Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

“…Dengan banyaknya data yang diproses di IDS, maka perlu dilakukan ekstraksi fitur untuk mengurangi biaya komputasi saat memproses data mentah di sistem sensor IoT [16]. Ekstraksi fitur bertujuan untuk mengekstraksi fitur dari fitur asli yang sudah ada dan memodifikasi fitur pada ukuran yang lebih kecil untuk mempercepat proses pelatihan dan meningkatkan akurasi [17] [18]. Pada penelitian mengusulkan Principal Components Analysis (PCA) untuk mengurangi dimensi dari dataset.…”

Section: Features Extraction Menggunakan Pcaunclassified

Deteksi Serangan pada Jaringan Kompleks IoT menggunakan Recurrent Neural Network

Winanto¹,

Kurniabudi

Sharipuddin

et al. 2022

Jur. Ris. Kom.

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The complex network in the Internet of Things is challenging to maintain network security. With network complexity including data, protocols, sizes, communications, standards, and more, it becomes difficult to implement an intrusion detection system (IDS). One way to improve IDS on complex IoT networks is by using deep learning to detect attacks that occur on complex IoT networks. Recurrent neural network (RNN) is a deep learning method that enhances the detection of complex IoT networks because it takes into account the current input as well as what has been learned from previously received inputs. When making decisions about RNNs, consider current information as well as what has been learned from previous input. Therefore, this study proposes the RNN method to improve the performance of attack detection systems on complex IoT networks. The results of this experiment show satisfactory results by increasing the performance of the accuracy detection system in complex IoT networks which reaches 87%.

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“…Due to the success of deep-learning technologies in image processing and natural language processing, they have been intensively studied in network intrusion detection [14,15], network traffic tracking [16], and network traffic abnormal behavior detection [17]. Besides, time-series density analysis [18], wavelet [19], principal components analysis [20], and ensemble learning technologies [21] have been extensively investigated in network anomaly detection.…”

Section: Network Anomaly Traffic Detection Approachesmentioning

confidence: 99%

“…Characterization of network anomaly traffic is one of the key technologies commonly used to model and detect network anomalies and then to raise the cybersecurity awareness capability of network administrators. e existing approaches of network anomaly detection can be mainly classified into six categories [1]: classification-based methods [2][3][4], clustering-based methods [5][6][7][8][9], statistical methods [10,11], stochastic methods [12,13], deep-learning-based methods [14][15][16][17], and others [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Network Anomaly Traffic with Euclidean Distance-Based Multiscale Fuzzy Entropy

Zhou

Xiao

Yang

et al. 2021

Security and Communication Networks

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The prosperity of mobile networks and social networks brings revolutionary conveniences to our daily lives. However, due to the complexity and fragility of the network environment, network attacks are becoming more and more serious. Characterization of network traffic is commonly used to model and detect network anomalies and finally to raise the cybersecurity awareness capability of network administrators. As a tool to characterize system running status, entropy-based time-series complexity measurement methods such as Multiscale Entropy (MSE), Composite Multiscale Entropy (CMSE), and Fuzzy Approximate Entropy (FuzzyEn) have been widely used in anomaly detection. However, the existing methods calculate the distance between vectors solely using the two most different elements of the two vectors. Furthermore, the similarity of vectors is calculated using the Heaviside function, which has a problem of bouncing between 0 and 1. The Euclidean Distance-Based Multiscale Fuzzy Entropy (EDM-Fuzzy) algorithm was proposed to avoid the two disadvantages and to measure entropy values of system signals more precisely, accurately, and stably. In this paper, the EDM-Fuzzy is applied to analyze the characteristics of abnormal network traffic such as botnet network traffic and Distributed Denial of Service (DDoS) attack traffic. The experimental analysis shows that the EDM-Fuzzy entropy technology is able to characterize the differences between normal traffic and abnormal traffic. The EDM-Fuzzy entropy characteristics of ARP traffic discovered in this paper can be used to detect various types of network traffic anomalies including botnet and DDoS attacks.

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Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

Cited by 10 publications

References 13 publications

IoT data analytics in dynamic environments: From an automated machine learning perspective

IoT data analytics in dynamic environments: From an automated machine learning perspective

Deteksi Serangan pada Jaringan Kompleks IoT menggunakan Recurrent Neural Network

Characterizing Network Anomaly Traffic with Euclidean Distance-Based Multiscale Fuzzy Entropy

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