Detecting and predicting malicious attacks on Internet of Things devices using deep neural networks
Disciplines
Artificial Intelligence and Robotics | Information Security
Abstract (300 words maximum)
In recent years, Internet of Things devices have become increasingly prevalent because of their ability to connect to the internet for the purpose of functioning automatically, eliminating the need for human interaction. The possible applications are endless-- IoT devices are present in most, if not all commercial sectors.
However, these benefits are also where the drawbacks of IoT are introduced. Before the advent of IoT, there were much less possible points of entry for an attacker, and those devices that did exist had specific standards and defenses in place to deter malicious activity. IoT poses a serious security concern because of the sheer number of devices in use and the novelty of the technology. The primary concerns IoT faces include losing user data through espionage, physical security, and hijacking devices.
This paper will make use of Machine Learning and Deep Learning Methods to perform detection for the purpose of preventing attacks on IoT devices.
Academic department under which the project should be listed
CCSE - Data Science and Analytics
Primary Investigator (PI) Name
Liang Zhao
Detecting and predicting malicious attacks on Internet of Things devices using deep neural networks
In recent years, Internet of Things devices have become increasingly prevalent because of their ability to connect to the internet for the purpose of functioning automatically, eliminating the need for human interaction. The possible applications are endless-- IoT devices are present in most, if not all commercial sectors.
However, these benefits are also where the drawbacks of IoT are introduced. Before the advent of IoT, there were much less possible points of entry for an attacker, and those devices that did exist had specific standards and defenses in place to deter malicious activity. IoT poses a serious security concern because of the sheer number of devices in use and the novelty of the technology. The primary concerns IoT faces include losing user data through espionage, physical security, and hijacking devices.
This paper will make use of Machine Learning and Deep Learning Methods to perform detection for the purpose of preventing attacks on IoT devices.