Density-based clustering and probabilistic classification for integrated transmission-distribution network security state prediction

Oladeji, Ifedayo; Makolo, Peter; Zamora, Ramon; Lie, Tek Tjing

doi:10.1016/j.epsr.2022.108164

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…Specifically, determine whether to prune by calculating the loss function before and after pruning the decision tree. Cost complexity pruning uses the cost complexity function to measure the complexity of the decision tree before and after pruning [10]. The cost complexity function can be expressed as:…”

Section:  mentioning

confidence: 99%

A method for identifying key features of network security situation based on decision tree algorithm

Liu,

2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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Section:  mentioning

confidence: 99%

A method for identifying key features of network security situation based on decision tree algorithm

Liu,

2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…The channel gain between client n and the server is a discrete random variable, following a distribution of

\Pr [\bar{h}_n]

[36–39]. The channel gain

h_n(j)

adheres to a discrete state block fading model.…”

Section: System Modelmentioning

confidence: 99%

Integration of online deep reinforcement learning and federated learning: A dynamic energy management strategy for industrial applications

Tang,

Du,

Long

et al. 2023

IET Communications

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In the context of federated learning, this paper focuses on managing inherent dynamics and uncertainties, and optimizing energy management for devices in real‐world industrial environments. The problem is formulated by proposing an online deep reinforcement learning algorithm that optimizes model iteration updates between clients and servers, and an energy harvesting strategy that enhances device performance and extends lifespan. Through these innovative methods, solutions are provided for the dynamics and uncertainties in federated learning and a new approach is devised for energy usage optimization and task scheduling. Numerical results demonstrate the efficiency of the approach in transitioning federated learning from theory to practical application, and offering diversified application scenarios. In summary, this research contributes to the development of federated learning by providing novel insights and methodologies, and highlights the challenges and opportunities for future studies in the field.

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“…Security is based on attack, which means security on a network that is built based on the threat of future attacks, and the attacks that are carried out. The best application of network security is prevention before an attack occurs, in this case, it certainly requires a lot of anticipation, and one of the most important is installing a security alarm [1], [2]. This security alarm is called the Intrusion Detection System (IDS).…”

Section: Introductionmentioning

confidence: 99%

Implementation of signature-based intrusion detection system using SNORT to prevent threats in network servers

2022

joscex

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Security is an important factor in today's digital era. In a network, implementing a security system is the focus of a network developer. One of the most basic network securities is in the form of access. To manage the security of a system must be known in advance who is involved in the system and what activities are carried out. Just like a security alarm, which monitors work conditions, this is the function of the Intrusion Detection System (IDS). IDS has several effective methods for detecting threats, one of which is the Signature-based method. IDS can be implemented through the open-source SNORT application, and the method works with rules which are commands to IDS to recognize various attacks. IDS rules will be included in the signature matching process, which means matching between rules and incoming attacks and views of both protocols, then the IDS will generate alerts that contain notifications. This study conducted a reading of the MIT-DARPA 1999 dataset on 1,252,412 packages and tested alerting with Network Scanning and DoS attacks. Analyze Package Data runs at a speed of 83,494 packets /second and gets a true positive percentage reaching 100% and an accuracy of 98.10%.

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Density-based clustering and probabilistic classification for integrated transmission-distribution network security state prediction

Cited by 9 publications

References 39 publications

A method for identifying key features of network security situation based on decision tree algorithm

A method for identifying key features of network security situation based on decision tree algorithm

Integration of online deep reinforcement learning and federated learning: A dynamic energy management strategy for industrial applications

Implementation of signature-based intrusion detection system using SNORT to prevent threats in network servers

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