Routers Sequential Comparing Two Sample Packets for Dropping Worms

Raja, Nageshwari; Babu, K. Narendra; Senthamaraiselvan, A.; Arulanandam, K.

doi:10.5815/ijcnis.2012.09.05

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Deep learning algorithms perform exceptionally well when a vast amount of information is available. As of 2016 [3], a decent general principle for supervised deep learning algorithms is to have at least 5,000 labelled samples per class. They will also outperform humans when trained with a dataset containing at least 10 million labelled samples.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity

Das,

et al. 2024

JMC

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Recently, we have noticed tremendous growth in the field of Information Technology. This increased growth has proliferated the use of new technologies and continued advancement of networking systems. These systems are widely adopted for real-time online and offline tasks. Due to this growth in information technology, maintaining security has gained huge attention as these systems are vulnerable to various attacks. In this context, an Intrusion Detection System (IDS) plays an important role in ensuring security by detecting and preventing suspicious activities within the network. However, as technology is overgrowing, malicious activities are also increasing. Moreover, legacy IDS methods cannot handle new threats, such as traditional signature-based methods requiring a predefined rule set to detect malicious activity. Also, several new methods have been proposed earlier to address security-related issues; however, the performance of these methods is limited due to poor attack detection accuracy and increased false positive rates. In this work, we propose and compare different deep-learning (DL) models that can be used to construct IDSs to provide network security. Details on convolutional neural networks (CNNs), Multilayer Perceptron (MLP), and long short-term memories (LSTMs) are introduced. A discussion of the outcomes achieved follows an assessment of the proposed DL model known as the FOA-CNN-LSTM technique. Comparisons are made between the suggested models and other machine-learning methods. This work presents a deep-learning approach based on hybrid CNN-LSTM with Fruit fly Optimization Algorithm (FOA) by ensemble techniques to distinguish between normal and abnormal behaviors.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity

Das,

et al. 2024

JMC

View full text Add to dashboard Cite

show abstract

Routers Sequential Comparing Two Sample Packets for Dropping Worms

Cited by 1 publication

References 8 publications

An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity

An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity

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