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## Deep learning architecture for big data analytics in detecting intrusions and malicious URL

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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.

Chapter Contents:

• 14.1 Introduction
• 14.2 Related works
• 14.2.1 Network intrusion detection systems (NIDSs)
• 14.2.2 Related works on phishing URL detection
• 14.3 Background
• 14.3.1 Deep neural network
• 14.3.2 Recurrent neural network
• 14.3.3 Convolutional neural network
• 14.4 Intrusion detection
• 14.4.1 Description of KDD-Cup-99 data set
• 14.4.2 Description of Kyoto network intrusion detection (ID) data set
• 14.4.3 Experiments on KDD-Cup-99
• 14.4.4 Proposed architecture for KDD-Cup-99 data set
• 14.4.5 Experiments on Kyoto network intrusion detection (ID) data set
• 14.4.6 Proposed architecture for Kyoto
• 14.4.7 Evaluation results for KDD-Cup-99
• 14.4.8 Evaluation results for Kyoto
• 14.5 Intrusion detection (ID) using multidimensional zoom (M-ZOOM) framework
• 14.5.1 Density measures
• 14.5.2 Problem formulation
• 14.5.3 Data set description
• 14.5.4 Experiments and observations
• 14.6 Phishing URL detection
• 14.6.1 Data set description of phishing URL detection
• 14.6.2 URL representation
• 14.6.3 Experiments
• 14.6.4 Hyper-parameter tuning
• 14.6.5 Proposed architecture for URL analysis
• 14.7 Proposed architecture for machine learning based cybersecurity
• 14.8 Conclusion and future work
• Acknowledgments
• References

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