Journal of Cardiovascular Disease Research
LOGISTIC REGRESSION AND RANDOM FOREST CLASSIFIER FOR ATTACK DETECTION IN IOT SENSOR DATA
S.Sunil Kumar , K. Vyshnavi , M. Pavana sri , K. Bharathi , N. Vyshnavi
JCDR. 2023: 761-768
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, IoTbased attacks can lead to severe consequences such as data manipulation, privacy breaches, and economic losses
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