the DBN system, the TSA is used. The experimental validation of the TSADL-LULCCD algorithm is tested on LANDSAT-7-based Nallamalla region images. The experimental results stated that the TSADL-LULCCD technique exhibits better performance over other existing models in terms of different evaluation measures