An Environmental Intrusion Detection Technology Based on WiFi

Zhu, Xianxun; Xu, Hongxuan; Zhao, Zhiyang; Wang, Xu; Xiong, Wei; Zhang, Yang; Zuo, Jiancun

doi:10.1007/s11277-021-08288-4

Cited by 29 publications

(21 citation statements)

References 12 publications

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“…Zhu et al studied the problem of maximizing the computing rate of binary computing with wireless power supply. This paper analyzes the energy delay dynamic shunting balance of mobile edge computing technology with energy collection devices and proposes an incentive compatible auction mechanism for resource transactions between mobile devices and cloud base stations to stimulate binary computing shunting [9]. Wen-He et al proposed a strategic computing diversion algorithm based on the deep Q network, which is used to learn the optimal binary computing diversion strategy in a superdense network [10].…”

Section: Related Workmentioning

confidence: 99%

Certificateless Batch Authentication Scheme and Intrusion Detection Model Based on the Mobile Edge Computing Technology NDN-IoT Environment

Sun

2022

Journal of Function Spaces

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With the rapid development of mobile communication technology, the data transmission rate of mobile communication has been significantly improved and emerging Internet of things applications need a lot of computing resources to meet their own computing needs. However, the existing intelligent terminals have limited computing power and cannot meet the low-latency computing requirements with limited energy consumption. Mobile edge computing technology is considered to be one of the technical solutions that can efficiently solve this problem. Through mobile edge computing technology, intelligent terminals can actively divert some computing tasks to the computing servers deployed in edge network nodes and return the computing results to intelligent terminals after the edge computing servers have finished computing, thus greatly reducing the computing delay of users. In this paper, aiming at multiple edge server networks, combined with dual-connection technology, based on the research of a single edge computing server network, it is further extended to two edge computing servers and a joint optimization problem of computing task allocation and security performance requirements of intelligent terminals and edge computing servers is proposed, so as to minimize the global time delay for completing the computing tasks of intelligent terminals. By layering the problem, this paper proposes a corresponding algorithm, which can efficiently obtain the optimal solution of the initial problem. Compared with the linear search algorithm, the algorithm proposed in this paper can significantly reduce the computation time.

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Section: Related Workmentioning

confidence: 99%

Certificateless Batch Authentication Scheme and Intrusion Detection Model Based on the Mobile Edge Computing Technology NDN-IoT Environment

Sun

2022

Journal of Function Spaces

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“…This starts with a timestamp of when the transaction occurs and is recorded to the server by taking a hash along with other associated items such as IP address etc. This information is encrypted so the actual person making the transaction will not be at risk with having their personal information exposed [11]. This shows that the data has to have existed in order for the hash to have been created.…”

Section: Theory Of Algorithm/ Methodologymentioning

confidence: 99%

Proof of Work vs. Proof of Stake in Cryptocurrency

Lin¹

2023

HSET

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This research document demonstrates the understanding of two key elements: Proof of Work (PoW) and Proof of Stake (PoS) within cryptocurrency. Cryptocurrency is often misunderstood as just volatile and risky but many investors do not understand what it even is. What is proof of work (BTC)? What is proof of stake (ETH)? How are they similar and how are they different? Cryptocurrency is actually more relatable that originally imagined once a person understands digital currency, money, it’s origins, and how they hold similar monetary value within their own wallets. Cryptocurrency in PoW involves complicated mathematical equation solving via mining from a large growing pool of miners. These miners receive rewards such as bitcoin/tokens/etc. from their mined hash blocks verified, and blocks added to the blockchain. Challenges are that there are many miners and there may be a computational limit per core of each PC to be able to mine since Bitcoin has increased its difficulty 70 billion times since it started. BTC or Bitcoin is still widely trusted due to its increasing difficulty to race to the finish line to finish the advanced math computations. Whereas within PoS this is not a race between the masses and advanced math computations, but a validator that generally has more put in their stake vs. gained by receiving the processing fees associated. ETH or Ethereum has shifted from PoW to PoS for its better energy efficiency in resources. It has employed many other additional checks from Casper to Gasper which is the combination of Casper (fork-choice algorithm), LMD-GHOST (heavest observed subtree), and finality which requires 2/3rd agreement as well as once a block is justified it is upgraded to a finalized block. the similarities between the two PoW and PoS are that they are consensus-driven algorithms. They are designed to reach an agreement between the systems in place before each block is placed in the blockchain. Additionally, they also have a shared public ledger that operates on a global if not international scale. With blockchains, there is always only one true version and this is relying on a network rather than a governing authority like a bank/government entity to provide security and to prevent fraudulent transactions. It is still widely contested which is better than these two well-known consensus algorithms and it is still widely contested. The best answer is suited on a per investor per business basis in terms of the willingness of taking a risk just like any investment. This document serves to help provide guidance in the understanding of cryptocurrency of two main consensus algorithms, its similarities, differences, and help identify see what a potential investor the reader may be.

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“…As a result, a matrix may be used to represent the STBO population, and a vector may be used to characterize each STBO individual. The STBO population is determined in a matrix as per Equation (12).…”

Section: Stbo Mathematical Modelmentioning

confidence: 99%

“…Three categories of detection methods have been established: hybrid-based detection, anomaly-based detection, and signature-based detection. 12 The anomaly detection computes the current network traffic status using the benchmark of typical traffic behavior as a main basis. If it deviates from the accepted standard, the network administrator is notified that an attack has occurred.…”

mentioning

confidence: 99%

Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process

Chandan,

Santhi,

Srinivasa Rao

2024

Concurrency and Computation

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SummaryAttacks have a significant negative impact on the wireless sensor network's network operations. They target to weaken the network layer, if they are not completely eradicated, the networks' ability to execute their desired function becomes collapsed. While intended to monitor such unexpected attacks, anomaly‐based intrusion detection systems (AIDS) have a high risk of false positives. This article intends to propose a Deep Ensemble Intrusion detection with a Self‐adaptive Sewing Training‐Based Optimization (DIED‐SASTO) model that includes the following steps: (a) preprocessing, (b) feature extraction, (c) optimal feature selection, and (d) detection. Initially, an improved class imbalance processing will take place to solve the imbalance problem during the preprocessing step. Subsequently, the features are extracted including higher‐order statistical features, improved entropy, and correlation‐based features during the feature extraction step. From the extracted feature set, as the curse of dimensionality becomes a serious challenge, it is significant to choose the optimal features. In the optimal feature selection process, a new Self‐adaptive Sewing Training‐Based Optimization (SASTO) is used. In addition, the intrusion detection will be employed based on the selected optimal features and trained using deep ensemble models like Deep maxout, Deep Belief Network (DBN), and Bidirectional Long short‐term memory (Bi‐LSTM). Once the presence of an attack is detected, it is mandatory to mitigate the attacker node form the network. For Attack Mitigation, improved entropy will be progressed. The effectiveness of the DIED‐SASTO is evaluated over conventional methods in terms of various metrics.

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An Environmental Intrusion Detection Technology Based on WiFi

Cited by 29 publications

References 12 publications

Certificateless Batch Authentication Scheme and Intrusion Detection Model Based on the Mobile Edge Computing Technology NDN-IoT Environment

Certificateless Batch Authentication Scheme and Intrusion Detection Model Based on the Mobile Edge Computing Technology NDN-IoT Environment

Proof of Work vs. Proof of Stake in Cryptocurrency

Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process

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