Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
4 results
Search Results
Item Hybrid Malicious Encrypted Network Traffic Flow Detection Model(Springer Science and Business Media Deutschland GmbH, 2023) Hublikar, S.; Shet, N.S.V.Encrypted communication technology has evolved as the network, and Internet applications have advanced. Malicious communication, on the other hand, employs encryption to bypass standard detection and security protection. The existing security prevention and detection technologies are unable to identify harmful communication that is encrypted. The growth of artificial intelligence (AI) in these days has enabled to employ machine learning (ML) as well as deep learning approaches to identify encrypted malicious communications without decryption, with remarkably precise detection outcomes. At this moment, research on detecting harmful encrypted traffic is mostly focused on analyzing the features of encrypted data and selecting neural network (NN) techniques. Hybrid ML is proposed in this study by merging two well-performing data mining algorithms with natural language processing tasks. Here, a new traffic flow detection method is performed by the hybrid ML technique. At first, the benchmark data is collected from public sources. The features are extracted using the convolutional layer of deep convolutional neural network (DCNN). Then, the weighted feature extraction is performed by grasshopper optimization algorithm (GOA). Employed the hybrid machine learning-based malicious detection with the “support vector machine (SVM) and neural network (NN)” is utilized in this model to detect the traffic affected by malicious activities, where the hidden neuron count of NN and kernel of SVM are tuning by the same GOA for increasing the accuracy and precision. This research provides findings from experiment, encouraging various researchers to develop the research as future work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Biometric-Based Authentication in Online Banking(Springer Science and Business Media Deutschland GmbH, 2023) Hublikar, S.; Pattanashetty, V.B.; Mane, V.; Pillai, P.S.; Lakkannavar, M.; Shet, N.S.V.The use of technology has become the integral part of human life. Most importantly the introduction of Internet has made the lives of people easy and due to its cost effectiveness; usage of Internet by people has been increased. The Internet has made the people to move toward the online mode of transaction. Banks recommend their customers to use Internet banking facility and assure it as a safest mode of transaction but it is associated with huge risk. The continuous rise in online banking brings several security issues and increased cost of implementing higher security systems for banks and customers. Present online banking technology works on 2-Factor authentication mode, i.e., it works on both transaction level and authentication level. The main problem associated with 2-Factor authentication is that there may be risk associated, like cyberattack (SIM swapping fraud), etc., so we have proposed a biometric authentication technique which makes transaction more secure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Machine Learning-Based Malicious URLs Detection(Springer Science and Business Media Deutschland GmbH, 2023) Hublikar, S.; Kalginkar, A.; Shet, N.S.V.Malicious URL’s also known as harmful website which is a serious and common thread to network security. World Wide Web (WWW) (Vanhoenshoven et al. in 2016 IEEE symposium series on computational intelligence (SSCI). IEEE, 2016 [1]), which encourages a wide range of illicit actions like financial fraud, malware distribution and e-commerce. It is of vital importance to discover and act on different ventures on time. Several procedures like blacklisting have been carried out to detect malicious Uniform Resource Locators (URLs) (Catak et al. in Malicious URL detection using machine learning. IGI Global, 2021 [2]). To advance the majority of harmful Uniform Resource Locator’s, different machine learning algorithms are executed in the modern years. Here, in this research paper, we are addressing malicious URLs detection as a problem of classification and also to understand the working and functioning of known machine learning classifiers, namely support vector machines (SVMs), K-nearest neighbors (KNNs), random forest (RF) and Naive Bayes (Vanhoenshoven et al. in 2016 IEEE symposium series on computational intelligence (SSCI). IEEE, 2016 [1]). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Malicious encrypted network traffic flow detection using enhanced optimal deep feature selection with DLSTM(World Scientific, 2024) Hublikar, S.; Shet, N.S.V.This paper plans to implement a novel detection model of maliciously encrypted internet protocol network flow using the deep structured concept. The major processing levels are (i) data collection, (ii) feature extraction, (iii) optimal feature selection, and (iv) detection. In the beginning, the standard dataset is taken from online databases. The deep convolutional neural network (DCNN) is introduced for the deep feature extraction process. The accurate features are chosen by the crossover decision-based krill herd algorithm (CD-KHA) which helps to minimize the training complexity of the deep structured architecture. These selected features are given to the hybridized deep learning with long short-term memory (LSTM) and deep neural network (DNN). Here, the structural design of the model is improved by the same CD-KHA. Through the comparison and analysis, the accuracy rate of the offered method shows higher performance than the other baseline approaches. © 2024 World Scientific Publishing Company.