Event Title

IoT Botnet and Nw Intrusion Detect- CS 7999

Event Website

https://madhuridk7.wixsite.com/website

Document Type

Event

Start Date

3-12-2020 5:00 PM

Description

In the present-day world, there are different types of attacks being launched on computing devices. The world is experiencing more and more cyber-attacks, and the types of attacks are also increasing. For example, an IoT device in a home network can act as a botnet attacking other devices or there could be Man in the Middle attack. As time goes by more and more devices are being connected within any given network. All these devices will be vulnerable to attacks if any one of the devices is compromised within the network. This complicates Intrusion Detection in any given network. Manual detection and intervention are nearly impossible. Hence it is quintessential to detect different types of attacks with more confidence and with less computation complexity and time. In this research, the focus is on detecting intrusions including IoT botnet attacks and other types of network attacks. To achieve this, we built a multiclass classifier model using supervised learning algorithms along with the dimensionality reduction technique. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD dataset. In this study, we used a new dataset, the IoT network Intrusion Detection dataset.

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Dec 3rd, 5:00 PM

IoT Botnet and Nw Intrusion Detect- CS 7999

In the present-day world, there are different types of attacks being launched on computing devices. The world is experiencing more and more cyber-attacks, and the types of attacks are also increasing. For example, an IoT device in a home network can act as a botnet attacking other devices or there could be Man in the Middle attack. As time goes by more and more devices are being connected within any given network. All these devices will be vulnerable to attacks if any one of the devices is compromised within the network. This complicates Intrusion Detection in any given network. Manual detection and intervention are nearly impossible. Hence it is quintessential to detect different types of attacks with more confidence and with less computation complexity and time. In this research, the focus is on detecting intrusions including IoT botnet attacks and other types of network attacks. To achieve this, we built a multiclass classifier model using supervised learning algorithms along with the dimensionality reduction technique. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD dataset. In this study, we used a new dataset, the IoT network Intrusion Detection dataset.

https://digitalcommons.kennesaw.edu/cday/Fall/graduateresearch/2