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

N. B. Harikrishnan, R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran, B. Annappa, Mamoun Alazab

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


    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.

    Original languageEnglish
    Title of host publicationBig Data Recommender Systems
    Subtitle of host publicationAlgorithms, Architectures, Big Data, Security and Trust
    EditorsOsman Khalid, Samee Khan, Albert Zomaya
    Place of PublicationStevenage, UK
    PublisherInstitution of Engineering and Technology
    Number of pages34
    ISBN (Electronic)9781785619762
    ISBN (Print)9781785619755
    Publication statusPublished - 2019

    Publication series

    NameIET Professional Applications of Computing Series


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