Data-Driven Cyber Security in Perspective—Intelligent Traffic Analysis

Coulter, Rory; Han, Qing‐Long; Pan, Lei; Zhang, Jun; Xiang, Yang

doi:10.1109/tcyb.2019.2940940

Cited by 94 publications

(35 citation statements)

References 74 publications

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“…They also extracted and summarized the research methodology at critical phases of predicting cybersecurity incident. In the research of Coulter et al [18], a new research methodology of data-driven cyber security (DDCS) was demonstrated, and its application in social and Internet traffic analysis was studied. DDCS shows the strong link between data, model, and methodology during the review of key recent works in Twitter spam detection and IP traffic classification.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the idea of classification, the researchers have designed numerical form characteristics to identify spam users. The supervised machine learning algorithm can be split into a single classification algorithm and an integrated classification algorithm (e.g., Support Vector Machine (SVM) [3] [8][9][10][11] [13][14], meta-classifiers (Decorate, Logit Boost) [4], Naive Bayesian (NB) [6][9] [11], Back Propagation Neural Network (BP) [16], Radial Basis Function (RBF) [18], Extreme Learning Machine (ELM) [8] [22], K-nearest Neighbor (KNN) [9] [19], Decision Tree (DT) [9] [20], Random Forest (RF) [5] [7][8][9][ [23][24][25][26] and eXtreme Gradient Boosting (XGBoost) [31,32]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Social Network Spam Based on Improved Extreme Learning Machine

2020

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With the rapid advancement of the online social network, social media like Twitter has been increasingly critical to real life and become the prime objective of spammers. Twitter spam detection refers to a complex task for the involvement of a range of characteristics, and spam and non-spam have caused unbalanced data distribution in Twitter. To solve the mentioned problems, Twitter spam characteristics are analyzed as the user attribute, content, activity and relationship in this study, and a novel spam detection algorithm is designed based on regularized extreme learning machine, called the Improved Incremental Fuzzy-kernel-regularized Extreme Learning Machine (I2FELM), which is used to detect the Twitter spam accurately. As revealed from the experience validation results, the proposed I2FELM can efficiently identify the balanced and unbalanced dataset. Moreover, with few characteristics taken, the I2FELM can more effectively detect spam, which proves the effectiveness of the algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Social Network Spam Based on Improved Extreme Learning Machine

2020

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“…However, it still faces some sort based attack and the client storage is heavy. Furthermore, data-driven cyber security [14][15][16][17] is also the focus of protection.…”

Section: Introductionmentioning

confidence: 99%

EncodeORE: Reducing Leakage and Preserving Practicality in Order-Revealing Encryption

Liu

et al. 2022

IEEE Trans. Dependable and Secure Comput.

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Order-preserving encryption (OPE) is a cryptographic primitive that preserves the order of plaintexts. In the past few years, many OPE schemes were proposed to solve the problem of executing range queries in encrypted databases. However, OPE leaks some certain information (for example, the order of ciphertext), so it is vulnerable to many attacks. Subsequently, order-revealing encryption (ORE) was proposed by Boneh et al. (Eurocrypt 2015) as a generalization of order-preserving encryption. It breaks through the limitation of the numeric order of OPE plaintext. It implements ciphertext comparison for any specific form of plaintext through a publicly computable comparison function. In this work, we aim to design a new ORE scheme which reduces the leakages and preserves the practicality in terms of ciphertext length and encryption time. We first propose the hybrid model named HybridORE. Then, we propose an improved scheme named EncodeORE which achieves acceptable security and appropriate ciphertext length. They both explore the encode strategy of encoding plaintext into different parts and apply suitable ORE algorithms to each part according to its security characteristics to reduce leakages. Compared with the typical CLWW scheme (FSE 2016) and Lewi-Wu (CCS 2016) in large domain, they have fewer leakages. The experiment shows that the proposed EncodeORE is very practical.

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“…In reconstruction network, the fused feature maps are the input, and the source image pair also used for reconstruction of fusion image. Considering trustworthy is a critical issue in the real-world applications of image fusion [7][8][9][10], we also propose to apply blockchain technology to protect sensitive information.…”

Section: Introductionmentioning

confidence: 99%

Trustworthy Image Fusion with Deep Learning for Wireless Applications

Zhang

Tai

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

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To fuse infrared and visible images in wireless applications, the extraction and transmission of characteristic information security is an important task. The fused image quality depends on the effectiveness of feature extraction and the transmission of image pair characteristics. However, most fusion approaches based on deep learning do not make effective use of the features for image fusion, which results in missing semantic content in the fused image. In this paper, a novel trustworthy image fusion method is proposed to address these issues, which applies convolutional neural networks for feature extraction and blockchain technology to protect sensitive information. The new method can effectively reduce the loss of feature information by making the output of the feature extraction network in each convolutional layer to be fed to the next layer along with the production of the previous layer, and in order to ensure the similarity between the fused image and the original image, the original input image feature map is used as the input of the reconstruction network in the image reconstruction network. Compared to other methods, the experimental results show that our proposed method can achieve better quality and satisfy human perception.

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Data-Driven Cyber Security in Perspective—Intelligent Traffic Analysis

Cited by 94 publications

References 74 publications

Detection of Social Network Spam Based on Improved Extreme Learning Machine

Detection of Social Network Spam Based on Improved Extreme Learning Machine

EncodeORE: Reducing Leakage and Preserving Practicality in Order-Revealing Encryption

Trustworthy Image Fusion with Deep Learning for Wireless Applications

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