Security in Wireless Local Area Networks (WLANs)

Abstract

Major research domains in the WLAN security include: access control & data frame protection, lightweight authentication and secure handoff. Access control standard like IEEE 802.11i provides flexibility in user authentication but on the other hand fell prey to Denial of Service (DoS) attacks. For Protecting the data communication between two communicating devices-three standard protocols i.e., WEP (Wired Equivalent Privacy), TKIP (Temporal Key Integrity Protocol) and AES-CCMP (Advanced Encryption Standard-Counter mode with CBC-MAC protocol) are used. Out of these, AES-CCMP protocol is secure enough and mostly used in enterprises. In WLAN environment lightweight authentication is an asset, provided it also satisfies other security properties like protecting the authentication stream or token along with securing the transmitted message. CAPWAP (Control and Provisioning of Wireless Access Points), HOKEY (Hand Over Keying) and IEEE 802.11r are major protocols for executing the secure handoff. In WLANs, handoff should not only be performed within time limits as required by the real time applications but should also be used to transfer safely the keying material for further communication. In this chapter, a comparative study of the security mechanisms under the above-mentioned research domains is provided.

Computer and Network Security

“…Hence, at the time of handoff only 4-way handshake is required between STA and candidate AP. In this pre-authentication process, an inaccurate candidate AP prediction has associated resource wastage issues as full 802.1X will again be required [31]. Researchers have considered predictive authentication and proactive key distribution for reducing the handoff times.…”

“…Some of these approaches are as follows: (1) association rule mining [12][13][14], (2) fuzzy association rule mining [15], (3) artificial neural network [16][17][18], (4) support vector machines [19,20], (5) nearest neighbor [21], (6) hidden Markov model [22][23][24], (7) Kalman filter [25], (8) clustering [26], and (9) random forest [27,28]. Other machine learning methods have been proposed for learning the probability distribution of data and in applying statistical tests to detect outliers [29][30][31][32][33][34][35].…”