A Survey Paper on Machine Learning Approaches to Intrusion Detection

This electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. For any nation, government, or cities to compete favorably in today's world, it must operate smart cities and e-government. As trendy as it may seem, it comes with its challenges, which is cyber-attacks. A lot of data is generated due to the communication of technologies involved and lots of data are produced from this interaction. Initial attacks aimed at cyber city were for destruction, this has changed dramatically into revenue generation and incentives. Cyber-attacks have become lucrative for criminals to attack financial institutions and cart away with billions of dollars, led to identity theft and many more cyber terror crimes. This puts an onus on government agencies to forestall the impact or this may eventually ground the economy. The dependence on cyber networked systems is impending and this has brought a rise in cyber threats, cyber criminals have become more inventive in their approach. This proposed dissertation discusses various security attacks classification and intrusion detection tools which can detect intrusion patterns and then forestall a break-in, thereby protecting the system from cyber criminals. This research seeks to discuss some Intrusion Detection Approaches to resolve challenges faced by cyber security and e-governments; it proffers some intrusion detection solutions to create cyber peace. It discusses how to leverage on big data analytics to curb security challenges emanating from internet of things. This survey paper discusses machine learning approaches to efficient intrusion detection model using big data analytic technology to enhance computer cyber security systems.