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.

An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction

Hussein Alrammahi · Mohammed Thakir Mahmood

Intrusion Detection Systems (IDS) are elementary building blocks of network security that can be used to detect unauthorized access and malicious activity. But traditional IDS approaches often suffer from problems such as high false positives, inability to adapt quickly to new threats, and scalability. This paper presents an advanced intrusion detection model that uses machine learning algorithms like Random Forest, Support Vector Machine (SVM), and Neural Networks to enhance detection. Using the KDD Cup 1999 data, the framework was highly preprocessed, feature engineered, and hyperparameters adjusted to achieve optimal performance. The Neural Network model outperformed other algorithms at 92.5% accuracy, 93.8% recall, and 92.4% F1-score, proving its ability to identify complex attack patterns with minimal false positives effectively. Additionally, the proposed framework reflected significant improvement over existing IDS solutions that always achieve accuracies of 80–85%. Intrusion Detection Systems (IDS) are important components of security, assuming the task of monitoring, detecting, and responding to unauthorized activities in network frameworks. This work's most notable contributions are its integration of sophisticated machine learning methods, systematic assessment of detection performance on a wide range of attack types, and comparison with well-established IDS benchmarks. In spite of facing issues like the complexity of the dataset and computational requirements, findings point to the efficacy of machine learning-based IDS in countering modern-day cybersecurity threats. Real-time data fusion and improving model interpretability for real-world implementation are areas that need to be addressed in the future.

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%.