An Improved SVM-based Framework for Unmanned Swarm Network Intrusion Detection
The heterogeneous communication network between distributed unmanned swarms possesses such characteristics as complex structure,large coverage area,and high data traffic.However,the insufficient data throughput of traditional network in-trusion detection frameworks has limited the development of anti-intrusion and anti-loss-of-control capabilities of unmanned swarms.In light of this,this study proposes an improved network intrusion detection framework for unmanned clusters based on SVM,of which improvements of the support vector machine algorithm by adopting the pipeline can not only meet the requirements of ensemble learning,but also realize the detection and classification of network intrusion.The improvements of pipeline have bet-ter adaptability to the distributed unmanned integration framework,and it can rely on the Spark streaming framework to adapt to the rapid calculation and processing requirements of large-scale communication data.Through experimental evaluation of the im-proved framework,the results show that it possesses excellent performance in terms of accuracy,real-time response,and other as-pects,effectively supporting the need for intrusion classification detection of large-scale data in unmanned swarms.
NETL
NSTL
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