Browsing by Author "Barik, Kousik"

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Citation - WoS: 16
Citation - Scopus: 30
Cybersecurity Deep: Approaches, Attacks Dataset, and Comparative Study
(Taylor & Francis inc, 2022) Barik, Kousik; Misra, Sanjay; Konar, Karabi; Fernandez-Sanz, Luis; Murat, Koyuncu; Information Systems Engineering; Computer Engineering
Cyber attacks are increasing rapidly due to advanced digital technologies used by hackers. In addition, cybercriminals are conducting cyber attacks, making cyber security a rapidly growing field. Although machine learning techniques worked well in solving large-scale cybersecurity problems, an emerging concept of deep learning (DL) that caught on during this period caused information security specialists to improvise the result. The deep learning techniques analyzed in this study are convolution neural networks, recurrent neural networks, and deep neural networks in the context of cybersecurity.A framework is proposed, and a real-time laboratory setup is performed to capture network packets and examine this captured data using various DL techniques. A comparable interpretation is presented under the DL techniques with essential parameters, particularly accuracy, false alarm rate, precision, and detection rate. The DL techniques experimental output projects improvise the performance of various real-time cybersecurity applications on a real-time dataset. CNN model provides the highest accuracy of 98.64% with a precision of 98% with binary class. The RNN model offers the second-highest accuracy of 97.75%. CNN model provides the highest accuracy of 98.42 with multiclass class. The study shows that DL techniques can be effectively used in cybersecurity applications. Future research areas are being elaborated, including the potential research topics to improve several DL methodologies for cybersecurity applications.
Citation - WoS: 1
Citation - Scopus: 2
Ronsi: a Framework for Calculating Return on Network Security Investment
(Springer, 2023) Barik, Kousik; Misra, Sanjay; Fernandez-Sanz, Luis; Koyuncu, Murat; Information Systems Engineering; Computer Engineering
This competitive environment is rapidly driving technological modernization. Sophisticated cyber security attacks are expanding exponentially, inflicting reputation damage and financial and economic loss. Since security investments may take time to generate revenues, organizations need more time to convince top management to support them. Even though several ROSI techniques have been put out, they still need to address network-related infrastructure. By addressing gaps in existing techniques, this study delivers a comprehensive framework for calculating Return on Network Security Investment (RONSI). The proposed framework uses a statistical prediction model based on Bayes' theorem to calculate the RONSI. It is validated by Common Vulnerability Security Systems (CVSS) datasets and compared to existing studies. The results demonstrate that the annual loss is reduced to 75% with the proposed RONSI model after implementing a security strategy, and the proposed model is compared with existing studies. An organization can effectively justify investments in network-related infrastructure while enhancing its credibility and dependability in the cutthroat marketplace.