Quantifying the Significance and Relevance of Cyber-Security Text Through Textual Similarity and Cyber-Security Knowledge Graph

Mendsaikhan, Otgonpurev; Hasegawa, Hirokazu; Yamaguchi, Yukiko; Shimada, Hiroyuki

doi:10.1109/access.2020.3027321

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…initialize the network, that is, set the SOM network and initialize the initial value of each training parameter [ 10 , 11 ]. The values to be initialized are: the random number that gives the link weight W in the [0,1] interval; determine the initial value of the learning rate n (1) η (o) (0 < η ( ω )< 1); and determine the initial value n ( o ) of the domain n ( t ), where G is the winning neuron; calculate the Euclid distance between the weight vector w = ( w , m ) and the input sample x = ( x 1 , x 2 ,…, x n ), and select the minimum distance to determine the winning neuron [ 12 ].…”

Section: Visualization Of Key Knowledge Points Of Nursing Teaching Ma...mentioning

confidence: 99%

Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis

Fang

2022

Computational Intelligence and Neuroscience

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The traditional nursing teaching knowledge point recommendation algorithm based on collaborative filtering is difficult to deal with the problem of data sparsity, while the traditional recommendation algorithm based on matrix decomposition has poor scalability in dealing with high-dimensional data, and their recommendation results are only determined according to the prediction score, resulting in low recommendation accuracy. In view of this, a nursing teaching knowledge point recommendation method based on a SOM neural network and ranking factor decomposition machine is proposed. Firstly, the SOM neural network is used to cluster users based on users’ academic background information, then the partial order relationship of nursing teaching knowledge points is constructed by using users’ explicit and implicit web access behavior, and finally, the factor decomposition machine is used as the ranking function to classify users’ academic background web access behavior, borrowing nursing teaching introduction text, and other characteristic information were modeled, and the peer-to-peer ranking learning algorithm was used to accurately recommend nursing teaching knowledge points. Experimental results show that the proposed method can effectively alleviate the problem of data sparsity and improve the accuracy and efficiency of recommendations.

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Section: Visualization Of Key Knowledge Points Of Nursing Teaching Ma...mentioning

confidence: 99%

Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis

Fang

2022

Computational Intelligence and Neuroscience

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“…Multiple points of the AV system are outfitted with the suggested IDS to ensure the safety of all internal and external communications. The IDS can be mounted on top of the CAN bus, where it can process every transmitted message and check for compromised nodes [10]. This helps to detect attacks on the CAN bus and keep it secure.…”

Section: Methodsmentioning

confidence: 99%

“…Researchers have examined the security of the Internet of Things in contexts as varied as the smart home, public transit, healthcare, and wireless sensor networks. In [10], the authors analysed the security of Internet of Things (IoT) gadgets that use wireless protocols like WiFi, NFC, Bluetooth, and Zigbee. The authors of [16] conducted a survey focusing on Internet of Things network attacks.…”

Section: Introductionmentioning

confidence: 99%

Efficient Cyber Security Framework for IoT Using Machine Learning Algorithms

Urvashi Sangwan

2024

jes

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Through the network's infrastructure, the IoT can impart perception, recognition, and remote control to inanimate objects. Due to IoT's characteristics, it's possible to integrate the real world into a digital system, which improves precision, productivity, and bottom line. While traditional internet infrastructure comprises of highly capable computers and servers, IoT gadgets have limited processing power and storage space. The authentication and key agreement system SecureAuthKey is very lightweight. The suggested technique is meant to solve the security and privacy problems that plague modern constraint-based CPS programmes. A lightweight approach for authenticating cyber-physical objects is one of the expected outcomes. Adaptation and autonomy in cyber-physical systems need not be compromised by a lack of trust or security in users' data or the devices themselves, according to a new type of security algorithm. To provide flexibility and scalability, a new middleware module has been developed on the Raspberry platform to facilitate communication between CPS-based devices and services. Secure Internet of Things (IoT) evaluation methodology that may be used in a variety of contexts and is user-focused. With the suggested system, the two CPS units will be able to authenticate one another. When a network grows larger, the possibility of an attack rises. Therefore, the IoT network is much more susceptible to attacks than conventional networks. As the number of connected devices grows, so do the number of potential threats to their security. To protect the IoT ecosystem from current threats, cutting-edge technology is essential. The proposed approach must be very efficient and have low computational overheads. It creates random session keys to protect wireless transmissions. The system is protected from a variety of cyber threats. The current constraint-based CPS system requires a new lightweight security solution.

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“…This mainframe faces various types of problems like viruses, spyware, malware, phishing, spam, scams etc. A virus program damages a computer and replicates itself, usually resulting in data loss [1]. Spyware is a type of malicious code that monitors your Internet activity without your knowledge.…”

Section: Introductionmentioning

confidence: 99%

An efficient cyber security system based on flow-based anomaly detection using Artificial neural network

Hephzipah¹,

Vallem²,

Sheela

et al. 2023

MJCS

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Cyber security is developing factor for protecting internet resources by handing various monitoring feature based support to improve the security. Increasing internet cries in the defined facts for need of advance met in cyber security. Most internet attacker’s theft the information through malicious activities, false data injection, hacking and make soon creating procedures. In most cases cyber sercuity failed to detect the malicious activities because the monitoring feature analyses improper to predict the result in previous machine learning algorithms. TO resolve this problem to propose an advance cyber security based on flow-based anomaly detection using Min max game theory optimized artificial neural network (MMGT-ANN). The reprocessing was carried out with KDD crime dataset. Then Data driven network model is applied to monitor the feature margins and defect scaling rate. Based on the feature scaling rate Transmission Flow defect rate is estimated and applied with Min max Game theory to select the feature limits. Then features are trained with optimized ANN to detect the crime rate. By the attention of the proposed system achieves higher performance in precision rate to attain higher detection accuracy with lower time complexity compared to the other system.

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Quantifying the Significance and Relevance of Cyber-Security Text Through Textual Similarity and Cyber-Security Knowledge Graph

Cited by 16 publications

References 23 publications

Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis

Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis

Efficient Cyber Security Framework for IoT Using Machine Learning Algorithms

An efficient cyber security system based on flow-based anomaly detection using Artificial neural network

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