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The dramatic adoption of Bitcoin and other cryptocurrencies in the USA has revolutionized the financial landscape and provided unprecedented investment and transaction efficiency opportunities. The prime objective of this research project…

In this paper, Bayesian based aggregation of decision trees in an ensemble (decision forest) is investigated. The focus is laid on multi-class classification with number of samples significantly skewed toward one of the classes. The…

Botnets are increasingly used by malicious actors, creating increasing threat to a large number of internet users. To address this growing danger, we propose to study methods to detect botnets, especially those that are hard to capture with…

We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests. This problem is motivated from fast emerging studies…

In response to the increasing ransomware threat, this study presents a novel detection system that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. By leveraging Sysmon logs, the system enables…

In the era of the digitally driven economy, where there has been an exponential surge in digital payment systems and other online activities, various forms of fraudulent activities have accompanied the digital growth, out of which credit…

Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only consider the information in a single scale…

Discovery of diagnostic and prognostic molecular markers is important and actively pursued the research field in cancer research. For complex diseases, this process is often performed using Machine Learning. The current study compares two…

This article attempts to discover the surreptitious features of ransomware and to address it in information systems security research. It intends to elicit attention with regard to ransomware, a newly emerged cyber threat using such…

Cyclostationarity involves periodic statistical variations in signals and processes, commonly used in signal analysis and network security. In the context of attacks, cyclostationarity helps detect malicious behaviors within network…

Ransomware, a type of malicious software that encrypts a victim's files and only releases the cryptographic key once a ransom is paid, has emerged as a potentially devastating class of cybercrimes in the past few years. In this paper, we…

The rapid proliferation of Internet of Medical Things (IoMT) devices in healthcare has introduced unique cybersecurity challenges, primarily due to the diverse communication protocols and critical nature of these devices This research aims…

According to The Exchange Act, 1934 unlawful insider trading is the abuse of access to privileged corporate information. While a blurred line between "routine" the "opportunistic" insider trading exists, detection of strategies that…

The expansion of digital payment systems has heightened both the scale and intricacy of online financial transactions, thereby increasing vulnerability to fraudulent activities. Detecting fraud effectively is complicated by the changing…

Data owners face increasing liability for how the use of their data could harm under-priviliged communities. Stakeholders would like to identify the characteristics of data that lead to algorithms being biased against any particular…

The incidence rate for skin cancer has been steadily increasing throughout the world, leading to it being a serious issue. Diagnosis at an early stage has the potential to drastically reduce the harm caused by the disease, however, the…

Computer network anomaly detection and log analysis, as an important topic in the field of network security, has been a key task to ensure network security and system reliability. First, existing network anomaly detection and log analysis…

Decision forest, including RandomForest, XGBoost, and LightGBM, is one of the most popular machine learning techniques used in many industrial scenarios, such as credit card fraud detection, ranking, and business intelligence. Because the…

An algorithm to improve performance parameter for unsupervised decision forest clustering and density estimation is presented. Specifically, a dual assignment parameter is introduced as a density estimator by combining Random Forest and…

The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for (1)…