Enhancement performance of random forest algorithm via one hot encoding for IoT IDS

The random forest algorithm is one of important supervised machine learning (ML) algorithms. In the present paper, the accuracy of the results of the random forest (RF) algorithm has been improved by the use of the One Hot Encoding method. The Intrusion Detection System (IDS) can be defined as a system that can predict security vulnerabilities within network traffic and is located out of range on a network infrastructure. It does not affect the efficiency of the built-in network because it analyzes a copy of the built-in traffic flow and reports results to the administrator by giving alerts. However, since IDS is a listening system only, it cannot take automatic action to prevent an attack or security vulnerability detected from infecting the system, it provides information about the source address to start the break-in, the address of the target and the type of suspected attack. The IoTID20 dataset is used to verify the improved algorithm, where this dataset is having three targets, the proposed system is compared with the state-of-art approaches and shows superiority over them

Enhancement performance of random forest algorithm via one hot encoding for IoT IDS

The random forest algorithm is one of important supervised machine learning (ML) algorithms. In the present paper, the accuracy of the results of the random forest (RF) algorithm has been improved by the use of the One Hot Encoding method. The Intrusion Detection System (IDS) can be defined as a system that can predict security vulnerabilities within network traffic and is located out of range on a network infrastructure. It does not affect the efficiency of the built-in network because it analyzes a copy of the built-in traffic flow and reports results to the administrator by giving alerts. However, since IDS is a listening system only, it cannot take automatic action to prevent an attack or security vulnerability detected from infecting the system, it provides information about the source address to start the break-in, the address of the target and the type of suspected attack. The IoTID20 dataset is used to verify the improved algorithm, where this dataset is having three targets, the proposed system is compared with the state-of-art approaches and shows superiority over them

Building a cheap security solution for a smart home network using a Raspberry Pi and Snort 3

Undergraduate thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in / Computer Science, May 2020Digitisation is moving at breakneck speed, and soon almost all devices will be interconnected via a network. The goal of the Internet of Things (IoT) is to extend internet connectivity to such devices. In a smart home, examples of such devices are a toaster or a refrigerator that could be linked to the Internet and accessed remotely. This predicted future promises to improve the standard of living; however, it brings with it a new set of security challenges, such as a denial of service attack and ARP spoofing, among others. This paper therefore, seeks to discover if using Snort 3, a popular intrusion detection system deployed on a Raspberry Pi would be able to protect these devices.Ashesi Universit