Secure and Efficient <i>k</i> NN Classification for Industrial Internet of Things

Yang, Haomiao; Liang, Shaopeng; Ni, Jianbing; Li, Hongwei; Shen, Xuemin

doi:10.1109/jiot.2020.2992349

Cited by 38 publications

(12 citation statements)

References 35 publications

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“…An outlier/anomaly is detected by comparing the local density of the point investigated with the local density of its neighbours. The locality is provided by KNN [64] through which the density is estimated by measuring their distance. When the density of the point investigated is significantly lower than its neighbours' density, it is considered an outlier/anomaly.…”

Section: Bdac Evaluation Resultsmentioning

confidence: 99%

“…Multiple ML and DL methods were investigated and evaluated for each detection model of the BDAC Analysis Engine. In particular, regarding the detection models adopting a multiclass classification, the following ML methods were used: Logistic Regression [57], Linear Discriminant Analysis (LDA) [58], Decision Tree Classifier [38], Naive Bayes [59], SVM Linear [60], SVM RBF [60], Random Forest [61], Adaboost [53], Multi-Layer Perceptron (MLP) [62], Quadratic Discriminant Analysis [63], K Nearest Neighbour (KNN) [64]. Moreover, three custom DNNs were also used in our evaluation analysis.…”

Section: Bdac Comparative Methodsmentioning

confidence: 99%

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SPEAR SIEM: A Security Information and Event Management system for the Smart Grid

et al. 2021

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The technological leap of smart technologies has brought the conventional electrical grid in a new digital era called Smart Grid (SG), providing multiple benefits, such as two-way communication, pervasive control and self-healing. However, this new reality generates significant cybersecurity risks due to the heterogeneous and insecure nature of SG. In particular, SG relies on legacy communication protocols that have not been implemented having cybersecurity in mind. Moreover, the advent of the Internet of Things (IoT) creates severe cybersecurity challenges. The Security Information and Event Management (SIEM) systems constitute an emerging technology in the cybersecurity area, having the capability to detect, normalise and correlate a vast amount of security events. They can orchestrate the entire security of a smart ecosystem, such as SG. Nevertheless, the current SIEM systems do not take into account the unique SG peculiarities and characteristics like the legacy communication protocols. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) SIEM, which focuses on SG. The main contribution of our work is the design and implementation of a SIEM system capable of detecting, normalising and correlating cyberattacks and anomalies against a plethora of SG application-layer protocols. It is noteworthy that the detection performance of the SPEAR SIEM is demonstrated with real data originating from four real SG use case (a) hydropower plant, (b) substation, (c) power plant and (d) smart home.

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Section: Bdac Evaluation Resultsmentioning

confidence: 99%

Section: Bdac Comparative Methodsmentioning

confidence: 99%

SPEAR SIEM: A Security Information and Event Management system for the Smart Grid

et al. 2021

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“…As a popular machine learning algorithm, kNN has been successfully applied in many fields [34,35]. Some literatures try to improve it, mostly around the adjustment of parameter k [36,37].…”

Section: E K-nearest Neighbors Algorithm (Knn)mentioning

confidence: 99%

A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Pan

Fang

Chu

et al. 2021

Security and Communication Networks

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The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

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“…In 2020, Parvin et al [33] developed an electronic medical record analysis system on the blockchain based on KNN and LDA algorithms to automatically and safely share medical data sets among medical experts. In the same year, in order to realize the classification of large-scale ciphertext data in distributed servers, Yang et al [34] proposed a vector homomorphic encryption (VHE) scheme through constructing key switching matrix and noise matrix and constructed a secure distributed KNN classification algorithm (seed KNN) based on it. Recently, Kim et al [35] proposed an index-based KNN query processing algorithm and improved query processing efficiency through Yao's garbled code and data packaging technology.…”

Section: Related Workmentioning

confidence: 99%

Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

Liu

Wang²,

Tu³

et al. 2021

Security and Communication Networks

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With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.

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Secure and Efficient k NN Classification for Industrial Internet of Things

Cited by 38 publications

References 35 publications

SPEAR SIEM: A Security Information and Event Management system for the Smart Grid

SPEAR SIEM: A Security Information and Event Management system for the Smart Grid

A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

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