Arp Attack Detection Software Poisoning and Sniffers in WLAN Networks Implementing Supervised Machine Learning

摘要

Nowadays, the growing number of mobile device users such as tablets and smart phones, has shown an increase of wireless network usage (Wi-Fi). At the same time, the number of attacks against this network has been growing too, taking advantage of vulnerabilities typical of protocols such as ARP and 802.11 as shown in a study done by Verizon on social network attacks. The proposal is to create a tool capable of detecting man in the middle attacks such as ARP poisoning/spoofing and network sniffers that use NICs in monitor mode. A machine learning algorithm is then generated which is trained with data from networks being attacked or neutral to later be able to classify incoming network data and catalog them as an attack alert or not.