As we continue to connect more devices to the internet, such as phones, IoT appliances, and vehicles, concerns about security are growing. The increasing number of devices also means an increased risk of attacks, which are becoming more severe as these devices handle essential tasks. To combat network attacks, a combination of systems is typically employed.
The first line of defense is a firewall, which helps to prevent attacks from infiltrating the network. However, if the firewall fails, detection systems come into play. These systems are designed to identify and halt attacks in progress. Additionally, they keep records of the attacks to enhance future security measures.
While these detection systems are crucial to network security, they do have a significant limitation. They primarily rely on known attack patterns, which means they may not recognize new types of attacks. To address this issue, researchers are exploring more innovative systems that can learn to identify regular network activity.
The goal of this project is to develop a tool that utilizes machine learning and deep learning to determine whether network traffic is safe or potentially dangerous. By training the system to recognize new, unusual patterns, it can effectively identify and respond to emerging attack methods.
By creating a more intelligent system that goes beyond known attack patterns, we can enhance network security and stay one step ahead of cyber threats. This research project aims to contribute to the development of advanced tools that can effectively detect and mitigate new network attacks.
In conclusion, as the number of connected devices continues to rise, it is crucial to develop innovative solutions to protect network security. The use of machine learning and deep learning techniques holds great promise in creating intelligent systems that can recognize new network attacks and safeguard our digital infrastructure.