Evaluation of Machine Learning Classification Techniques in IDS

ISBN-13: 9786204742229 / Angielski / Miękka / 100 str.

Amirhossein Abdi

Opis

With the rapid development of information technology, computer networks are exposed to a wide variety of vulnerabilities and an increasing number of security threats. Intrusive traffics affects the normal functionality of the network's operation. Thus, a need for more intelligent and sophisticated security controls such as intrusion detection systems (IDSs) is necessary. Since IDS has to deal with problems such as large network tra c volumes and di culty to realize decision boundaries between normal and abnormal behaviors, classification plays an important role in the detection. Machine learning approaches have been extensively used in network intrusion detection techniques because they require less human expert knowledge, significantly reduce the burden of analyzing huge volumes of network traffic, and provide more precise results by separating data into different classes (normal and abnormal) as correctly as possible with the help of a model. The experimental results indicate that using significant features instead of all features improved the performance of classification techniques based-IDS, and hence shows significant improvement in detecting the intrusions correctly.

O autorze

Abdi, AmirhosseinI graduated with a Master of computer science from UPM(Universiti Putra Malaysia) in 2020 and I have been working and furthering my knowledge in this field since 2010 when I got my first degree in computer software engineering. All the information in this book is based on my research over the past decade from valuable resources which are mentioned.