5,555 research outputs found

    Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

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    It is estimated that between 600and600 and 850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with 125to125 to 175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

    Online Machine Learning-enabled Network Intrusion Detection

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    Machine Learning (ML) is seeing growing usage on Network Intrusion Detection Systems (NIDS) and allowing for an early detection of novel attacks, a prime example of how this technology may be applied to the field of cybersecurity. However, computational requirements associated with ML might make it difficult to process traffic in real time, particularly when dealing with a large volume of data or a high throughput connection, undermining the NIDS' usefulness or its detection capabilities. The goal of this dissertation is to implement an IDS capable of detecting malware command and control traffic over TLS. This system will include: 1) a baseline component which detects and discards blacklisted TLS certificates, 2) a ML component that classifies traffic flows based on a previously created model, and 3) an information storing component allowing for the ML model to be retrained as the TLS certificates' blacklist is updated. The online performance of this detection system will be evaluated against a high traffic volume, with conclusions contributing to Machine Learning-enabled network intrusion detection
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