Computer networks have been experienced ever-increasing growth since they play a critical role in different aspects of human life. Regarding the vulnerabilities of computer networks, they should be monitored regularly to detect intrusions and attacks by using high-performance Intrusion Detection Systems (IDSs). IDSs try to differentiate between normal and abnormal behaviors to recognize intrusions. Due to the complex behavior of malicious entities, it is crucially important to adopt machine learning methods for intrusion detection with a fine performance and low time complexity. Boosting approach is considered as a way to deal with this challenge. In this paper, we prepare a clear summary of the latest progress in the context of intrusion detection methods, present a technical background on boosting, and demonstrate the ability of the three well-known boosting algorithms (Real Adaboost, Gentle Adaboost, and Modest Adaboost) as IDSs by using five IDS public benchmark datasets. The results show that the Modest AdaBoost has a higher error rate compared to Gentle and Real AdaBoost in IDSs. Besides, in the case of IDSs, Gentle and Real AdaBoost show the same performance as they have about 70% lower error rates compared to Modest Adaboost, however, Modest AdaBoost is about 7% faster than them. In addition, as IDSs need to retrain the model frequently, the results show that Modest AdaBoost has a much lower performance than Gentle and Real AdaBoost in case of error rate stability.

由于计算机网络在人类生活的各个方面都起着至关重要的作用，因此经历了不断增长的增长。关于计算机网络的漏洞，应定期监视它们，以使用高性能入侵检测系统（IDS）来检测入侵和攻击。IDS试图区分正常和异常行为，以识别入侵。由于恶意实体的行为复杂，因此采用机器学习方法进行入侵检测具有良好的性能和较低的时间复杂度至关重要。加强方法被认为是应对这一挑战的一种方式。在本文中，我们对入侵检测方法的最新进展进行了清晰的总结，并提出了增强技术的技术背景，并通过使用五个IDS公共基准数据集展示了三种著名的增强算法（Real Adaboost，Gentle Adaboost和Modest Adaboost）作为IDS的功能。结果表明，与IDS中的Gentle和Real AdaBoost相比，谦虚AdaBoost的错误率更高。此外，在IDS方面，Gentle和Real AdaBoost的性能相同，因为它们的错误率比Modest Adaboost低约70％，但是Modest AdaBoost的错误率比它们快约7％。另外，由于IDS需要频繁地重新训练模型，结果表明，在错误率稳定的情况下，Modest AdaBoost的性能比Gentle和Real AdaBoost的性能低得多。结果表明，与IDS中的Gentle和Real AdaBoost相比，谦虚AdaBoost的错误率更高。此外，在IDS方面，Gentle和Real AdaBoost的性能相同，因为它们的错误率比Modest Adaboost低约70％，但是Modest AdaBoost的错误率比它们快约7％。另外，由于IDS需要频繁地重新训练模型，结果表明，在错误率稳定的情况下，Modest AdaBoost的性能比Gentle和Real AdaBoost的性能低得多。结果表明，与IDS中的Gentle和Real AdaBoost相比，谦虚AdaBoost的错误率更高。此外，在IDS方面，Gentle和Real AdaBoost的性能相同，因为它们的错误率比Modest Adaboost低约70％，但是Modest AdaBoost的错误率比它们快约7％。另外，由于IDS需要频繁地重新训练模型，结果表明，在错误率稳定的情况下，Modest AdaBoost的性能比Gentle和Real AdaBoost的性能低得多。