As a tremendous amount of service being streamed online to their users along with massive digital privacy information transmitted in recent years, the internet has become the backbone of most people’s everyday workflow. Given this trend, our society is gradually transforming into a digital world. When living in this oncoming digital world, even the most sensitive data for corporations, vital personal information, or confidential reports from the government are all carried by the internet.
The extending usage of the internet, however, also expands the attack surface for cyberattacks. As an example, the implementation of IoT devices creates more access points within a local network. These access points can potentially be abused by attackers with malicious purposes to the network system that increases the risk of a cyber-attack. Therefore, if no effective protection mechanism is implemented, the internet will only be much vulnerable and this will raise the risk of data getting leaked or hacked.
The focus of this thesis is to propose an Intrusion Detection System (IDS) based on the Convolutional Neural Network (CNN) to reinforce the security of the internet. The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious. The Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS2017) dataset has been used to train and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. A comparative study of the proposed model’s performance against nine other well-known classifiers has been presented.
|Commitee:||Al Jufout, Saleh, Zhang, Wenlu|
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 82/8(E), Masters Abstracts International|
|Subjects:||Electrical engineering, Artificial intelligence, Computer science|
|Keywords:||CICIDS 2017, CNN, Cybersecurity, Deep learning, IDS|
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