MUSH: Multi-Stimuli Hawkes Process Based Sybil Attacker Detector for User-Review Social Networks

Qu, Zheng; Lyu, Chen; Chi, Chi‐Hung

doi:10.1109/tnsm.2022.3186513

Cited by 8 publications

(2 citation statements)

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“…Hawkes processes, as a type of self-exciting point process, have found increasingly widespread applications in the field of network attack modeling, particularly in financial security and social network security [23][24][25][26]. Dutta et al [23] developed a novel classifier named HawkesEye, which utilizes time windows for prediction.…”

Section: Hawkes Process For Modeling Network Attackmentioning

confidence: 99%

“…This classifier combines Hawkes processes with specific topic models for analyses based on time and textual information. Qu et al [24] proposed a social network witch attack detection model based on multi-stimulus Hawkes processes. This model simulates other users' endorsement behaviors toward a particular user through external stimuli and reflects the user's own reposting and commenting behaviors through internal stimuli, effectively identifying witch attacks in social networks.…”

Section: Hawkes Process For Modeling Network Attackmentioning

confidence: 99%

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HP-LSTM: Hawkes Process–LSTM-Based Detection of DDoS Attack for In-Vehicle Network

Li,

Liu

2024

Future Internet

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Connected and autonomous vehicles (CAVs) are advancing at a fast speed with the improvement of the automotive industry, which opens up new possibilities for different attacks. A Distributed Denial-of-Service (DDoS) attacker floods the in-vehicle network with fake messages, resulting in the failure of driving assistance systems and impairment of vehicle control functionalities, seriously disrupting the normal operation of the vehicle. In this paper, we propose a novel DDoS attack detection method for in-vehicle Ethernet Scalable service-Oriented Middleware over IP (SOME/IP), which integrates the Hawkes process with Long Short-Term Memory networks (LSTMs) to capture the dynamic behavioral features of the attacker. Specifically, we employ the Hawkes process to capture features of the DDoS attack, with its parameters reflecting the dynamism and self-exciting properties of the attack events. Subsequently, we propose a novel deep learning network structure, an HP-LSTM block, inspired by the Hawkes process, while employing a residual attention block to enhance the model’s detection efficiency and accuracy. Additionally, due to the scarcity of publicly available datasets for SOME/IP, we employed a mature SOME/IP generator to create a dataset for evaluating the validity of the proposed detection model. Finally, extensive experiments were conducted to demonstrate the effectiveness of the proposed DDoS attack detection method.

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