article · International Journal of Computer Applications Technology and Research
Network security faces continuous threats from brute force attacks, where unauthorised users repeatedly attempt trial-and-error combinations to crack encrypted passwords or keys. Because brute force traffic closely resembles legitimate network activity, conventional perimeter-based defence mechanisms often struggle to identify and block it. A supervised deep learning model using a Convolutional Neural Network offers an effective approach for detecting Secure Shell brute force attacks. When benchmarked against five traditional machine learning algorithms, namely Naive Bayes, Logistic Regression, Decision Tree, k-Nearest Neighbour, and Support Vector Machine, the deep learning model demonstrated superior identification performance. Evaluated across standard assessment metrics, the model achieved an accuracy of 94.3 percent, a precision rate of 92.5 percent, a recall rate of 97.8 percent, and an F1-score of 91.8 percent, demonstrating high capability in recognising and isolating brute force intrusion attempts within network traffic.
Brute force intrusions pose severe risks to networked systems by systematically guessing credentials to gain unauthorised access. Because these attacks imitate legitimate traffic, standard security tools frequently miss them. Applying deep learning techniques enables automated, highly accurate detection of these intrusion attempts, providing network administrators and organisations with stronger defences to safeguard sensitive data and prevent unauthorised network access.
The research demonstrates an applied and tested detection model suitable for integration into intrusion detection and network security monitoring software. Organisations operating Secure Shell servers and internet-facing infrastructure could implement such deep learning algorithms to automate threat recognition. As the findings are based on comparative experimental testing, moving toward commercial deployment would require embedding the model into real-time operational network pipelines and assessing its processing overhead under live traffic conditions.
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The rising number of malicious threats on computer networks and Internet services owing to a large number of attacks makes the network security be at incessant risk. One of the predominant network attacks that poses distressing threats to networks security are the brute force attacks. A brute force attack uses a trial and error algorithm to decode encrypted data such as passwords or Data Encryption Standard keys, through exhaustive effort (using brute force) rather than using intellectual strategies. Brute force attacks resemble legitimate network traffic, making it difficult to defend an organization that rely mainly on perimeter-based security solutions a major challenge. For stopping the occurrence of such attacks, several curable steps must be taken. This paper proposes an efficient mechanism for SSH-Brute force network attacks detection based on a supervised deep learning algorithm, Convolutional Neural Network. The model performance was compared with experimental results from 5 classical machine learning algorithms including Naive Bayes, Logistic Regression, Decision Tree, k-Nearest Neighbour, and Support Vector Machine. Four standard metrics namely, Accuracy, Precision, Recall, and the F-measure were used. Results show that the CNN-based model is superior to the traditional machine learning methods with 94.3% accuracy, a precision rate of 92.5%, recall rate of 97.8% and F1-score of 91.8% in terms of the ability to detect SSH-Brute force attacks.
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DOI: 10.7753/ijcatr1001.1008
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