第一代微軟Windows個人電腦的用戶經常害怕出現恐怖的“死亡藍屏”,眾所周知,這個停機畫面標志著系統(tǒng)的致命崩潰。但如果這臺電腦正在控制著你行駛在高速公路上的汽車呢?自動駕駛汽車上發(fā)生的任何“藍屏”都意味著一起慘烈的撞車事故。
電氣與電子工程師學會《智能交通系統(tǒng)學報》最近發(fā)表的一篇分析文章認為:因為各種各樣的自動駕駛汽車與卡車將在未來5年左右的時間問世,這些車輛理論上已通過了合理的道路測試,并配備了道路控制系統(tǒng),因此,現在是汽車制造商思考應該如何保護車輛免受黑客攻擊的時候了。這篇文章可能是第一篇“研究針對自動駕駛汽車可能遇到的網絡攻擊”的文章,“這些車輛面對著各自不同的自的需求與風險,”文章中寫道。文章的聯合作者為愛爾蘭科克大學移動與網絡系統(tǒng)實驗室的研究員Jonathan Petit,與加州大學伯克利分校研究工程師兼加州PATH(先進交通技術合作伙伴)項目經理Steven E. Shladover。
文章警告讀者,汽車行業(yè)并沒有準備好應對朝著聯網的自動駕駛汽車逼近的威脅,這些車輛將通過車到車(V2V)和車到基礎設施(V2I)的專用短程通信(DSRC)交換數據。盡管這種外部的合作信息會提升車輛的性能與安全,但同時也出現了可能為不法分子所利用的網絡漏洞。
Shladover提到,在文章的分析中,研究人員將關注重點放在SAE J3016標準中所定義的自動駕駛級別中最高的三級——有條件的自動駕駛、高度自動駕駛和全自動駕駛。他本人曾在設立這些標準的SAE International委員會中任職。
文章說,在有條件的自動駕駛系統(tǒng)中,司機可以在發(fā)生不利事件后幾秒內掌握汽車控制權,但這幾秒鐘可能發(fā)生很多事情,比如汽車可以在幾秒種內行駛超過100米(328英尺)的距離。在高度自動駕駛系統(tǒng)和全自動駕駛系統(tǒng)中,即使司機不采取任何行動,汽車都必須回歸安全(“最低風險”)狀態(tài)。這一要求給系統(tǒng)設計師提出了非常高的要求,因為他們必須在不降低安全性的前提下,考慮到網絡攻擊的所有后果并加以防范。
車輛聯網vs.自動駕駛
兩位研究人員提出了三個問題:一輛獨立運作且設備齊全的、不與周圍車輛交流的自動駕駛汽車是怎樣受到攻擊的?相互交流的自動駕駛汽車是怎樣受到攻擊的?這兩種情況之間有何區(qū)別?
“我們對自動駕駛汽車進行了威脅分析,確定了聯網車輛和未聯網車輛分別可能碰到的問題,”Petit表示,他列舉了一些風險最高的場景,并試圖尋找防御措施。這意味著必須對ITS(智能交通系統(tǒng))網絡的網關進行防御,以防罪犯突破防火墻,控制路標/傳感器與地圖,這也意味著必須抵御針對以下設備的攻擊:GPS和導航設備、里程表與聲音傳感器、包括雷達、激光雷達、攝像頭和機器視覺在內的常見障礙物偵測與追蹤系統(tǒng)等。
作者指出,針對獨立汽車的威脅更具破壞性,因為如果駕駛員沒有將注意力放在駕駛上,那么他將無法在生死攸關的幾秒鐘內提供沒有受到破壞的全面信息,或戰(zhàn)勝失靈的系統(tǒng)。他們引述了通用汽車公司最近開展但尚未公布的一項研究結果,該研究表明“在汽車全自動駕駛連續(xù)5-30分鐘后,幾乎沒有參加駕駛任務或監(jiān)控駕駛環(huán)境的駕駛員將幾乎完全依賴自動駕駛系統(tǒng)。”
Shladover說,自動駕駛系統(tǒng)面臨著信號探測的難題。幾乎所有的主動錯誤信息和被動錯誤信息都會干擾駕駛員,因此必須避免。系統(tǒng)“對這兩者的探測成功率必須達到相當的高度,但是,這很難實現。”一些危險情形很難探測出來,例如嚴重交通堵塞中具有破壞性的路面坑槽。
文章認為,未來的自動駕駛汽車很可能會使用多種多樣的傳感器。但無論情況如何,最終都要求數據融合軟件扮演一個極其重要的角色,因為它們可以通過確認汽車與周邊環(huán)境的真實狀態(tài)以確保安全。智能控制算法將整合來自各源頭的數據,并對收集到的信息進行事實比對。
文章中將網絡攻擊按戰(zhàn)術策略分成三類:被動窺探或主動操縱、信號干擾和發(fā)送欺騙性信息、攻擊獨立運行的車輛或攻擊一個聯網車輛的網絡。
未聯網汽車的漏洞
“一輛未聯網的自動駕駛車輛可能察覺不到自身正在受到攻擊,” Shladover表示,“隱秘的攻擊可能更難察覺和避免,”尤其是當車輛的控制系統(tǒng)不知道它正使用著錯誤數據時,很可能一場車禍將不可避免。
文章認為,針對未聯網的自動駕駛汽車的威脅中,有兩種特別顯著,一是蒙蔽攝像頭或在視覺系統(tǒng)中插入虛假視頻,二是干擾GPS信號或發(fā)送錯誤的GPS數據。
“攝像頭就是車輛的眼睛,”Petit指出。“它們很容易受到攻擊。你可以給它輸入系統(tǒng)過去記錄下的圖像,或用一只小小的鐳射筆來操作亮度,以達到欺騙攝像頭的目的。而且發(fā)送欺騙性的GPS數據也不會耗費多少資源。”GPS干擾設備只需20美元就能購買,更昂貴的一些可以實施GPS欺騙,它們可以復制信號覆蓋真實數據,并通過干擾到達目標位置所需的信號校準,來傳遞錯誤位置信息。
此外,電磁脈沖(EMP)可能給未聯網汽車帶來中等程度的風險。它可以使所有電子設備停止運行,并將迷惑性的環(huán)境信息強加給雷達或激光雷達的掃描器。
聯網汽車的漏洞
為了交通管理的便捷,自動駕駛汽車很可能會連成一片網絡,因此錯誤數據可能會在整個網絡的車輛間傳遞。最嚴重的威脅有可能是注入并擴散一些可能導致錯誤反應的不實導航信號或安全信息(如虛假剎車信息),這將嚴重威脅整個區(qū)域內的車輛安全。
另一種針對聯網汽車的嚴重威脅是共享地圖數據庫。儲存在本地的動態(tài)地圖很容易受到攻擊。這種攻擊有別于未聯網自動駕駛車輛的地圖數據入侵行為,因為它攻擊的目標不是收集浮動汽車數據的在線服務器。
“共享網絡使得多種攻擊路徑和攻擊手段的結合成為可能,” Petit表示。文章認為,要迎擊這些威脅,必須建立基于加密技術的身份認證系統(tǒng)(如“公共秘鑰基礎架構”)和“錯誤行為探測系統(tǒng)”。“錯誤行為探測系統(tǒng)”能夠應用算法尋找不一致的地方,猜測汽車行為在哪里出了錯,并進行標記。這樣就能在網絡中建立一個不能信賴的撤回信息的數據庫并進行及時更新。
只有為自動駕駛汽車包裹上一層又一次的安全措施——在更深層面實現數字防御,OEM才有機會阻擋即將到來的黑客行為與外來干擾。
Users of the first Microsoft Windows personal computers feared the somber glow of the “Blue Screen of Death,” the infamous stop screen that signaled a fatal system crash. But what if your computer is driving you down the highway? Any BSoD moment in an autonomous vehicle might mean facing a far harsher crash altogether.
Because automated cars and trucks of one flavor or another, presumably piloted by reasonably road-tested and street-wise control systems, will hit the road in five years or so, it’s time for car makers to think about how they are to be protected from digital attack by hackers, according to the authors of a recent analysis published in IEEE Transactions of Intelligent Transportation Systems. The paper may be the first “investigation of the potential cyberattacks specific to automated vehicles, with their special needs and vulnerabilities,” wrote Jonathan Petit, Research Fellow at the University College Cork’s Mobile and Internet Systems Laboratory in Ireland, and Steven E. Shladover, Research Engineer at theUniversity of California, Berkeley, and Program Manager for California PATH(Partners for Advanced Transportation Technology).
The paper’s authors warn that the auto industry is unprepared for the impending threats against network-connected robot cars, which will exchange data via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) dedicated short-range communications (DSRC). Though the external cooperative information will improve performance and safety, such access means network vulnerabilities for potential malefactors, they stated.
For their investigation, the collaborators focused on the three highest levels of automation within the SAE J3016 definitions of driving automation: conditional automation, high automation, and full automation, said Shladover, who served on the SAE International committee that had formulated the standards.
For conditional automation systems, the driver is expected to be able to resume vehicle control within a few seconds of an adverse event, but much can happen in a few seconds, which can mean up to 100 m (328 ft) of travel, the paper stated. With high- and full-automation systems, it is required to bring the vehicle to a safe (“minimal risk”) state, even if the driver takes no action, placing a much higher burden on the designer of the system to manage any consequences of a cyberattack without compromising safety.
Connected vs. autonomous
The two researchers asked three questions: How can autonomous automated vehicles, those that are both independent and self-contained, not communicating with others around them, be attacked? How can cooperative automated vehicles be attacked? Finally, they considered the differences between the two.
“We did a threat-analysis for automated vehicles, identifying the problems that a networked, connected vehicle might encounter plus those that a self-contained autonomous car might face,” formulating a list of riskiest scenarios and then seeking defense strategies, Petit said,. This means guarding gateways to ITS (intelligent transportation system) networks from penetration and any control over road signs/sensors and community maps, as well as fending off attacks aimed at GPS and navigation devices, odometer and acoustic sensors, and common obstacle-detection and -tracking sensors including radar, lidar, cameras, and machine-vision systems.
The authors noted that threats to autonomous vehicles are potentially more damaging because the driver may not be available to provide independent uncorrupted information or to defeat a malfunctioning system within the critical few seconds if the driver is not paying attention to driving. They cited recent, as yet unpublished, research by General Motors that “has shown that drivers largely disengage from the driving task and monitoring of the driving environment after continuous intervals of fully automated driving ranging from 5 to 30 min, becoming almost totally dependent on the automation system.”
Self-driving systems face a tough signal-detection problem, Shladover said. Almost any false positives and false negatives would annoy drivers and so must be avoided, but the system’s “success rates for both of them have to be way out on the tails, which is hard to do.” Some hazardous conditions are very hard to detect, he noted, such as damaging pot holes amid heavy traffic.
The researchers said that future automated vehicles will probably involve more and different sensors; in any case they expect that data-fusion software will ultimately play an important role in ensuring safety by determining the true state of the vehicle and its surroundings. Smart control algorithms will combine the data received from all sources and fact-check the collected information.
The paper categorizes cyber-threats in terms of three alternative tactical approaches: passive snooping versus active manipulation, signal jamming versus sending false messages (or spoofing), and attacks targeting single vehicles versus those exploiting a network of connected vehicles.
Vulnerable alone
“An independent, autonomous car may not know that it’s been attacked,” Shladover said, “Stealthy attack is much more difficult,” particularly problematic if the vehicle control doesn’t know it has bad data and a road crash could be unavoidable.
Two principal threats to solo robot cars stood out, the paper said: blinding cameras or inserting fake video into vision systems, and jamming or spoofing GPS signals.
“Cameras are mobile eyes,” Petit said. “They’re hacked easily. You could feed a system's recorded images or mess up the cameras by playing with the brightness using something as simple as a laser pointer. And it doesn’t take a great amount of resources to do GPS spoofing,” he said. GPS jamming uses equipment that is available for around $20 and more expensive GPS jammers to perform GPS spoofing, where they replicate signals and pass false locations essentially by fouling the signal correction for drift in the target location.
Medium-level risks to single autonomous cars are posed by electromagnetic pulses (EMPs) that could shut down the electronics altogether or environmental confusion inflicted on radar and lidar scanners.
Vulnerable together
Automated cars will probably be connected in mesh networks to enable more efficient traffic management, so bad data could end up being passed among vehicles and through the network. Probably the biggest threat is the injection and propagation of incorrect navigation signals or safety messages that generate wrong reactions (such as spurious braking) that can be life-threatening for all in the vicinity.
The other high-level threat to connected automated vehicles is the shared map database. The locally stored dynamic maps are susceptible to map poisoning. This attack differs from map poisoning of autonomous automated vehicles in the sense that it does not target an online server that collects floating car data.
“Shared networks provide all kinds of attack routes and combination of attacks,” Petit said. Against these threats the authors propose establishing authentication systems, which might be based on encryption, say, a Public Key Infrastructure, and “misbehavior detection systems,” which use algorithms to look for inconsistencies and guess when something in a car’s activity just is not right and then flag it. The network thus develops a constantly updated list of untrustworthy and revoked information data sources.
Only by cladding robot cars in overlapping security measures—a digital defense in depth—have OEMs a chance of foiling the hacking and external tampering to come.