D. Vetriselvi

Network security is necessary to shield computer systems and data from unauthorized access, breaches, and cyberattacks. Machine learning can help make network security with more effective threat detection and response. Because machine learning techniques are intelligent and flexible, they are powerful tools to improve network security. An outline of the main uses of machine learning in network security is given in this abstract, with special attention on how ML is used in threat detection, anomaly identification, and incident response. After being trained on enormous datasets, machine learning algorithms can recognize patterns in typical network behaviours and distinguish variations that could be signs of security risks. Machine learning is a proactive security against cyber threats that includes features like behavioral profiling, predictive analysis, and intrusion detection. This chapter highlights the value of machine learning in enhancing conventional security measures and provides insights into its uses in a range of network security contexts for a resilient and flexible network. Request access from your librarian to read this chapter's full text.

Neurocryptology objective is to create novel and creative approaches to improve the security of cryptographic systems by incorporating knowledge from neuroscience, particularly the study of the cognitive functions of the human brain. Instead of using mathematical methods and keys to secure communication and data, neurocryptology makes use of knowledge about how the brain processes information and spots patterns. The form and operation of neural networks and biological brains serve as inspiration for neural-inspired algorithms, which have the potential to improve cryptography in several ways. Encryption keys can be created using neural networks. In order to improve encryption techniques, neural networks can be incorporated. Behavioral biometric authentication techniques can be improved by neural network-inspired algorithms. This chapter is discussing about the neuro inspired algorithms to enhance cryptographic protocols. It reviewing the different algorithms that underlie well-known cryptographic cryptosystems Request access from your librarian to read this chapter's full text.

Cryptography is the process of encrypting data or transforming plain text to ciphertext so that it can be deciphered only by appropriate key. Quantum cryptography employs quantum physics principles to encrypt and transport data in an unpackable manner. Quantum key distribution (QKD) is a technique for creating and exchanging private keys over quantum channels between two parties. Then, using standard cryptography, the keys can be used to encrypt and decrypt messages. Unbreakable encryption is something we really must have. The integrity of encrypted data is now in danger due to the impending development of quantum computers. Fortunately, quantum cryptography, via QKD, provides the answer we require to protect our information for a very long time to come. This is all based on the intricate principles of quantum mechanics. This chapter is discussing the various algorithms used and the applications of quantum cryptography.

The onset of autism related neurodevelopmental disorders happening in the embryonic stage itself. The effectiveness of treatment is greatly dependent on the early detection. Deep learning has made significant strides in the field of artificial intelligence. To perform semantic segmentation on the ultrasound image of the developing embryo, this research attempts to design a framework based on 3D Convolutional Neural Network, a deep learning model. The segments of the fetal brain can be more effectively separated by combining many convolutional neural networks with different configurations. Transfer learning can be used to lessen the paucity of data by using pretrained models. Two CNNs with different configurations can process a single image, and fusion is used to integrate the two feature maps. The normalization of the image takes place as a pre-processing step. The performance is measured with the evaluation parameters loss, training accuracy and testing accuracy.