International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Realtime Network Based Anomaly Detection and Malware Analysis for SMEs and Smart Homes

K.N.H De SilvaDepartment of Computer Systems Engineering, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe 10115, Sri LankaM.A.S.B ManchanayakaDepartment of Computer Systems Engineering, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe 10115, Sri LankaD.L.S.I PunyasiriDepartment of Computer Systems Engineering, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe 10115, Sri LankaH.A.D.N PereraDepartment of Computer Systems Engineering, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe 10115, Sri LankaAnjalie GamageDepartment of Computer Systems Engineering, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe 10115, Sri LankaNarmada GamageDepartment of Computer Systems Engineering, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe 10115, Sri Lanka

Vol 7 No 10 (2023): Volume 7, Issue 10, October 2023 | Pages: 249-257

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 29-10-2023

doi Logo doi.org/10.47001/IRJIET/2023.710032

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Abstract

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.

Keywords

Internet of Things (IoT), Natural Language Processing (NLP), Small and Medium-sized Enterprises (SMEs), Raspberry-Pi, Firewall, Machine Learning Module


Citation of this Article

K.N.H De Silva, M.A.S.B Manchanayaka, D.L.S.I Punyasiri, H.A.D.N Perera, Anjalie Gamage, Narmada Gamage, “Realtime Network Based Anomaly Detection and Malware Analysis for SMEs and Smart Homes” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 10, pp 249-257, October 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.710032

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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