Smart Homes Network Security Issues and Solutions IOT

Hussein Ahmed Khalaf

There has been a consistent uptick in both the number of connected devices in use and the number of smart homes being built in recent years (IDATE, 20160. Smart locks, HVAC systems, and networking technologies like Zigbee and Z-Wave have all entered the market in recent years, and the number of available options has increased dramatically. This thesis serves a dual function. For starters, it provides a concise overview of the dangers facing smart homes in the near and far future from a security standpoint. Second, using this data as a starting point helps with the overall smart home security management. The contribution is a prototype of a security module designed to monitor for and alert users to any suspicious activity. In this thesis, we'll look into whether or not the smart hub is an adequate environment for this kind of mechanism in terms of its impact on system resources and the frequency with which it may be detected. Our research for this thesis has centered on creating and assessing a security system for the Internet of Things (IoT) that can reduce the risk of cyber attacks on individuals and communities.

A Hybrid Technique Based on RF-PCA and ANN for Detecting DDoS Attacks IoT

Hayder Jalo · Mohsen Heydarian

The increasing reliance on smart products has increased vulnerabilities in Internet of Things (IoT) traffic, which poses significant security risks. These vulnerabilities allowed some hackers to exploit them, which led to system performance degradation. Attacks can lead to these vulnerabilities to various undesirable outcomes, including data leakage, economic losses, data breaches, operational disruptions, and damage to the company's reputation. To address these security challenges, network intrusion detection alarms play a crucial role in assessing system security. In recent years, the proliferation of intelligent and soft computing-based algorithmic and structural frameworks has been evident. However, previous studies have faced challenges related to comprehensiveness, zero-day attacks, realism, and data interpretation. In light of these concerns, this study proposes to design a neural network for proactive detection of attacks. Moreover, we propose to use a hybrid system called RF-PCA to facilitate dimensionality reduction and help classifiers. Notably, this is the first application of a BOT-IoT data set in such an approach. The study also includes a discussion of relevant IoT terms in the context of our work. The proposed method uses high-level data features to represent and draw conclusive conclusions. To evaluate its effectiveness, an experiment was conducted using Python as the programming environment, achieving a remarkable detection rate of 99.73%.