本文選題：Sybil攻擊 + 幾何交叉模型算法　； 參考：《通信學(xué)報》2017年04期

【摘要】：在車聯(lián)網(wǎng)中,同時使用多個虛假身份的Sybil攻擊,在網(wǎng)絡(luò)中散布虛假消息,都易造成資源的不公平使用和網(wǎng)絡(luò)混亂。針對這一問題,提出快速識別車輛虛假位置的事件驅(qū)動型輕量級算法,當(dāng)車輛出現(xiàn)在另一車輛的安全區(qū)域內(nèi),啟動快速識別兩車輛是否重疊的幾何交叉模型(GCR,geometrical cross-recognition)算法,檢測聲稱虛假位置的錯誤行為;同時,根據(jù)證實車輛收集的鄰居范圍內(nèi)的局部車輛,建立位置偏差矩陣(PDM,position deviation matrix),進(jìn)一步識別交叉車輛中的Sybil節(jié)點。性能分析和仿真實驗表明,安全區(qū)域驅(qū)動下的輕量級算法識別速度快,檢測率高,在車輛定位誤差較低時性能更好;安全區(qū)域的引入也均衡了車輛密度過大時造成的通信負(fù)載影響,與同類算法相比,通信處理時延較低。
[Abstract]:In the network of vehicles, using multiple Sybil attacks of false identities at the same time, spreading false messages in the network, it is easy to cause unfair use of resources and network confusion. In order to solve this problem, an event-driven lightweight algorithm is proposed to quickly identify the false position of a vehicle. When the vehicle appears in the safety area of another vehicle, the geometric cross model (GCR geometric cross-recognition) algorithm is started to quickly identify whether the two vehicles overlap. At the same time, according to the local vehicles in the neighborhood range of the confirmed vehicle collection, the position deviation matrix is established to further identify the Sybil nodes in the cross vehicle. The performance analysis and simulation results show that the lightweight algorithm driven by the safe region has the advantages of fast recognition speed, high detection rate and better performance when the vehicle positioning error is low. The introduction of security region also balances the communication load when the vehicle density is too high. Compared with the similar algorithms, the delay of communication processing is lower.
【作者單位】： 江蘇大學(xué)計算機(jī)科學(xué)與通信工程學(xué)院;安徽大學(xué)信息保障技術(shù)協(xié)同創(chuàng)新中心;
【基金】：國家自然科學(xué)基金資助項目(No.61472001) 江蘇省重點研發(fā)計劃基金資助項目(No.BE2015136)~~
【分類號】：U495;TN929.5
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本文編號：1825139