Defending Against DDoS Attacks via Heterogeneous Data Conflict Resolution for Secure Services in Internet of Things

Yang Xiao, Jiakui Xiang, Bowen Zhao, Cheng Qiao, Muhammad Shafiq, Qingqi Pei, Mohammad Mahtab Alam

Abstract: With the promising development of Internet of Things(IoT), many rich services configured in IoT devices exhibit high responsiveness to users' personalized requirements. However, recent studies have shown that distributed Denial of Service (DDoS) attacks pose a significant threat to IoT service via flooding the Internet Protocol (IP) and excessively consuming bandwidth, finally renders network services inaccessible. Machine learning-based approaches to mitigate DDoS attacks often depend on existing databases of DDoS incidents, yet the construction of such databases is non-trivial. Furthermore, solutions grounded in statistical analysis frequently encounter performance limitations. While the Economical Intelligent DDoS Demotivation (EID) algorithm, introduced in a study published at CCS'21, demonstrates effective defense against DDoS attacks by analyzing discrepancies in traffic patterns across different network nodes, it is marred by a high false positive rate, which can severely impact the quality of network services. To address these challenges, this paper introduces CRH4DDoS, a novel DDoS defense mechanism that employs conflict resolution strategies for the integration of heterogeneous data. Our proposed CRH4DDoS conceptualizes network traffic as heterogeneous data, framing the identification of malicious traffic as a conflict resolution problem. This approach does not necessitate a pre-existing DDoS database, achieves superior performance metrics, and markedly diminishes the false positive rate. Empirical assessments conducted on our real-world network platform reveal that the proposed CRH4DDoS is capable of defending against a variety of hybrid and dynamic DDoS attacks, occurring within seconds, with a false positive rate of merely 0.73%.

External IDs:doi:10.1109/tetci.2025.3595575