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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT)
Mukhamad Angga Gumilang
Politeknik Negeri Jember, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Efficient Intrusion Detection System Utilizing Ensemble Learning and Statistical Feature Selection in Agricultural IoT Networks Ahmad Fahriyannur Rosyady; Bekti Maryuni Susanto; Agus Hariyanto; Mukhamad Angga Gumilang
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.448

Abstract

To enhance agricultural processes, smart agriculture combines a variety of devices,protocols, computing paradigms, and technologies. The cloud, edge computing, big data, andartificial intelligence all offer tools and solutions for managing, storing, and analyzing the vastamounts of data produced by various parts. Smart agriculture is still in its infancy and lacks severalsecurity measures, brought in the creation of numerous networks that are vulnerable to cyberattacks.The most well-known cyberattack is called a denial of service (DoS) attack, in which the attackersoverwhelm the network with massive amounts of data or requests, preventing the nodes fromaccessing the various services that are provided in that network. Intrusion Detection Systems (IDS)have shown to be effective defense mechanisms in the event of a cyberattack. The implementationof conventional intrusion detection systems (IDS) approaches in Internet of Things (IoT) deviceswas hindered by resource constraints, such as reduced computing capacity and low powerconsumption. In this paper, we used an ensemble learning and statistical based feature selectionstrategy to create a lightweight intrusion detection solution. The results show that the stackingensemble method is able to improve the performance of single machine learning in the classificationof anomalous events even though the computation time required is quite large compared to thecomputation time of single machine learning
Co-Authors Agus Hariyanto Ahmad Fahriyannur Rosyady Bekti Maryuni Susanto