Detecting Location Spoofing using ADAS sensors inVANETs

Kiho LimDepartment of Computer Science

University of South DakotaVermillion SD, USA

kiho.lim@usd.edu

Kastuv M. TuladharDepartment of Computer Science

University of South DakotaVermillion SD, USA

kastuv.tuladhar@coyotes.usd.edu

Hyunbum KimDepartment of Computer Science

University of North Carolina at WilmingtonWilmington, NCkimh@uncw.edu

Abstract—Location information plays a vital role in VANETsas numerous applications are location dependent. Thus, it isessential to preserve the integrity of the location informationof vehicle. With the advancement of the vehicular technologies,it is observed that advanced driver-assistance system (ADAS)sensors are being installed on vehicles. In this paper, we proposea detection mechanism of location spoofing by leveraging theon-board ADAS sensors. Under our proposed scheme, attacksbased on location spoofing such as sybil attack could be efficientlydetected using unique ADAS sensor data like fingerprint, withoutthe involvement of the infrastructure and third party trustedauthority.

Key words: V2V, VANET, location spoofing detection, ADASsensor, sybil attack.

I. INTRODUCTION

In the last few years, we have witnessed the rapid de-velopment and deployment of advanced vehicular technology.Various car manufacturers and researchers have been work-ing on the smart vehicle systems, pilot assisted self-drivingvehicles towards the fully autonomous driving. Recently, theUS Department of Transportation (DOT) has also conductedthe connected vehicle (CV) pilot deployment program [1] forreal-world feasibility. Vehicular communication has severalsecurity requirements as it deals with applications for the safedriving environment such as traffic information, weather con-dition, road emergency, navigation, value-added-applicationsetc. Most of, if not all, the above applications relies on thelocation information. If the location information of vehicles arecompromised, then most of the applications will not functionproperly. Further, false or mis-leaded location informationcould trigger an accident that can cause financial loss and eventhreaten driver’s lives.

The provision of the on-board GPS has revolutionized thenavigation system in driving. Similarly, recent introduction ofthe short-range radar and Lidar (Light detection and ranging)has provided the virtual eye creating the surrounding mapfor vehicles and applied as advanced driver-assistance sys-tems (ADAS) to reduce the navigation problems and possibleaccidents by providing the accident awareness warning [2].Although the advanced sensing technology is leading thevehicles towards connected vehicles and fully autonomousvehicles, the vehicular communication should be protected bypossible attackers.

Location is a fundamental and essential information in

VANETs, thus a malicious node or attacker can attempt tospread a fake location information to take advantage of findingshort routes or to trigger malicious attacks. Some of possibleattacks based on location spoofing adversaries can attempt arethe following: 1) Fabrication Attack: creating a bogus messageand lie about the location of traffic congestion or its ownlocation. 2) Alteration Attack: Attacker modify the locationin the message or its own location information. 3) PacketDropping: Attacker can drop the packets as black-hole attack(dropping all packets) or gray-hole attack (dropping selectivepackets). 4) Replay attack: Attacker pretends to be a vehiclein the past and re-injects the previously received packets orinformation or beacon messages. 5) Sybil Attack: The attackerpretends to be multiple vehicles with fake location information.The attacker may advertise the non-existing neighbors.

In order to address such location spoofing based at-tacks, various approaches have been studied [3]–[7]. Raya etal. [3] proposed a public-key cryptography scheme to detectand revoke the malicious nodes. The scheme used severalpseudonyms for each node where pseudonyms are bind withthe private/public key pair issued by the certificate authority(CA). The nodes are revoked by revoking its correspondingcertificates. In [8], a scheme to detect a sybil attack wasproposed using the resource testing (RT) method in P2Pnetworks. In this method, equal resources are assigned to allthe nodes and the attacker has to allocate more resoursesto its sybil node in order to perform the attack, thus, itcannot behave as a normal node. However, monitoring resourceutilization demands separate entity and channel. The modernvehicles perform variety of applications and may requiresdifferent resources. Matrucci et al. [4] proposed self-certifiedpseudonyms to prevent from location spoofing attacks. Authorsin [5] proposed a scheme called P 2DAP (Privacy-preservingDetection of Abuses of Pseudonyms). Their scheme consistsof two pool of pseudonyms for vehicles. The pools are dividedinto two hash functions (i.e. coarse-grained and fine-grained).The hash value of the fine-grained pseudonyms are the samefor all the pseudonyms of a same node and different for thedifferent nodes. Thus, a roadside unit (RSU) and CA togethercan verify in case of dispute. Although, pseudonyms fulfill theprivacy requirements of the VANET, there is some tradeoff inpreserving the anonymity and detecting the location spoofingattacks at the same time.

Park et al. [6] used the TS (time stamps) series to detectthe location spoofing attack with the support of RSU. The RSU

provides the certificates to the vehicles. With the assumptionof two RSUs cannot provide certificates to the same vehiclesat the same time, if the two certificates has same TS then itwill be treated as a sybil attack. Similarly, in [9], the vehiclelocation is monitored by gathering the GPS node and collectingposition from active neighboring nodes through the signatureTS. However, as the scheme relies on the neighbor information,the group of corrupted vehicles can easily circumvent the loca-tion verification mechanism. Rabieh et al. [7] proposed cross-layer scheme to detect the sybil attack. The scheme containsa challenge packet to the claimed location of the vehicle. Inthis scheme, a directional antenna is utilized to communicatein the expected location of the vehicle. However, vehicles maychange their speed and trajectory depending on circumstances,further, maintaining the directional antenna for communicationis inefficient in VANET as the nodes change their positiondynamically. Ruj et al. [10] proposed data-centric misbehaviordetection algorithms that detect the false alert messages fromthe particular location by observing the actions after sendingout the alert messages. In this scheme, vehicles send periodicbeacon messages so that the neighbors’ positions are monitoredover the time. The inconsistent position during the alert marksthe message as a flag. Similarly, authors in [11] exploited theradar and beacons to crosscheck the location send during thealert message. Golle et al. [12] proposed a scheme to detectmisbehaving nodes leveraging the the sensor capabilities. Thearrived information is validated with the angle of arrival andreceived signal strength, if mismatched, possible maliciousnode is suspected. However, the accuracy and efficiency can beelevated by utilizing the advance and accurate sensors. Authorsin [13] proposed threshold-based event validation scheme thatsuspects the malicious nodes on particular location based oncounting the number of events reported by multiple vehicles.However, it is possible that multiple vehicles can colludeand report the same event message. Further, separate eventvalidating authority is required for all the recorded events.

Most of the above-mentioned approaches require the in-frastructure and/or require a third party authority such as theTA to manage and maintain the certificates. In this paper,we propose a scheme for detecting location spoofing attackswithout any support from the infrastructure and/or a thirdpart authority. Also, our scheme leverages the existing ADASsensors installed on vehicles to detect the location spoofingattack, so it does not require any additional hardware cost.

The rest of the paper is organized as follows. Section IIintroduces the system model of the proposed scheme. Sec-tion III presents our proposed scheme in detail. In Section IV,we present the implementation of our scheme and analyzeit. Finally, we conclude with discussion and future work inSection V.

II. SYSTEM MODEL

In this section, we describe our system model and theassumptions for the proposed protocol and the attacks ofinterest. In our approach, the locality information of vehicle isutilized to detect the location spoofing attack.

- A wireless transceiver: For the fast and short-rangewireless communication, the transceiver of the vehicleadopts the standard dedicated short range communication

Figure 1: Overview of Location Spoofing Detection

(DSRC) [14] designed for the VANET standardIEEE802.11p [15]. DSRC for Intelligent TransportationSystems (ITS) is operates in the 5.9 GHz band in the U.S.The range of the transmission is about 250 to 300 meters. Weassume that an attacker can modify the location informationin messages before transmitting.

- GPS receiver: To obtain the accurate location, a GPSreceiver is used, which provides location information suchas latitude, longitude, altitude, speed and the direction of thevehicle. The location provided by the GPS can be fabricatedby an attacker but the relative distance and angle of thevehicle can be calculated with the ADAS sensors, thus thelocation information can be verified.

- ADAS Sensor: Modern vehicles are equipped with theADAS sensor including radar [16], [17], lidar [18], camera [19]and other sensors such as microwave, infrared or ultrasonicradar. Radar is a low-cost sensor compared to lidar and it offerlonger operating distance. Similarly, high resolution camerasare now installed in vehicles that are combined with the imageprocessing, can be utilized to identify the object type. Lidarhas been recently used to enable the self-driving car as itcan provide continuous 360 degrees of visibility and accuratedepth information of the surrounding. We assume a ray-castinglidar is mounted at the top of the vehicle and it generates theframe of the casted points around its surrounding. The framegenerated by the ADAS sensors system is in a raw form andit is processed by On-Board-Unit (OBU) of the vehicle. Theprocessed data can find the object around the vehicle with thedistance & angle.

For adversary model, we consider attackers who attemptto launch a location related attack by sending fake positionmessages. An attacker might fabricate, alter, replay the locationrelated messages or launch a sybil attack. We are interested inthe location spoofing attacks which are co-located with theneighboring vehicles traveling along the road. In this paper,we mainly focus on detecting such location spoofing basedattacks.

The overview of the location spoofing detection is de-scribed in Figure 1. The vehicle equipped with ADAS sensorsscans the periphery areas and detects the surrounding objectsnear the vehicle. The attacker claims the fake position and triesto communicate with the vehicle. The vehicle then verifies ifthe claimed location is real or fake. To summarize, our maincontributions are highlighted as follows:

• A detection scheme that can verify fake location withoutthe presence of a trusted third party and/or an infrastruc-ture in VANET environment.

• Utilization of existing ADAS sensors i.e. lidar, radar,cameras and other on-board sensor units which do notrequire additional cost.

• A secure and robust protocol against the possible locationspoofing attacks.

III. PROPOSED SCHEME

Our proposed scheme uses three main step to detectlocation spoofing attacks: 1) Source verification; 2) receiverconfirmation; 3) surrounding objects verification. We discussthe details of our protocol as follows.

When a vehicle VR receives a message M with gpsinformation gps and sensor data sensor data from the sourcevehicle VS , VR runs the algorithm for detecting locationspoofing as shown in Algorithm 1. To begin the process, VRchecks if the timestamp contained in M is valid and withinthe threshold τ. If the timestamp is valid, VR compute thedistance dRS and the angle φRS of VS from VR using gpsinformation of VS and VR. Note that the distance and theangle are computed by leveraging the center point of the targetvehicle. For example, if the vehicle is in (x1, y1) and the targetvehicle is in (x2, y2) positions, then the distance and angle canbe calculated using Equation 1 & Equation 2.

d =

√(x1 − x2)

2+ (y1 − y2)

2 (1)

φ = tan−1(y2 − y1x2 − x1

) (2)

By using the dRS and φRS , VR calculates its location andcompares it with the sensor dataR to see if VS actually existsat the location where VS claims to be. If VS does not existat the location in sensor dataR, then location spoofing isdetected. The process of source verification is illustrated in theline 6-11 in Algorithm 1. Once the source location is verified,the location of the receiver also needs to be confirmed inthe source’s sensor data sensor dataS . Similarly, the distancedSR and the angle φSR of VR from VS are computed and thelocation of VR is confirmed in sensor dataS . If the receiver isnot found in sensor dataS , then there exists possible locationspoofing. The recipient verification process is illustrated in theline 13-18.

After the locations of source VS and the receiver VR areverified, VR continues to verify the surrounding objects of VSbecause the surrounding objects of VS captured by the ADASsensor system of VS should also be captured by the ADASsensor system of VR. Note that we assume that the clocks aresynchronized and both use sensor dataR and sensor dataSgenerated at the same time ts. To verify the surroundingobjects of VS , VR generates two objects lists: SLi generatedusing sensor dataS and S′Li generated using sensor dataR.The objects list contains information such as distance, angleand object type, and the objects are sorted in the order ofdistance from VS . Now, VR compares the distances, the angles,and the objects types between SLi and S′Li and check if the

Algorithm 1 Location Spoofing DetectionRequire: gpsS , gpsR, sensor dataS , sensor dataR1: . Check if timestamp is within threshold2: if ts > τ then3: return FALSE4: end if5:6: . Source verification7: dRS ← ComputeDistance(gpsR, gpsS)8: φRS ← ComputeAngle(gpsR, gpsS)9: if V erifyObject(dRS , φRS , sensor dataR)! = VS then

10: return FALSE11: end if12:13: . Recipient confirmation14: dSR ← ComputeDistance(gpsS , gpsR)15: φSR ← ComputeAngle(gpsS , gpsR)16: if V erifyObject(dSR, φSR, sensor dataS)! = VR then17: return FALSE18: end if19:20: . Surrounding objects verification21: SLi = ComputeObjectList(sensor dataS)22: S′

Li = ComputeObjectList(sensor dataR)23: . Compare S′

Li with SLi24: for each L′

i ∈ L, rφ ≤ Li.φ′ ≤ lφ do25: if ((Li.d − εd ≤ Li.′d ≤ Li.d + εd) & &

(Li.φ − εφ ≤ Li.φ′ ≤ Li.φ + εφ) & &(Li.

′type = Li.type)) then

26: continue27: else28: return FALSE29: end if30: end for31: . location spoofing not detected32: return TRUE

object type is matched and the distance and the angle of thesame object is within the error boundary (εd and εφ). Theprocess of surrounding objects verification is illustrated in line20-30. Note that it will only check the selected closest objectsas VR might not be able to detect all of surroundings of VSthrough its sensors. In this case a similarity score techniquescould be used. After the surrounding objects are verified, thealgorithm is terminated with location spoofing not detected.

IV. IMPLEMENTATION & ANALYSIS

In this section, we present the implementation of ourscheme and the security analysis. We simulated our schemeusing an autonomous urban driving simulator, CARLA [20].The lidar used in the simulation performs similar to VelodyneHDL-32E and is configured with the following parameters:32 number of channels, 100 meters of the range, points perseconds (PPS) fixed to 10000, rotating frequency set to 10,field of view (FOV) set to 40◦. And the sensor data wascollected in the auto-pilot mode.

The simulation environment is shown in the Fig 2 fromthe Carla simulator that contains the visible objects like targetvehicle, trees, trash can, traffic lights etc. The lidar is placed atthe vehicle v that sends the pulses of the laser light and scansthe object on the basis of the time elapsed by the reflectedpulse waves. Note that to cover longer distance and improveaccuracy, radar data also can be added, and we considerthe objects within 100m in our simulation. By scanning theperiphery of the vehicle using ADAS sensor, the surrounding

Figure 2: Simulation Environment

Figure 3: ADAS Sensor Map

objects including the source vehicle can be identified and theraw data with object detection is shown in the Fig 3.

To prevent location spoofing attacks, our scheme verifiesthe location of the source vehicle sending a message bymatching the locality information i.e. the distance, angle &the object type near to its surrounding. It consists of the threesteps of location verification for the source, the receiver, andthe surrounding objects. The location of the vehicle is con-firmed by matching the objects in its surrounding. The wholeprocess works like a finger print, i.e., the vehicle has a uniquedistance and angle towards the surrounding objects from itsunique position. This unique sensor information can not begenerated unless the vehicle is situated in its claimed location.In VANETs, adversaries may try to attack by fabricating,altering or replaying the location information, but using thisscheme, such location spoofing attack can be detected underour scheme.

V. CONCLUDING REMARKS & FUTURE WORK

VANET application has revolutionized our driving experi-ence. Either it is safety related or a entertainment applications,if not all, most of the applications are location aware. In thispaper, we introduced our preliminary work to detect the attacksrelated location spoofing. Also, we explored the different typesof location related attacks and proposed a detection mechanismby leveraging the on-board ADAS sensor.

In the future, we will continue to work on our currentimplementation to improve accuracy and efficiency. To betteraccuracy on recognizing the surrounding objects and matchingthem between vehicles, we will utilize some machine learningalgorithms. Also, we will conduct other location validation

tests under various traffic situations to further evaluate ourscheme.

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