Faculty Publications
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Item Robust Detection of Evasive Fileless Powershell Malware: A Machine Learning Approach(2025 International Conference on Artificial intelligence and Emerging Technologies (ICAIET), Artificial intelligence and Emerging Technologies (ICAIET), 2025 International Conference on,20250828, IEEE Xplore Digital Library, 2025) Meher, Manish Kumar; Rath, Adyasha; Panda, Ganapati; Thanapati, Biswa Bhusana; Puthal, Deepak KumarIn the growing age of cybersecurity, the most obnoxious attack type is PowerShell-based fileless attacks. PowerShell provides the most favored environment to perform advanced tasks. This feature leads to its misuse, especially in the case of fileless attacks. The traditional methods uses signature based detection, are not able to identify the malware. Modern-day scripts are complex and obfuscated, which avoids detection. This paper proposed a machine learning (ML)-based model for malicious sample detection using feature analysis. It efficiently differentiates the benign and malicious samples with a considerable degree of accuracy. To enhance the detection further, the mutual information (MI) technique was applied to retrieve the most efficient and relevant features. This extensive experiment evaluation demonstrated that the proposed ML-based model achieved improved accuracy of 97.64 % and a robust performance.Item Environmental, social and governance-type investing: a multi-stakeholder machine learning analysis(Management Decision, 2025-03-26) Jaiswal, Rachana; Gupta, Shashank; Tiwari, Aviral KumarPurpose This research delves into the determinants influencing the adoption of environmental, social and governance (ESG) investing through an analysis of social media dialogs using the uses and gratification theory. Design/methodology/approach This study employs a mixed-methods approach, integrating sentiment analysis, topic modeling, clustering, causal loop analysis and ethnography to examine ESG-related content on social media. Analyzing social media data, study identified key themes and derived ten propositions about ESG investing. Industry professionals, financial advisors and investors further validated these findings through expert interviews. Combining data-driven analysis and qualitative insights provides a comprehensive understanding of how social media shapes investor preferences and decision-making in the ESG domain. Findings Environmental aspects, such as conservation, preservation of natural resources, renewable and clean energy, biodiversity, restoration and eco-friendly products and technologies, shape attitudes toward ESG investing. Social considerations, including inclusivity, diversity, social justice, human rights, stakeholder engagement, transparency, community development and philanthropy, significantly influence ESG investing sentiments. Governance elements such as transparency, accountability, ethical governance, compliance, risk management, regulatory compliance and responsible leadership also play a pivotal role in shaping ESG investing opinions. Practical implications This study presents actionable insights for policymakers and organizations by identifying key constructs in ESG investing and proposing an integrated framework that includes mediating factors like resource efficiency and stakeholder engagement alongside moderating elements such as regulatory environment and investor preferences. Policymakers should establish standardized ESG reporting frameworks, incentivize sustainable practices and use social media data for regulatory purposes. For businesses, integrating social media insights into decision-making can enhance ESG communication strategies and accountability. These measures will foster greater transparency, strengthen investor relations and contribute to a more sustainable and inclusive global economy. Originality/value To the authors' best knowledge, this is the first study to investigate improving ESG investing preferences based on big data mined from social media platforms.Item An Evaluation Framework for Machine Learning and Data Science (ML/DS) Based Financial Strategies: A Case Study Driven Decision Model.(IEEE Transactions on Engineering Management, Engineering Management, 2025) Saadatmand, M.; Daim, T. Mena,; C. Yalcin, H.; Bolatan, G.; Chatterjee, MBig data and computational technologies are increasingly important worldwide in asset and investment management. Many investment management firms are adopting these data science (DS) methods and technologies to improve performance across all investment processes. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the main objective of this research was to develop a multicriteria assessment framework and scoring decision support system to evaluate quantitative investment strategies that apply machine learning (ML) and DS techniques in their research and development. Subject matter experts will assess all framework perspectives from a systematic literature review to approve their reliability. The perspectives consist of economic and financial foundations , data perspective , features perspective , modeling perspective , and performance perspective . The research methodology applied was the hierarchical decision model (HDM) to provide a 360° view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. This study accomplished a rigorous integration of an extensive literature review connecting DS, ML, and investment decision-making in developing quantitative investment strategies. As a result, the major contribution of this study is the comprehensive examination, which included identifying and quantifying perspectives and criteria. The results, while limited indicated significant gaps in strategies examined and therefore generated critical knowledge to improve ML/DS-driven investment strategies, which are valuable for financial companies and policymakers.