Classical cryptography is the process of hiding information and it manages the secret knowledge by encrypting the plain text message through the translation of it to an unintelligible message. Quantum cryptography is also hiding the plain text through encryption and it works based on the law of quantum physics for providing absolute security of data communication. It uses the idea of quantum mechanics to gadget a cryptographic system and the key problems are solved by intrusion detection including the Eavesdropping detection using quantum expertise. On the contrary, principles of secured communication protocol systems are demonstrated with classical quantum cryptography where the keys are distributed securely and are applied in quantum key distribution as well. In this paper, we provide a survey of works on classical cryptography and quantum cryptography and compare them with respect to time, security level and the classification of the data. Moreover, we perform a concise analysis of perspective classical cryptography and the conception of Quantum cryptography with various protocols and highlight the benefits of classical cryptography and Quantum cryptography in different applications. Finally, we provide a set of recommendations for selecting the suitable encryption model for securing the communication.

Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recognise the new types of attacks, which are introduced by malicious users leading to vulnerability and information loss in the network. In order to address this challenge, a new intrusion detection system, which detects the known and unknown types of attacks using an intelligent decision tree classification algorithm, has been proposed. For this purpose, a novel feature selection algorithm named dynamic recursive feature selection algorithm, which selects an optimal number of features from the data set is proposed. In addition, an intelligent fuzzy temporal decision tree algorithm is also proposed by extending the decision tree algorithm and integrated with convolution neural networks to detect the intruders effectively. The experimental analysis carried out using KDD cup data set and network trace data set demonstrates the effectiveness of this proposed approach. It proved that the false positive rate, energy consumption, and delay are reduced in the proposed work. In addition, the proposed system increases the network performance through increased packet delivery ratio.