Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
The
increasing risk landscape of cyberattacks requires the implementation of strong
solutions for detecting anomalies in real-time within network systems and
analyzing malware. These solutions should be specifically designed to cater to
the needs of Small and Medium-sized Enterprises (SMEs) and smart homes. This
study presents a comprehensive framework to effectively address the pressing
security concern at hand. To begin with, a cost-effective and advanced firewall
architecture, based on Raspberry Pi, is implemented in order to protect
connected devices from external malicious entities. Simultaneously, this
architecture captures network traffic for further analysis. By utilizing
supervised machine learning models, such as Random Forests, a system has been
developed to detect anomalies in Internet of Things (IoT) environments. This
system enables timely notifications and facilitates informative discussions
regarding the identified irregularities. In order to enhance the ability to
detect network anomalies, a unique methodology is implemented, which involves
the utilization of Natural Language Processing (NLP). This approach is
complemented by the strategic deployment of honeypots to confuse potential
attackers and is further supported by a collaborative infrastructure for
sharing information on threats. Moreover, this study conducts an analysis of
malware using both signature-based and behavior-based approaches. These
methodologies are facilitated by supervised machine learning models, which are
seamlessly incorporated with an alerting mechanism to ensure prompt
notifications. This research study makes a significant contribution to the
field by addressing existing gaps in knowledge, such as comparative
assessments, zero-day vulnerabilities, user-centric design, and
cost-effectiveness considerations. Additionally, it provides a practical guide
for enhancing cyber resilience in small and medium-sized enterprises (SMEs) and
smart homes.
Country : Sri Lanka
IRJIET, Volume 7, Issue 10, October 2023 pp. 249-257