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Item Deep learning architecture for big data analytics in detecting intrusions and malicious URL(Institution of Engineering and Technology, 2019) Harikrishnan, N.B.; Ravi, R.; Padannayil, K.P.; Poornachandran, P.; Annappa, A.; Alazab, M.Security attacks are one of the major threats in today’s world. These attacks exploit the vulnerabilities in a system or online sites for financial gain. By doing so, there arises a huge loss in revenue and reputation for both government and private firms. These attacks are generally carried out through malware interception, intrusions, phishing uniform resource locator (URL). There are techniques like signature-based detection, anomaly detection, state full protocol to detect intrusions, blacklisting for detecting phishing URL. Even though these techniques claim to thwart cyberattacks, they often fail to detect new attacks or variants of existing attacks. The second reason why these techniques fail is the dynamic nature of attacks and lack of annotated data. In such a situation, we need to propose a system which can capture the changing trends of cyberattacks to some extent. For this, we used supervised and unsupervised learning techniques. The growing problem of intrusions and phishing URLs generates a need for a reliable architectural-based solution that can efficiently identify intrusions and phishing URLs. This chapter aims to provide a comprehensive survey of intrusion and phishing URL detection techniques and deep learning. It presents and evaluates a highly effective deep learning architecture to automat intrusion and phishing URL Detection. The proposed method is an artificial intelligence (AI)-based hybrid architecture for an organization which provides supervised and unsupervised-based solutions to tackle intrusions, and phishing URL detection. The prototype model uses various classical machine learning (ML) classifiers and deep learning architectures. The research specifically focuses on detecting and classifying intrusions and phishing URL detection. © The Institution of Engineering and Technology 2020.Item Machine Learning Based Data Quality Model for COVID-19 Related Big Data(Springer Science and Business Media Deutschland GmbH, 2022) Kumar, P.V.; Chandrashekar, A.; Chandrasekaran, K.Big Data is being used in various aspects of technology. The quality of the data being used is essential and needs to be accurate, reliable, and free of defects. The difficulty in improving the quality of big data can be overcome by leveraging computing resources and advanced techniques. In this paper, we propose a solution that utilizes a machine learning (ML) model combined with a data quality model to improve the quality of data. An auto encoder neural network that detects the anomalies in the data is used as the Machine Learning model. This is followed by using the data quality model to ensure the data meets appropriate data quality characteristics. The results obtained from our solution show that the quality of data can be improved efficiently and effortlessly which in turn aids researchers to achieve better results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.