Feature selection for intrusion detection system in Internet-of-Things (IoT)

Internet of Things (IoT) telah memasuki berbagai aspek kehidupan manusia, diantaranya smart city, smart home, smart street, dan smart industry yang memanfaatkan internet untuk memantau informasi yang dibutuhkan. Meskipun sudah dienkripsi dan diautentikasi, protokol jaringan <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) yang dapat menghubungkan benda-benda yang terbatas sumber daya di IoT masih belum dapat diandalkan. Hal ini dikarenakan benda-benda tersebut masih dapat terpapar oleh routing attacks yang berasal dari jaringan 6LoWPAN dan internet. Makalah ini menyajikan kinerja Smart Intrusion Detection System berdasarkan Compression Header Analyzer untuk menganalisis model routing attacks lainnya pada jaringan IoT. IDS menggunakan compression header 6LoWPAN sebagai fitur untuk machine learning algorithm dalam mempelajari jenis routing attacks. Skenario simulasi dikembangkan untuk mendeteksi routing attacks berupa selective forwarding attack dan sinkhole attack. Pengujian dilakukan menggunakan feature selection dan machine learning algorithm. Feature selection digunakan untuk menentukan fitur signifikan yang dapat membedakan antara aktivitas normal dan abnormal. Sementara machine learning algorithm digunakan untuk mengklasifikasikan routing attacks pada jaringan IoT. Ada tujuh machine learning algorithm yang digunakan dalam klasifikasi antara lain Random Forest, Random Tree, J48, Bayes Net, JRip, SMO, dan Naive Bayes. Hasil percobaan disajikan untuk menunjukkan kinerja Smart Intrusion Detection System berdasarkan Compression Header Analyzer dalam menganalisis routing attacks. Hasil evaluasi menunjukkan bahwa IDS ini dapat mendeteksi antara serangan dan non-serangan. AbstractInternet of Things (IoT) has entered various aspects of human life including smart city, smart home, smart street, and smart industries that use the internet to get the information they need. Even though it's encrypted and authenticated, Internet protocol <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) networks that can connect limited resources to IoT are still unreliable. This is because these objects can still be exposed to attacks from 6LoWPAN and the internet. This paper presents the performance of an Smart Intrusion Detection System based on Compression Header Analyzer to analyze other routing attack models on IoT networks. IDS uses a 6LoWPAN compression header as a feature for machine learning algorithms in learning the types of routing attacks. Simulation scenario was developed to detect routing attacks in the form of selective forwarding and sinkhole. Testing is done using the feature selection and machine learning algorithm. Feature selection is used to determine significant features that can distinguish between normal and abnormal activities. While machine learning algorithm is used to classify attacks on IoT networks. There were seven machine learning algorithms used in the classification including Random Forests, Random Trees, J48, Bayes Net, JRip, SMO, and Naive Bayes. Experiment Results to show the results of the Smart Intrusion Detection System based on Compression Header Analyzer in analyzing routing attacks. The evaluation results show that this IDS can protect between attacks and non-attacks.

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