Visible to the public Unauthorized Access Point Detection Using Machine Learning Algorithms for Information Protection

TitleUnauthorized Access Point Detection Using Machine Learning Algorithms for Information Protection
Publication TypeConference Paper
Year of Publication2018
AuthorsKim, D., Shin, D., Shin, D.
Conference Name2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
ISBN Number978-1-5386-4388-4
KeywordsClassification algorithms, computer network security, detection, hacking attacks, information protection, learning (artificial intelligence), machine learning, machine learning algorithms, Metrics, military facilities, multilayer perceptrons, Prediction algorithms, privacy, protection, pubcrawl, RTT value data, security, support vector machine, Support vector machines, threat vectors, unauthorized Access Point detection, unauthorized AP, Wi-Fi, wireless AP security, Wireless communication, Wireless fidelity, wireless integrated environment, wireless Internet environment, wireless LAN

With the frequent use of Wi-Fi and hotspots that provide a wireless Internet environment, awareness and threats to wireless AP (Access Point) security are steadily increasing. Especially when using unauthorized APs in company, government and military facilities, there is a high possibility of being subjected to various viruses and hacking attacks. It is necessary to detect unauthorized Aps for protection of information. In this paper, we use RTT (Round Trip Time) value data set to detect authorized and unauthorized APs in wired / wireless integrated environment, analyze them using machine learning algorithms including SVM (Support Vector Machine), C4.5, KNN (K Nearest Neighbors) and MLP (Multilayer Perceptron). Overall, KNN shows the highest accuracy.

Citation Keykim_unauthorized_2018