Intrusion Detection in the Digital Age: A Hybrid Data Optimization Perspective
Keywords:
Intrusion detection system, cyber-attack, threat, security, Machine learningAbstract
The ever-growing use of technology has resulted in a considerable rise in the total number of cyber threats and security breaches. Intrusion detection systems (IDSs) have become an crucial tool in combating these threats by detecting and preventing unauthorized access to computer systems and networks. In this research paper, we present a hybrid data optimization perspective on intrusion detection in the digital age. The importance of IDS cannot be overstated in the current digital landscape. With the increasing sophistication of cyber threats, traditional intrusion detection methods may prove insufficient. A hybrid approach that combines the strengths of multiple algorithms can lead to improved accuracy and reduced false alarms. In our research, we use a hybrid feature selection approach that combines genetic algorithms (GA) and random forest (RF) to choose the most important characteristics for the purpose of intrusion detection. The proposed hybrid approach to detecting intrusions has been shown to significantly improve the system's accuracy compared to the use of both RF and GA alone. We performed a comprehensive evaluation of the three algorithms, namely the SVM-RF, the support vector machine (SVM) and the random forest. Our research provides a valuable contribution to the field of intrusion detection by presenting a hybrid data optimization perspective that can significantly improve the accuracy of intrusion detection systems. This work can be used as a reference for future research in the area and can be applied in real-world intrusion detection systems to provide better protection against cyber threats.
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