CAJ | 학술논문

移动Ad hoc网络(MANET)是一种无线自组织网络，易受内部恶意节点攻击。针对由于网络内部攻击行为复杂而导致内部恶意节点不易识别的问题，提出一种基于模糊数学理论的 MANET 内部恶意节点识别方法。通过分析节点通信行为，建立由节点平均包转发延迟、转发率和丢包率组成的属性向量，利用最大隶属度原则进行分类识别。设置不同的仿真场景和恶意节点密度，采用NS2软件进行仿真实验，结果表明，该方法能识别多数内部恶意节点，虽然恶意节点密度对识别结果影响较大，但在恶意节点密度为30%的情况下，仍能保持96%以上的识别率和5%以下的误检率。
이동Ad hoc망락(MANET)시일충무선자조직망락，역수내부악의절점공격。침대유우망락내부공격행위복잡이도치내부악의절점불역식별적문제，제출일충기우모호수학이론적 MANET 내부악의절점식별방법。통과분석절점통신행위，건립유절점평균포전발연지、전발솔화주포솔조성적속성향량，이용최대대속도원칙진행분류식별。설치불동적방진장경화악의절점밀도，채용NS2연건진행방진실험，결과표명，해방법능식별다수내부악의절점，수연악의절점밀도대식별결과영향교대，단재악의절점밀도위30%적정황하，잉능보지96%이상적식별솔화5%이하적오검솔。
Mobile Ad hoc Network(MANET) is a wireless Ad hoc network, and it is vulnerable to be attacked by inside malicious nodes. For the complexity of inside attack behavior, the malicious nodes are difficult to be identified. In order to solve this problem, this paper presents a method of identifying inside malicious nodes based on fuzzy mathematics. By analyzing the node’s communication behavior, it finds an attribute vector which consists of node’s average packet forwarding delay, forwarding ratio and packet loss ratio, then classifies it using the principle of maximum membership grade. Experiment simulates on the NS2 software, and sets different simulation scenarios and malicious node density. The simulation results show that the moving speed of nodes has little impact on the recognition results, while the malicious density has larger impact. Even the malicious nodes are rather dense, reaching 30%, a high recognition ratio still maintains more 96%, and the false recognition ratio is less 5%.