Detecting insider attacks in medical cyber–physical networks based on behavioral profiling

Meng, Weizhi; Li, Wenjuan; Wang, Yu; Au, Man Ho

doi:10.1016/j.future.2018.06.007

Cited by 57 publications

(39 citation statements)

References 36 publications

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“…However, the standard deviation of sentiment values can truly reflect the public's sentiment tendency according to the above analysis. Meng et al [18], used Euclidean distance to judge a node's reputation; accordingly, we think it may be useful to calculate every point's Euclidean distance in the standard deviation figure to find hidden social event as quickly as possible. Figure 13 shows the standard deviation distribution of comment scores vs time in March 2017, and has similar characteristics to Figure 9.…”

Section: Comment Number Distribution Vs Timementioning

confidence: 99%

MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

et al. 2019

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The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.

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Section: Comment Number Distribution Vs Timementioning

confidence: 99%

MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

et al. 2019

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“…As described above, SDN can provide much global visibility and flexibility of network configuration by decoupling the network control from the data plane. However, SDN still suffers many limitations, i.e., the centralized controller may become a single point of failure [20], whereas distributed controllers might be vulnerable to insider attacks [29], [30].…”

Section: Framework Of Blockchain-based Sdnmentioning

confidence: 99%

Towards Blockchain-Based Software-Defined Networking: Security Challenges and Solutions

Meng

Liu

et al. 2020

IEICE Trans. Inf. & Syst.

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Software-Defined Networking (SDN) enables flexible deployment and innovation of new networking applications by decoupling and abstracting the control and data planes. It has radically changed the concept and way of building and managing networked systems, and reduced the barriers to entry for new players in the service markets. It is considered to be a promising solution providing the scale and versatility necessary for IoT. However, SDN may also face many challenges, i.e., the centralized control plane would be a single point of failure. With the advent of blockchain technology, blockchain-based SDN has become an emerging architecture for securing a distributed network environment. Motivated by this, in this work, we summarize the generic framework of blockchainbased SDN, discuss security challenges and relevant solutions, and provide insights on the future development in this field.

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“…In recent years, machine learning techniques have been widely applied in network security domain, such as IP traffic identification, 25 malware detection, 26 user authentication, [27][28][29][30] and anomaly detection. [31][32][33] The application of machine learning itself can raise security and privacy issues, especially in the case of distributed and outsourced learning, which need to be taken care of when deploying in the real-world. [34][35][36][37][38]…”

Section: Distributed/collaborative Intrusion Detectionmentioning

confidence: 99%

Adaptive machine learning‐based alarm reduction via edge computing for distributed intrusion detection systems

Wang

Meng

et al. 2019

Concurrency and Computation

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Summary To protect assets and resources from being hacked, intrusion detection systems are widely implemented in organizations around the world. However, false alarms are one challenging issue for such systems, which would significantly degrade the effectiveness of detection and greatly increase the burden of analysis. To solve this problem, building an intelligent false alarm filter using machine learning classifiers is considered as one promising solution, where an appropriate algorithm can be selected in an adaptive way in order to maintain the filtration accuracy. By means of cloud computing, the task of adaptive algorithm selection can be offloaded to the cloud, whereas it could cause communication delay and increase additional burden. In this work, motivated by the advent of edge computing, we propose a framework to improve the intelligent false alarm reduction for DIDS based on edge computing devices. Our framework can provide energy efficiency as the data can be processed at the edge for shorter response time. The evaluation results demonstrate that our framework can help reduce the workload for the central server and the delay as compared to the similar studies.

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Detecting insider attacks in medical cyber–physical networks based on behavioral profiling

Cited by 57 publications

References 36 publications

MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Towards Blockchain-Based Software-Defined Networking: Security Challenges and Solutions

Adaptive machine learning‐based alarm reduction via edge computing for distributed intrusion detection systems

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