Smart environments are worried about private keys and security problems. To prevent security attacks, which take benefit of a few of these intrusion detection systems (IDSs) and security vulnerabilities, were modeled. In the age of the Internet of Things (IoT), data generated by connected objects presents the fundamentals for big data analytics that could be used to recognize the anomalies and patterns in the data. Numerous cyber security systems use IDSs to detect intrusions that employ a variety of approaches and architectures. In this work, several research papers have been utilized based on diverse approaches used for detecting and preventing cyber security vulnerabilities. Moreover, this research work is reviewed by classifying them based on the detection approaches used in the preceding works. In addition, the research gaps and problems recognized in the previous works are stated; thus, the researchers can bring elucidation and improve their research in the future. The works in the literature are analyzed based on several features, such as performance evaluation measures, datasets utilized and approaches used.