Abstract-Autonomous vehicle capable of navigating unpredictable real-world environments with little human feedback are a reality today. Such systems rely heavily on on-board sensors such as cameras, radar/LIDAR, and GPS as well as capabilities such as 3G/4G connectivity and V2V/V2I communication to make real time maneuvering decisions. Autonomous vehicle control imposes very strict requirements on the security of the communication channels used by the vehicle to exchange information as well as the control logic that performs complex driving tasks, e.g., adapting vehicle velocity, or changing lanes. This study presents a first look at the effects of security attacks on the communication channel as well as sensor tampering of a connected vehicle stream equipped to achieve Cooperative Adaptive Cruise Control (CACC). Our simulation results show that an insider attack can cause significant instability in the CACC vehicle stream. We also illustrate how different countermeasures, such as downgrading to ACC mode, could potentially be used to improve security and safety of the connected vehicle streams.
TitlePlatoon management with cooperative adaptive cruise control enabled by VANET AbstractPrevious studies have shown the ability of vehicle platooning to improve highway safety and throughput. With Vehicular Ad-hoc Network (VANET) and Cooperative Adaptive Cruise Control (CACC) system, vehicle platooning with small headway becomes feasible. In this paper, we developed a platoon management protocol for CACC vehicles based on wireless communication through VANET. This protocol includes three basic platooning maneuvers and a set of micro-commands to accomplish these maneuvers. Various platooning operations such as vehicle entry and vehicle (including platoon leader) leaving can be captured by these basic platoon maneuvers. The protocol operation is described in detail using various Finite State Machines (FSM), and can be applied in collaborative driving and intelligent highway systems. This protocol is implemented in an integrated simulation platform, VENTOS, which is developed based on SUMO and OMNET++. The validity and effectiveness of our approach is shown by means of simulations, and different platooning setting are calibrated.
Sampling techniques are widely used for traffic measurements at high link speed to conserve router resources. Traditionally, sampled traffic data is used for network management tasks such as traffic matrix estimations, but recently it has also been used in numerous anomaly detection algorithms, as security analysis becomes increasingly critical for network providers. While the impact of sampling on traffic engineering metrics such as flow size and mean rate is well studied, its impact on anomaly detection remains an open question.This paper presents a comprehensive study on whether existing sampling techniques distort traffic features critical for effective anomaly detection. We sampled packet traces captured from a Tier-1 IP-backbone using four popular methods: random packet sampling, random flow sampling, smart sampling, and sample-and-hold. The sampled data is then used as input to detect two common classes of anomalies: volume anomalies and port scans. Since it is infeasible to enumerate all existing solutions, we study three representative algorithms: a wavelet-based volume anomaly detection and two portscan detection algorithms based on hypotheses testing. Our results show that all the four sampling methods introduce fundamental bias that degrades the performance of the three detection schemes, however the degradation curves are very different. We also identify the traffic features critical for anomaly detection and analyze how they are affected by sampling. Our work demonstrates the need for better measurement techniques, since anomaly detection operates on a drastically different information region, which is often overlooked by existing traffic accounting methods that target heavy-hitters.
Abstract-Link failures are part of the day-to-day operation of a network due to many causes such as maintenance, faulty interfaces, and accidental fiber cuts. Commonly deployed link state routing protocols such as OSPF react to link failures through global link state advertisements and routing table recomputations causing significant forwarding discontinuity after a failure. Careful tuning of various parameters to accelerate routing convergence may cause instability when the majority of failures are transient. To enhance failure resiliency without jeopardizing routing stability, we propose a local rerouting based approach called failure insensitive routing. The proposed approach prepares for failures using interface-specific forwarding, and upon a failure, suppresses the link state advertisement and instead triggers local rerouting using a backwarding table. With this approach, when no more than one link failure notification is suppressed, a packet is guaranteed to be forwarded along a loop-free path to its destination if such a path exists. This paper demonstrates the feasibility, reliability, and stability of our approach.
This paper introduces a hybrid server/P2P streaming system called BitTorrent-Assisted Streaming System (BASS) for large-scale Video-on-Demand (VoD) services. By distributing the load among P2P connections as well as maintaining active server connections, BASS can increase the system scalability while decreasing media playout wait times. To analyze the benefits of BASS, we examine torrent trace data collected in the first week of distribution for Fedora Core 3 and develop an empirical model of BitTorrent client performance. Based on this, we run tracebased simulations to evaluate BASS and show that it is more scalable than current unicast solutions and can greatly decrease the average waiting time before playback.
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