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Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. In this study, we have developed a complete prototype of Malicious URL Detection using machine learning methods. In particular, we have attempted an exact formulation of Malicious URL exposure from a machine learning perspective and proposed an approach using the AdaBoost algorithm -the proposed approach has brought forward more accuracy than other existing algorithms.

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Latest advancement in technologies has impacted the modern day teenagers both positively and negatively. One of the major impacts is the addiction to internet. The purpose of this research is to investigate and identify the various cyber security threats which may affect the teenagers and propose the countermeasures to support them and their parents on how to avoid or deal with the cyber security threats.Qualitative and quantitative methods have been used to analyze the internet usage patterns of the Emirati teenagers, the awareness that the Emirati teenagers have on the various cyber security threats and ways to deal with them.Recommendations were made based on the findings.

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