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

  • Nicolas Ricardo Enciso
  • Octavio José Salcedo ParraEmail author
  • Erika Upegui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11005)


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.


Supervised machine learning MITM attacks ARP table NIC’s monitor mode Packages sniffers 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nicolas Ricardo Enciso
    • 1
  • Octavio José Salcedo Parra
    • 1
    • 2
    Email author
  • Erika Upegui
    • 3
  1. 1.Department of Systems and Industrial Engineering, Faculty of EngineeringUniversidad Nacional de ColombiaBogotá D.C.Colombia
  2. 2.Faculty of Engineering, Intelligent Internet Research GroupUniversidad Distrital “Francisco José de Caldas”Bogotá D.C.Colombia
  3. 3.Faculty of Engineering, GRSS-IEEE/UD & GEFEM Research GroupUniversidad Distrital “Francisco José de Caldas”Bogotá D.C.Colombia

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