TY - GEN

T1 - A Detection Mechanism for Internal Attacks on Pull-Based P2P Streaming Systems

AU - Ismail, H.

AU - Roos, S.

AU - Suri, Neeraj

PY - 2018/6/12

Y1 - 2018/6/12

N2 - Online streaming is a popular service for data-intensive applications such as video streaming. P2P-based streaming solutions are advocated to help reduce costs for both providers and users. Yet, involving users over data dissemination entails security risks including a variety of denial-of-service attacks. While extensive research exists on mitigating varied attack types, their effectiveness is limited if the attacker can infer information about the topology such as the identity of nodes that have direct connections to the source. The attacker can then leverage the gained insights to place malicious participants in prominent positions. By dropping chunks that should be forwarded, the malicious peers degrade the performance in a stealthy way that does not raise suspicion. We first demonstrate the feasibility of conducting such attacks. Accordingly, we propose a detection mechanism that identifies the attack and removes potential malicious peers from their disruptive positions. We ascertain, theoretically and through simulations, that malicious peers cannot misuse the detection mechanism to gain influence. Our simulation-based study indicates that the proposed detection mechanism is able to detect malicious peers with up to 80-90% accuracy while inducing a small overhead of approximately 8%. © 2018 IEEE.

AB - Online streaming is a popular service for data-intensive applications such as video streaming. P2P-based streaming solutions are advocated to help reduce costs for both providers and users. Yet, involving users over data dissemination entails security risks including a variety of denial-of-service attacks. While extensive research exists on mitigating varied attack types, their effectiveness is limited if the attacker can infer information about the topology such as the identity of nodes that have direct connections to the source. The attacker can then leverage the gained insights to place malicious participants in prominent positions. By dropping chunks that should be forwarded, the malicious peers degrade the performance in a stealthy way that does not raise suspicion. We first demonstrate the feasibility of conducting such attacks. Accordingly, we propose a detection mechanism that identifies the attack and removes potential malicious peers from their disruptive positions. We ascertain, theoretically and through simulations, that malicious peers cannot misuse the detection mechanism to gain influence. Our simulation-based study indicates that the proposed detection mechanism is able to detect malicious peers with up to 80-90% accuracy while inducing a small overhead of approximately 8%. © 2018 IEEE.

KW - Denial-of-service attack

KW - Network security

KW - Data dissemination

KW - Data-intensive application

KW - Detection mechanism

KW - Internal attacks

KW - Malicious participant

KW - Malicious peer

KW - P2p streaming systems

KW - Security risks

KW - Peer to peer networks

U2 - 10.1109/WoWMoM.2018.8449812

DO - 10.1109/WoWMoM.2018.8449812

M3 - Conference contribution/Paper

SN - 9781538647264

SP - 1

EP - 7

BT - 2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

PB - IEEE

ER -