Faculty Publications

Now showing 1 - 3 of 3

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.
Real-time big data analytics framework with data blending approach for multiple data sources in smart city applications
(West University of Timisoara, 2020) Manjunatha, S.; Annappa, A.
Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead to the continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implemented using a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification performs better in generating emergency alerts. © 2020 SCPE.
Real-time emergency event detection system for public safety using multi-source data
(Science and Engineering Research Support Society ijbsbt@sersc.org PO Box 5014Sandy Bay TAS 7005 Tasmania, 2020) Manjunatha, S.; Annappa, A.
Public safety is an essential service offered in smart city projects to provide better safety and security for individuals and city infrastructure. The advancement in the field of Information Technology and the Internet of Things created much scope for using smart applications in the city to enhance the quality of service, leading to a better life in cities. This digitization generates a large amount of data within the city from distinct sources like social media, IoT, sensors, any user-generated content from smart applications. The data generated within the city are analyzed to discover valuable insights for producing better data-driven decisions and predictions, that are more crucial for efficient city administration. Making quick decisions and early predictions of crimes by real-time analysis of data help the smart policing system to provide better services in the city. This paper describes the scope of real-time big data analytics for finding appro-priate predictions and making quick decisions for public safety. A real-time big data analytics framework using multiple data sources is proposed for the smart policing service in the smart city environment. The framework is used to design a real-time emergency events detection system to help city administrators in taking quick actions for the safety of people and city infrastructure. The proposed system achieved an average accuracy of 73% for emergency event classification. © 2020 SERSC.

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