Name
Abstract | ||
|---|---|---|
In 802.11, all devices are uniquely identified by a Media Access Control (MAC) address. However, legitimate MAC addresses can be easily spoofed to launch various forms of attacks, such as Denial of Service attacks. Impersonating the MAC address of a legitimate user poses a big challenge for cyber crime investigators. Indeed, MAC spoofing makes the task of identifying the source of the attack very difficult. Sequence number analysis is a common technique used to detect MAC spoofing attack. Existing solutions relying on sequence number analysis, adopt a threshold-based approach where the gap between consecutive sequence numbers is compared to a threshold to decide the presence of a MAC spoofing attack. Nevertheless, threshold-based approach may lead to a high rate of false alerts due to lost or duplicated frames. To overcome the limitations of threshold-based approach, this paper proposes a detection method that relies on a machine learning approach, namely Artificial Neural Network (ANN). ANNs provide the potential to identify and classify network behavior from limited, noisy, incomplete and non-linear data sources. The experimentation results showed the effectiveness of the proposed detection technique. Moreover, we proposed a user-friendly graphical representation of information to support the interpretation of quantitative results. |
Year | Venue | Field |
|---|---|---|
2016 | ICDCN | Media access control,Protocol spoofing,Spoofing attack,Denial-of-service attack,Computer science,Computer security,IP address spoofing,MAC address,Computer network,ARP spoofing,MAC spoofing |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chafika Benzaid | 1 | 54 | 13.06 |
| Abderrahman Boulgheraif | 2 | 1 | 0.35 |
| Fatma Zohra Dahmane | 3 | 1 | 0.35 |
| Ameer Al-Nemrat | 4 | 42 | 9.12 |
| Khaled Zeraoulia | 5 | 1 | 0.69 |