Sanjay Misra (editor) - Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities

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As COVID-19 started spreading out of China in early 2020, most countries began imposing partial or total lockdown to save their citizens from this deadly virus. At the same time, organizations started implementing work from home policies for many of their employees. Work from home was acceptable from software and information technology-related companies. Still, it was not practical for all sectors, and as a result, a considerable number of professionals lost their jobs. Studies have not been done yet, but the high rate of job losers also caused high cyber-crimes. Artificial intelligence plays a crucial role in assessing, predicting, and solving problems, and its role in cyber security cannot be ignored.

It is with great pleasure that we present our book: Artificial Intelligence for Cyber security: Methods, Issues, and Possible Horizons or Opportunities. The book provides useful in-depth information regarding current research in artificial intelligence on various cyber security challenges. It comprises 18 papers from authors around the world.

In the paper , Ganesh et al. presented a system for multiple parties to learn a neural network model without disclosing their local training data to a central cloud server. The system is achieved by using distributed stochastic gradient descent combined with additively homomorphic encryption (HE). This process thereby bridges both deep learning and cryptography. A privacy-preserving deep learning framework is discussed in which all clients encrypt using a different public key in contrast to where each client encrypts using the same key.

Bindu and Thomas have presented the . In this paper, the authors have illustrated the necessity of a suitable preprocessing stage to eliminate the irrelevant and redundant features before the classifier for enhancing its performance, facilitating data visualization, and reducing storage requirements and modeling time.

In the paper , Ugot et al. trained deep convolutional generative adversarial network (DCGAN) to generate fake biometric fingerprint images. The quality of the generated images was measured using the NFIQ2 algorithm, and qualitative analysis of the fake fingerprints indicates that the images are of good quality.

In the paper , Kumar et al. have presented an assessment procedure for evaluating economic risks in virtual power plants (VPPs) using two deep learning techniques, viz. Nave Bayes algorithm and J48 bagging tree model. A comprehensive matrix of cyber security economic assessment based on threats, motives, impacts, challenges, and economic impacts is provided in this paper.

Aniche et al. have presented a Bio-EVS system in their work . Authors have adopted artificial intelligence (AI) pattern recognition and cyber security algorithms to encrypt and decrypt data. The presented system BIO-EVS was an excellent contribution as this system contains necessary features for e-voting systems and be adopted by government organizations in Sub-Saharan African countries.

Olalekan et al. have presented work on . In this paper, the authors developed a new methodology for intrusion detection by combining a wrapper feature selection approach based on a genetic algorithm with synthetic minority oversampling (SMOTE) and resample techniques to balance the class distribution.

In their paper entitled , Abirami and Palanikumar proposed a system to identify and detect malicious packets over a wide range of attacks using a machine learning classifier model. In their work, the KDD dataset is trained and evaluated for performance in terms of accuracy to provide a high-quality detection system. The proposed work uses machine learning algorithms such as support vector machine (SVM) and Nave Bayesian to train the model and validate using incoming live network packets for denial of service (DoS) type of attack.