DETECTION OF FRAUDULENT MEDICARE PROVIDERS USING DECISION TREE AND LOGISTIC REGRESSION MODELS

Abstract

The Internet of Things (IoT) connects a vast array of devices, ranging from home appliances to industrial sensors, creating an interconnected network of smart devices. IoT applications generate large volumes of sensor data, which are highly susceptible to security breaches and attacks. Cyber-criminals may exploit vulnerabilities in the IoT ecosystem to manipulate sensor data, leading to disastrous consequences such as unauthorized access, data falsification, and service disruption. In addition, IoT-based attacks can lead to severe consequences such as data manipulation, privacy breaches, and economic losses. One of the major challenges is detecting and preventing attacks on the valuable sensor data collected by IoT devices. Traditional security methods designed for conventional networks may not be suitable for the complex and distributed nature of IoT systems. Here, Logistic Regression, and Random Forest classifiers are employed for attack detection from the IoT sensor data, where the first one is a straightforward yet powerful technique for binary classification, enabling the detection of simple intrusion attempts.

Specification

Description:The rapid growth of IoT devices across various domains, including critical infrastructure, healthcare, smart homes, and industrial applications, has created an extensive attack surface susceptible to cyber threats. IoT sensor data is vulnerable to various types of attacks, including but not limited to malware infections, DDoS (Distributed Denial of Service) attacks, data breaches, and physical tampering. These threats can lead to data compromise, service disruptions, and privacy breaches. Protecting the integrity and confidentiality of sensor data is crucial, as it often includes sensitive information. Unauthorized access or tampering with this data can have severe consequences. IoT environments pose unique challenges for attack detection due to their diverse and resource-constrained nature. Traditional security measures may not be directly applicable. Many IoT devices have limited computational power, memory, and bandwidth, making it challenging to implement resource-intensive security solutions. The need for real-time or near-real-time attack detection is critical, as prompt responses are essential to prevent or mitigate the impact of attacks on IoT ecosystems. Protecting critical infrastructure such as power grids, water treatment facilities, and transportation systems from cyber attacks is paramount. IoT attack detection ensures the reliability and security of these systems. In the healthcare sector, IoT devices are used for patient monitoring, drug administration, and medical equipment management. Detecting attacks in this context safeguards patient data and ensures the accuracy of medical procedures. IoT plays a pivotal role in creating smart cities with improved services and sustainability. Attack detection helps secure smart city infrastructure, including traffic management, waste management, and public safety systems. IoT applications in manufacturing, agriculture, and logistics rely on IoT sensors and devices. Detecting attacks in IIoT environments ensures the smooth operation of critical processes. IoT devices are common in smart homes, controlling lighting, heating, security, and entertainment systems. Attack detection safeguards personal data and home automation systems. Connected vehicles and smart transportation systems use IoT for navigation, traffic management, and safety features. Detecting attacks is crucial for passenger safety and system reliability.
Precision agriculture relies on IoT sensors for monitoring and optimizing crop growth and livestock management. Attack detection protects agricultural data and ensures efficient farming practices. IoT devices are used for energy monitoring, optimization, and grid management. Detecting attacks helps prevent disruptions to energy supply and enhances resource management. IoT sensors are employed for environmental monitoring, including air quality, water quality, and weather data collection. Attack detection ensures the accuracy and reliability of environmental data. In retail, IoT devices are used for inventory management, supply chain tracking, and customer engagement. Attack detection safeguards business operations and customer information. IoT is integrated into telecommunications networks for device management and service provisioning. Detecting attacks helps maintain the integrity of these networks. IoT devices are used in financial institutions for asset tracking, security, and customer service. Attack detection is crucial to protect sensitive financial data. IoT devices are increasingly used in educational settings for remote learning and campus management. Attack detection safeguards educational data resources. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicated that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks. The demographic characteristics of primary studies were also outlined. Every unsuccessful log-in attempt increases the complexity of solving the login puzzle for the next attempt.
, C , C , Claims:
i. We claim, threats can lead to data compromise, service disruptions, and privacy breaches.
ii. We claim, complex problems leads to personal and intellectual growth, as you learn and adapt to new challenges.
iii. We claim, unauthorized access or tampering with this data can have severe consequences.
iv. We claim, many IoT devices have limited computational power, memory, and bandwidth.

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