Context-aware Connectivity Analysis Method using Context Data Prediction Model in Delay Tolerant Networks

Jeong, Rae-Jin; Oh, Young-Jun; Lee, Kang-whan

doi:10.6109/jkiice.2015.19.4.1009

Cited by 2 publications

(3 citation statements)

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“…A Hard-Voting model determines the predicted value of the class that receives the highest number of votes among the outcomes of each classifier model using majority classification. On the other hand, a Soft-Voting model selects the class with the highest mean probability among all classifier predictions.The user's text is " [7]". We utilised the Soft-Voting model for the proposed concept.…”

Section: Equation 1 Cosine Similarity Formulamentioning

confidence: 99%

“…5,6,7,8,9 shows each algorithm's BLEU and Cosine Similarity Score scores according to the number of test data sets. Figure4is BLEU Score and Cosine Similarity Score Graph about S-BEL Mixture Model.…”

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confidence: 99%

“…Figures 4,5,6,7,8,and 9 show that the evaluation score differs depending on the number of data sets. Since the text of the correct answer heavily influences BLEU and Cosine Similarity Scores, the accuracy decreases when an answer is generated by using a word that is not in the correct answer in the test data set.…”

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confidence: 99%

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Enhancing Security Metrics of Machine Learning Models and TOR against Malicious Attacks

T. Gayathri

2023

IJRITCC

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With the increasing sophistication of cyber attacks, it has become essential for network security professionals to develop robust and effective methods for detecting BOT attacks. Machine learning algorithms have emerged as a promising solution in this regard, offering the ability to learn from data and detect patterns that may be indicative of malicious activity. Onion routing is a technique which protects internet user from the malicious attacks. Due to its slow performance in multi-layer of encryption and data can be routed to several servers. In our research we can combine onion routing with machine learning algorithms. In this paper, we analysed the performance of several popular machine learning algorithms, including Random Forest, Support Vector Machine (SVM), Neural Network Stochastic Gradient Descent, Neural Network Limited-memory Broyden-Fletcher-Goldfarb-Shanno, Neural Network adam, K-means Clustering, Hidden Markov Models (HMM), and Logistic Regression, for detecting BOT attacks in a network and portable executable file (PE File) modification. We begin by providing an overview of BOT attacks , PE file modification and the different machine learning algorithms used for detecting them. Next, we explore the performance metrics used to evaluate these algorithms and compare their performance based on these metrics. Finally, we identify the algorithm(s) that perform better in detecting BOT attacks and compared with initial model and resilient model of PE file. By the end of this paper, readers will have a comprehensive understanding of the strengths and weaknesses of different machine learning algorithms for detecting BOT attacks in a network.

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Section: Equation 1 Cosine Similarity Formulamentioning

confidence: 99%

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confidence: 99%