A Biologically-Inspired Approach to Network Traffic Classification for Resource-Constrained Systems

Schmidt, Brian; Kountanis, Dionysios; Al-Fuqaha, Ala

doi:10.1109/bdc.2014.13

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Using linear kernel, the performance achieved was 92.3% of accuracy. Other two algorithms such as Naïve Bayes and SVM achieved an accuracy of 82.2% and 44.10% respectively [18]. Imbalance nature of the dataset used for the experimentation affects the algorithm proposed for the few classes as stated in [19].…”

Section: Related Workmentioning

confidence: 99%

Multilayer Feedforward Neural Network for Internet Traffic Classification

Manju¹,

Harish²,

Nagadarshan³

2020

IJIMAI

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Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard).

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Section: Related Workmentioning

confidence: 99%

Multilayer Feedforward Neural Network for Internet Traffic Classification

Manju¹,

Harish²,

Nagadarshan³

2020

IJIMAI

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A bio-inspired secure IPv6 communication protocol for Internet of Things

Saleem

Chaudhry

Orgun

et al. 2017

2017 Eleventh International Conference on Sensing Technology (ICST)

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How Artificial Intelligence Can Help Accounting in Information Management

Rosário

2024

Artificial Intelligence Approaches to Sustainable Accounting

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Artificial intelligence can greatly assist accounting professionals in information management, streamlining processes, and improving overall efficiency. AI-powered software can automate data entry tasks by extracting relevant information from documents, such as invoices, receipts, and bank statements. AI algorithms can analyze financial data and identify patterns or anomalies indicative of fraud or financial irregularities. AI can analyze historical financial data and market trends to provide predictive insights, helping businesses anticipate future financial challenges and opportunities. Human accountants will continue to play a vital role in interpreting AI-generated insights, making strategic decisions, and maintaining a human touch in client interactions. Based on the above, it is intended to systematically review the bibliometric literature on how artificial intelligence can help accounting in information management using the Scopus database to analyse 77 academic and/or scientific documents

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A Biologically-Inspired Approach to Network Traffic Classification for Resource-Constrained Systems

Cited by 3 publications

References 16 publications

Multilayer Feedforward Neural Network for Internet Traffic Classification

Multilayer Feedforward Neural Network for Internet Traffic Classification

A bio-inspired secure IPv6 communication protocol for Internet of Things

How Artificial Intelligence Can Help Accounting in Information Management

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