Accepted Papers

  • Denial-of-Service Attacks Against the 4-way Wi-Fi Handshake
    Mathy Vanhoef and Frank Piessen simec-DistriNet, KU Leuven

    client and Access Point (AP). We analyze this handshake, and discover several new denial-of- service (DoS) attacks against it. Interestingly, our attacks work even if Management Frame Pro- tection (MFP) is enabled. The rst attack abuses the observation that messages in the 4-way handshake undergo link-layer encryption once the pairwise key is installed. More precisely, when message 4 of the handshake is dropped, the handshake times out. The second attack is similar to the second one, but induces the AP into sending the rst message 4 with link-layer encryption. Again, this causes the handshake to time out. In the third attack, an adversary waits until the victim completes the 4-way handshake. Then she initiates a rekey by injecting a malformed 4-way handshake messages, causing several implementations to disconnect the client from the network. Finally, we propose countermeasures against our discovered attacks.

  • Identification of Sybil Attacks on Social Networks Using a Framework Based on User Interactions
    Hooman Asadian1, Hamid Haj Seyed Javadi2,1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran,2Department of Mathematics and Computer Science, Shahed University, Tehran, Iran

    The popularity of modern social networks has rendered social media platforms vulnerable to malicious activities. One such activity is a Sybil attack, in which a single entity emulates the behaviours of multiple users and attempts to create problems for other users and a network itself. This problem has prompted researchers to develop several techniques for preventing Sybil attacks, but in most cases, the efficiency assumptions that underlie proposed methods are not oriented toward reality. The current study puts forward an efficient framework for identifying Sybil attacks. The highly precise framework is underlain by rational assumptions and detects attacks on the basis of the structural characteristics of social networks and the social interactions among users. We evaluate our proposed framework using both synthetic and real world social network topologies. We show that SybilUncover is able to accurately identify high precision rate. Moreover, SybilUncover performs orders of magnitudes better than existing Sybil detection mechanisms.

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