SSH-Brute Force Attack Detection Model based on Deep Learning

Wanjau, Stephen Kahara; Wambugu, Geoffrey Mariga; Kamau, Gabriel

doi:10.7753/ijcatr1001.1008

Cited by 18 publications

(11 citation statements)

References 18 publications

(33 reference statements)

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“…This paper first introduces CNN and NB classification algorithm, then analyzes the TEA based on CNN and NB; Finally, through experiments, we compared the emotion classification results of the word vector model randomly constructed based on CNN and the emotion classification results based on CNN combined with Word2vec word vector and NB classification algorithm. The experimental results show that the TEA model designed in this paper based on CNN and NB has finally obtained the best experimental results, so it also proves the feasibility of this method [3][4].…”

Section: Introductionsupporting

confidence: 55%

Text Emotion Analysis based on Convolutional Neural Network and Naive Bayes

2021

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In the era of Internet big data, how to preprocess non-standard text data and obtain effective classification features from it has an important impact on text emotion analysis(TEA). In the work of emotion analysis, text data preprocessing and emotion feature acquisition are the basis of emotion analysis task. For this reason, this paper proposes convolutional neural network(CNN) and NB algorithm technology to study TEA. Firstly, it introduces the deep learning based cyclic neural network model and NB algorithm technology, constructs a TEA model based on CNN and Naive Bayes(NB), and then designs the emotion analysis model according to the process of text evaluation object extraction; Finally, taking microblog as the research object, by comparing the experimental results of CRF model of machine learning, model based on cyclic neural network and model based on CNN and NBian model proposed in this paper, it is found that the emotion classification model designed in this paper combined with the model of Word2vec word vector has obtained the best experimental results, which proves the feasibility of this method.

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Section: Introductionsupporting

confidence: 55%

Text Emotion Analysis based on Convolutional Neural Network and Naive Bayes

2021

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“…The most significant risk is the chosen plain-text problem, in which the attackers [30] can encrypt parts of plain-text. Brute-force is the eventual attack on the cipher, testing all the possible keys until the right one is encountered [3] uses trial and error algorithm [31]. Various ways were researched and developed for limiting or mitigating brute-force attacks [32]- [36].…”

Section: Testing and Evaluating The Resultsmentioning

confidence: 99%

Cryptography based on retina information

Alrifaee

Ismaeel

2022

IJEECS

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The security of message information has drawn more attention nowadays, so; cryptography has been used extensively. This research aims to generate secured cipher keys from retina information to increase the level of security. The proposed technique utilizes cryptography based on retina information. The main contribution is the original procedure used to generate three types of keys in one system from the retina vessel's end position and improve the technique of three systems, each with one key. The distances between the center of the diagonals of the retina image and the retina vessel's end (diagonal center-end (DCE)) represent the first key. The distances between the center of the radius of the retina and the retina vessel's end (radius center-end (RCE)) represent the second key. While the diagonal-radius center and the retina vessel's end (diagonal-radius center-end (DRCE)) represent the third key. The results illustrate the process's validity and applicability. Also, improve the time required to decrypt the cipher-text by a brute force attack (BFA) from (4.358e+139) year in the compared technique to (1.3074e+140) year for retina3. The BFA time will increase with increasing the number of retina vessels, as in retina1, 2, and 3, which have 24, 53, and 103 retina vessels.

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“…In the paper [12], Kahara Wanjau et al The research presents a very effective approach for detecting SSH-brute force network attacks using a supervised deep learning method called Convolutional Neural Network (CNN). The CNN-based model outperforms existing machine learning approaches in detecting SSH brute force assaults.…”

Section: Related Workmentioning

confidence: 99%

Detecting Brute Force Attacks on SSH and FTP Protocol Using Machine Learning: A Survey

Ali Hamza,

Urayh Al-Janabi

2024

J. Al-Qadisiyah Comp. Sci. Math.

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The significance of detecting network traffic anomalies in cybersecurity cannot be overstated, especially given the increasing frequency and complexity of computer network attacks. As new Internet-related technologies emerge, so do more intricate attacks. One particularly daunting challenge is represented by dictionary-based brute-force attacks, which require effective real- time detection and mitigation methods. In this paper, we investigate Secure Shell or Secure Socket Shell, is a network protocol that gives users, particularly system administrators, a secure way to access a computer over an unsecured network(SSH)and File Transfer Protocol is a standard network protocol used for the transfer of files from one host to another over a TCP-based network, such as the Internet (FTP) brute-force attack detection by using Our research focuses on using the machine learning approach to detect SSH and FTP brute-force attacks. A reasonably thorough investigation of machine learners' efficacy in identifying brute force assaults on SSH and FTP is made possible by employing several classifiers. Brute-force assaults are a popular and risky method of obtaining usernames and passwords. Applying ethical hacking is an excellent technique to examine the effects of a brute-force assault. This article discusses many defense strategies and approaches to using brute-force assaults. The pros and cons of several defense strategies are enumerated, along with information on which kind of assault is easiest to identify. we made use of machine learning classifiers: Naive Bayes, Random Forest, Logistic Regression, we determined that the Random Forest algorithm achieved the highest level with an accuracy the contribution lies in demonstrating the feasibility of training and evaluating basic Random Forest models with two independent variables to classify CSE-CIC-IDS2018 dataset.

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SSH-Brute Force Attack Detection Model based on Deep Learning

Cited by 18 publications

References 18 publications

Text Emotion Analysis based on Convolutional Neural Network and Naive Bayes

Text Emotion Analysis based on Convolutional Neural Network and Naive Bayes

Cryptography based on retina information

Detecting Brute Force Attacks on SSH and FTP Protocol Using Machine Learning: A Survey

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