Cybersecurity refers to the organizational practices followed by the different multinational companies to defend their computers, servers, mobile devices, and networks from malicious attacks. This data exploitation is usually done by accessing, changing, or destroying sensitive information or hacking the data for money extortion. It applies to systems and mechanisms aimed at stopping unauthorized entry, bugs, and cybercriminal threats to devices, networks, and records. It does not matter how many technologies are emerging to make our life easy; humans are the main vulnerability in every sector. In this chapter, the authors discussed social engineering techniques: how we are being attacked by unknown threats with simple manipulative actions.
Using data analytics in the problem of Intrusion Detection and Prevention Systems (IDS/IPS) is a continuous research problem due to the evolutionary nature of the problem and the changes in major influencing factors. The main challenges in this area are designing rules that can predict malware in unknown territories and dealing with the complexity of the problem and the conflicting requirements regarding high accuracy of detection and high efficiency. In this scope, we evaluated the usage of state-of-the-art ensemble learning models in improving the performance and efficiency of IDS/IPS. We compared our approaches with other existing approaches using popular open-source datasets available in this area.
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