Analyzing Attacks on Cooperative Adaptive Cruise Control (CACC)

Cooperative Adaptive Cruise Control (CACC) is one of the driving applications of vehicular ad-hoc networks (VANETs) and promises to bring more efficient and faster transportation through cooperative behavior between vehicles. In CACC, vehicles exchange information, which is relied on to partially automate driving; however, this reliance on cooperation requires resilience against attacks and other forms of misbehavior. In this paper, we propose a rigorous attacker model and an evaluation framework for this resilience by quantifying the attack impact, providing the necessary tools to compare controller resilience and attack effectiveness simultaneously. Although there are significant differences between the resilience of the three analyzed controllers, we show that each can be attacked effectively and easily through either jamming or data injection. Our results suggest a combination of misbehavior detection and resilient control algorithms with graceful degradation are necessary ingredients for secure and safe platoons.Comment: 8 pages (author version), 5 Figures, Accepted at 2017 IEEE Vehicular Networking Conference (VNC

Enhanced Position Verification for VANETs using Subjective Logic

The integrity of messages in vehicular ad-hoc networks has been extensively studied by the research community, resulting in the IEEE~1609.2 standard, which provides typical integrity guarantees. However, the correctness of message contents is still one of the main challenges of applying dependable and secure vehicular ad-hoc networks. One important use case is the validity of position information contained in messages: position verification mechanisms have been proposed in the literature to provide this functionality. A more general approach to validate such information is by applying misbehavior detection mechanisms. In this paper, we consider misbehavior detection by enhancing two position verification mechanisms and fusing their results in a generalized framework using subjective logic. We conduct extensive simulations using VEINS to study the impact of traffic density, as well as several types of attackers and fractions of attackers on our mechanisms. The obtained results show the proposed framework can validate position information as effectively as existing approaches in the literature, without tailoring the framework specifically for this use case.Comment: 7 pages, 18 figures, corrected version of a paper submitted to 2016 IEEE 84th Vehicular Technology Conference (VTC2016-Fall): revised the way an opinion is created with eART, and re-did the experiments (uploaded here as correction in agreement with TPC Chairs

Open issues in differentiating misbehavior and anomalies for VANETs

This position paper proposes new challenges in data-centric misbehavior detection for vehicular ad-hoc networks (VANETs). In VANETs, which aim to improve safety and efficiency of road transportation by enabling communication between vehicles, an important challenge is how vehicles can be certain that messages they receive are correct. Incorrectness of messages may be caused by malicious participants, damaged sensors, delayed messages or they may be triggered by software bugs. An essential point is that due to the wide deployment in these networks, we cannot assume that all vehicles will behave correctly. This effect is stronger due to the privacy requirements, as those requirements include multiple certificates per vehicle to hide its identity. To detect these incorrect messages, the research community has developed misbehavior data-centric detection mechanisms, which attempt to recognize the messages by semantically analyzing the content. The detection of anomalous messages can be used to detect and eventually revoke the certificate of the sender, if the message was malicious. However, this approach is made difficult by rare events –such as accidents–, which are essentially anomalous messages that may trigger the detection mechanisms. The idea we wish to explore in this paper is how attack detection may be improved by also considering the detection of specific types of anomalous events, such as accidents

Misbehavior detection in vehicular ad-hoc networks

In this paper we discuss misbehavior detection for vehicular ad-hoc networks (VANETs), a special case of cyber-physical systems (CPSs). We evaluate the suitability of existing PKI approaches for insider misbehavior detection and propose a classification for novel detection schemes

Message Type Identification of Binary Network Protocols using Continuous Segment Similarity

Protocol reverse engineering based on traffic traces infers the behavior of unknown network protocols by analyzing observable network messages. To perform correct deduction of message semantics or behavior analysis, accurate message type identification is an essential first step. However, identifying message types is particularly difficult for binary protocols, whose structural features are hidden in their densely packed data representation. We leverage the intrinsic structural features of binary protocols and propose an accurate method for discriminating message types. Our approach uses a similarity measure with continuous value range by comparing feature vectors where vector elements correspond to the fields in a message, rather than discrete byte values. This enables a better recognition of structural patterns, which remain hidden when only exact value matches are considered. We combine Hirschberg alignment with DBSCAN as cluster algorithm to yield a novel inference mechanism. By applying novel autoconfiguration schemes, we do not require manually configured parameters for the analysis of an unknown protocol, as required by earlier approaches. Results of our evaluations show that our approach has considerable advantages in message type identification result quality and also execution performance over previous approaches.Comment: 11 pages, 4 figures, to be published in IEEE International Conference on Computer Communications. INFOCOM. Beijing, China, 202

Поэзия абсурда в европейском варианте: к теоретической постановке проблемы

Целью данной работы является выяснение основных понятий и положений типов комического в литературе абсурда. Задача формирования основы типологии комического в поэзии, прозе и драматургии европейского абсурда обусловливает новизну исследования

Glycosylated hemoglobin as a screening test for hyperglycemia in antipsychotic-treated patients: A follow-up study

Purpose: To assess the point prevalence of undetected prediabetes (preDM) and diabetes mellitus (DM) in patients treated with antipsychotics and to compare metabolic parameters between patients with normoglycemia (NG), preDM, and DM. Furthermore, conversion rates for preDM and DM were determined in a 1-year follow-up.Patients and methods: In a naturalistic cohort of 169 patients, fasting glucose (FG) and hemoglobin A1c (HbA1c) criteria were applied at baseline and at follow-up after 1 year. A distinction was made between baseline patients diagnosed according to FG (B-FG) and those diagnosed according to HbA1c (B-HbA1c). Conversion rates in the 1-year follow-up were compared between B-FG and B-HbA1c.Results: At baseline, preDM and DM were present in 39% and 8%, respectively. As compared to patients with NG, metabolic syndrome was significantly more prevalent in patients with preDM (62% vs 31%). Although the majority of patients were identified by the FG criterion, HbA1c contributed significantly, especially to the number of patients diagnosed with preDM (32%). Regarding the patients with preDM, conversion rates to NG were much higher in the B-FG group than in the B-HbA1c group (72% vs 18%). In patients diagnosed with DM, conversion rates were found for B-FG only.Conclusion: PreDM and DM are highly prevalent in psychiatric patients treated with antipsychotic drugs. HbA1c was shown to be a more stable parameter in identifying psychiatric patients with (an increased risk for) DM, and it should therefore be included in future screening instruments