Faculty Publications
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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 Performance analysis of multiple classifiers using different term weighting schemes for sentiment analysis(Institute of Electrical and Electronics Engineers Inc., 2019) Anees, A.A.; Prakash Gupta, H.; Dalvi, A.P.; Gopinath, S.; Mohan, B.R.Information sharing and review platforms has generated large volumes of opinionated data which is usually in unstructured form. With the help of Sentiment Analysis, this data can be transformed into structured data which can be useful for commercial applications such as product reviews and feedback, marketing analysis, etc. The purpose of this work is to analyzes the performance of three classifiers(SVM, Naive Bayes, and Logistic Regression) with respect to providing positive or negative sentiment for three different scenarios(Movie Reviews, Election Opinions, and Food Reviews). The three classifiers are compared using fixed set of preprocessing steps and four different weighting schemes(Term frequency inverse document frequency (TFIDF), Term frequency inverse class frequency (TFICF), Mutual Information (MI), and X2 statistic (CHI)). The controlled experimental results showed that Logistic Regression classifier performs better in terms of overall accuracy when MI is used as weighting scheme. © 2019 IEEE.Item Feature selection using fast ensemble learning for network intrusion detection(Springer Verlag service@springer.de, 2020) Pasupulety, U.; Adwaith, C.D.; Hegde, S.; Patil, N.Network security plays a critical role in today’s digital system infrastructure. Everyday, there are hundreds of cases of data theft or loss due to the system’s integrity being compromised. The root cause of this issue is the lack of systems in place which are able to foresee the advent of such attacks. Network Intrusion detection techniques are important to prevent any system or network from malicious behavior. By analyzing a dataset with features summarizing the method in which connections are made to the network, any attempt to access it can be classified as malicious or benign. To improve the accuracy of network intrusion detection, various machine learning algorithms and optimization techniques are used. Feature selection helps in finding important attributes in the dataset which have a significant effect on the final classification. This results in the reduction of the size of the dataset, thereby simplifying the task of classification. In this work, we propose using multiple techniques as an ensemble for feature selection. To reduce training time and retain accuracy, the important features of a subset of the KDD Network Intrusion detection dataset were analyzed using this ensemble learning technique. Out of 41 possible features for network intrusion, it was found that host-based statistical features of network flow play an import role in predicting network intrusion. Our proposed methodology provides multiple levels of overall selected features, correlated to the number of individual feature selection techniques that selected them. At the highest level of selected features, our experiments yielded a 6% increase in intrusion detection accuracy, an 81% decrease in dataset size and a 5.4× decrease in runtime using a Multinomial Naive Bayes classifier on the original dataset. © Springer Nature Switzerland AG 2020.