【摘要】：研究點(diǎn)對點(diǎn)網(wǎng)絡(luò)入侵檢測優(yōu)化問題。點(diǎn)對點(diǎn)網(wǎng)絡(luò)是一種多跳的、無中心的、自組織無線網(wǎng)絡(luò),其主機(jī)經(jīng)常根據(jù)需要移動(dòng),主機(jī)的移動(dòng)會使網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)不斷發(fā)生變化,而且變化的方式和速度都是不可預(yù)測的,這給網(wǎng)絡(luò)入侵檢測帶來了困難。傳統(tǒng)的檢測方法針對網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)穩(wěn)定的網(wǎng)絡(luò)效果很好,對于自組織的不可預(yù)測的點(diǎn)對點(diǎn)網(wǎng)絡(luò)人侵檢測準(zhǔn)確性不高。為了提高檢測能力和準(zhǔn)確度,提出了改進(jìn)ART2的入侵檢測方法(SART)。當(dāng)人工神經(jīng)網(wǎng)絡(luò)中所存儲的模式量較大時(shí),可對學(xué)習(xí)所得模式進(jìn)行有效組織進(jìn)而提高檢測效率,通過調(diào)節(jié)幅度與相位的判斷條件線性組合來縮小聚類之間的大小差異。仿真結(jié)果表明,相比其它檢測算法,改進(jìn)后的算法聚類的檢測率較高,誤檢率較低,可滿足誤用檢測及異常檢測的需求。
[Abstract]:The optimization problem of point-to-point network intrusion detection is studied. A point-to-point network is a multi-hop, centreless, self-organized wireless network. Its hosts often move according to their needs. The mobility of the host makes the topology of the network constantly change, and the mode and speed of the changes are unpredictable. This brings difficulties to network intrusion detection. The traditional detection method is very effective for the network with stable network topology, and it is not accurate for the self-organized and unpredictable point-to-point network intrusion detection. In order to improve detection ability and accuracy, an improved ART2 intrusion detection method (SART).) is proposed. When the number of patterns stored in artificial neural networks is large, the learning patterns can be organized effectively and the detection efficiency can be improved, and the difference between clustering can be reduced by adjusting the linear combination of the judging conditions of amplitude and phase. Simulation results show that compared with other detection algorithms, the improved clustering algorithm has higher detection rate and lower false detection rate, which can meet the needs of misuse detection and anomaly detection.
【作者單位】： 河北大學(xué)附屬醫(yī)院計(jì)算機(jī)中心;
【分類號】：TP393.08

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 于堯;李U，

本文編號：2363653