![]() Internet landscape, catching zero-day exploits is a priority and it is not reasonable to wait until anĪttack signature has been discovered and uploaded. Signature detection accuracy is reliably high on knownĪttacks, however they can only detect attacks after they have been discovered. These systems are familiar to most users, they are anti-viruses that scan The "signatures" of recognized attacks, if a pattern lines up with one of these signatures, it isįlagged as an attack. Signature-based systems compare network traffic to Using signatures or by detecting anomalies. There are two ways that an IDS can detect these intrusions, by Īn Intrusion Detection System (IDS) is a system that monitors network traffic and flags potential Malware like ransomware is hard to defend against, as newer iterations are releasedĬonstantly, resulting in "zero-day" exploits that standard anti-viruses are not ready to catch. To the Internet, any network is taking on the risk of exposing itself to infiltration by nefariousĪctors. Vulnerabilities in network defenses, inadequate security policies, and lack of cybersecurityĮducation mean these adversaries have multiple avenues to attack most networks. As more and more dataīecomes Internet-facing, more lax and underfunded security systems are being exposed. Of the three credit bureaus in the United States, lost the personal data for at least 145 millionĪmericans, with more than 209,000 credit card numbers also lost. More than five billion dollars were lost in 2017, a 15,000% increase from 2015. The results also demonstrate how this benchmark can be used to create useful metrics for suchĪnomaly-based Detection, Intrusion Detection, BenchmarksĬyber security attacks are the most profitable they have ever been. Results show the differences in accuracy and performance between these Anomaly-based IDS solutions on The benchmark evaluation is performed on the popular NSL-KDD dataset. The algorithms include Naive Bayes, Support Vector Machines, Neural Networks, and K-meansĬlustering. We then use this benchmark to compareĪccuracy as well as the performance of four different Anomaly-based IDS solutions based on various MLĪlgorithms. We propose a benchmark that measures both accuracy and performance to produce objective metrics thatĬan be used in the evaluation of each algorithm implementation. However there is no standard benchmark to compare them based on quantifiable measures. Many proposed anomaly-based systems using different Machine Learning (ML) algorithms and techniques, University, Long Beach, Long Beach 90840, USA.Īnomaly-based Intrusion Detection Systems (IDS) have gained increased popularity over time. 10, No.5, September 2018ĭepartment of Computer Engineering and Computer Science, California State International Journal of Network Security & Its Applications (IJNSA) Vol. The results also demonstrate how this benchmark can be used to create useful metrics for such comparisons. The experimental results show the differences in accuracy and performance between these Anomaly-based IDS solutions on the dataset. The algorithms include Naive Bayes, Support Vector Machines, Neural Networks, and K-means Clustering. We then use this benchmark to compare accuracy as well as the performance of four different Anomaly-based IDS solutions based on various ML algorithms. In this paper, we propose a benchmark that measures both accuracy and performance to produce objective metrics that can be used in the evaluation of each algorithm implementation. There are many proposed anomaly-based systems using different Machine Learning (ML) algorithms and techniques, however there is no standard benchmark to compare them based on quantifiable measures. Anomaly-based Intrusion Detection Systems (IDS) have gained increased popularity over time.
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