Stuck in Traffic (SiT) Attacks: A Framework for Identifying Stealthy Attacks that Cause Traffic Congestion

Recent advances in wireless technologies have enabled many new applications in Intelligent Transportation Systems (ITS) such as collision avoidance, cooperative driving, congestion avoidance, and traffic optimization. Due to the vulnerable nature of wireless communication against interference and intentional jamming, ITS face new challenges to ensure the reliability and the safety of the overall system. In this paper, we expose a class of stealthy attacks -- Stuck in Traffic (SiT) attacks -- that aim to cause congestion by exploiting how drivers make decisions based on smart traffic signs. An attacker mounting a SiT attack solves a Markov Decision Process problem to find optimal/suboptimal attack policies in which he/she interferes with a well-chosen subset of signals that are based on the state of the system. We apply Approximate Policy Iteration (API) algorithms to derive potent attack policies. We evaluate their performance on a number of systems and compare them to other attack policies including random, myopic and DoS attack policies. The generated policies, albeit suboptimal, are shown to significantly outperform other attack policies as they maximize the expected cumulative reward from the standpoint of the attacker

Acid catalysed dimerisation of 2,3-dimethylbutadiene

The acid catalysed dimerisation of 2,3-dimethylbutadiene has been carried out using sulphuric acid in acetic acid as solvent. Analysis by gas chromatography, mass spectroscopy revealed that the product, b.p.46 - 120&deg; at 0.5 mm consisted of at least thirteencompounds. Of the eight major components (hydrocarbons), five have a molecular weight of 164 and three 162. No acetates appeared to be formed during the reaction and only one compound was found to be a solid (m.p. 87&deg;). The major compounds were separated in a pure state using preparative G.l.c. and their molecular structure investigated using H and C n.m.r. and open-chain, mono-cyclic, bicyclic, tricyclic and aromatic structures have been found in these compounds.The following compounds were suggested: 3-methylene-2,6,7-trimethyl-1,6-octadiene or 4-methylene-2,3,7-'trimethyl-2,7-octadiene, 1,2,4-trimethyl 4-isopnopenyl cyclohex-1-ene, 2-methylene-1,3,3,4-tetramethyl-bicyclo[2,2,1]heptane, 2-methylene-1,4,7,7-tetramethylbicyclo [2,2,1]heptane (solid), 2,4-dimethyltricyclene or 1,2,4,7,7-pentamethyltricyclo(2,2,1,0,2-6) heptane, 2,4-2,5- and 3,4-dimethyl t-butylbenzene.The kinetic study of the reaction has shown that it is second order in 2,3-dimethyl-1,3-butadiene. <p

Few-Shot Object Detection in Unseen Domains

Few-shot object detection (FSOD) has thrived in recent years to learn novel object classes with limited data by transferring knowledge gained on abundant base classes. FSOD approaches commonly assume that both the scarcely provided examples of novel classes and test-time data belong to the same domain. However, this assumption does not hold in various industrial and robotics applications, where a model can learn novel classes from a source domain while inferring on classes from a target domain. In this work, we address the task of zero-shot domain adaptation, also known as domain generalization, for FSOD. Specifically, we assume that neither images nor labels of the novel classes in the target domain are available during training. Our approach for solving the domain gap is two-fold. First, we leverage a meta-training paradigm, where we learn the domain shift on the base classes, then transfer the domain knowledge to the novel classes. Second, we propose various data augmentations techniques on the few shots of novel classes to account for all possible domain-specific information. To constraint the network into encoding domain-agnostic class-specific representations only, a contrastive loss is proposed to maximize the mutual information between foreground proposals and class embeddings and reduce the network's bias to the background information from target domain. Our experiments on the T-LESS, PASCAL-VOC, and ExDark datasets show that the proposed approach succeeds in alleviating the domain gap considerably without utilizing labels or images of novel categories from the target domain

Telomeres and replicative cellular aging of the human placenta and chorioamniotic membranes

Recent hypotheses propose that the human placenta and chorioamniotic membranes (CAMs) experience telomere length (TL)-mediated senescence. These hypotheses are based on mean TL (mTL) measurements, but replicative senescence is triggered by short and dysfunctional telomeres, not mTL. We measured short telomeres by a vanguard method, the Telomere shortest length assay, and telomere-dysfunction-induced DNA damage foci (TIF) in placentas and CAMs between 18-week gestation and at full-term. Both the placenta and CAMs showed a buildup of short telomeres and TIFs, but not shortening of mTL from 18-weeks to full-term. In the placenta, TIFs correlated with short telomeres but not mTL. CAMs of preterm birth pregnancies with intra-amniotic infection showed shorter mTL and increased proportions of short telomeres. We conclude that the placenta and probably the CAMs undergo TL-mediated replicative aging. Further research is warranted whether TL-mediated replicative aging plays a role in all preterm births

Stuck In Traffic (Sit) Attacks: A Framework For Identifying Stealthy Attacks That Cause Traffic Congestion

Recent advances in wireless technologies have enabled many new applications in Intelligent Transportation Systems (ITS) such as collision avoidance, cooperative driving, congestion avoidance, and traffic optimization. Due to the vulnerable nature of wireless communication against interference and intentional jamming, ITS face new challenges to ensure the reliability and the safety of the overall system. In this paper, we expose a class of stealthy attacks - Stuck in Traffic (SiT) attacks - that aim to cause congestion by exploiting how drivers make decisions based on smart traffic signs. An attacker mounting a SiT attack solves a Markov Decision Process problem to find optimal/suboptimal attack policies in which he/she interferes with a well-chosen subset of signals that are based on the state of the system. We apply approximate policy iteration algorithms to derive potent attack policies. We evaluate their performance on a number of systems and compare them to other attack policies including random, myopic and DoS attack policies. The generated policies, albeit suboptimal, are shown to significantly outperform other attack policies as they maximize the expected cumulative reward from the standpoint of the attacker. © 2013 IEEE

Pinball Attacks: Exploiting Channel Allocation In Wireless Networks

As wireless networks continue to grow rapidly denser with the introduction of various wireless-enabled elements, signal interference coupled with limited radio spectrum availability emerges as a significant hindrance to network performance. In order to retain high network throughput, channels must be strategically assigned to nodes in a way that minimizes signal overlap between neighboring nodes. Current static channel assignment techniques are intolerant of network variations and growth, but flexible dynamic techniques are becoming more feasible with the introduction of software defined networks and network function virtualization. As network maintenance tasks are increasingly handled by software, however, network stability becomes susceptible to malicious behavior. In this paper, we adopt an attacker\u27s prespective and expose stealthy attacks - which we coin pinball Attacks - that aim to trigger unnecessary channel switching behavior in a network and increase signal interference between neighboring nodes. We develop a Markov Decision Process (MDP) framework and investigate suboptimal attack policies applied to a number of real-world topologies. We derive attack policies as approximate MDP solutions due to the exponentially large state space. Our results show that pinball attack outperforms other attack policies such as Denial of Service, Random, and other heuristic policies

A Unifying Approach For The Identification Of Application-Driven Stealthy Attacks On Mobile Cps

Cyber-Physical Systems (CPS) employing mobile nodes rely on wireless communication in many critical applications. Due to interference and intentional jamming by adversaries, mobile nodes may fail to communicate with each other causing severe performance consequences. In this paper, we present a unifying approach for identifying attacks that target Mobile CPS applications. The attack policies are obtained as solutions to Markov Decision Process (MDP) problems, in which a decision to interfere with a signal on a given link is based on the current state of the system. Through applying approximate policy iteration methods, efficient attack policies that only interfere with a selective set of signals between the mobile nodes are derived to maximize damage while minimizing exposure and detection. The proposed approach is instantiated on pheromone-based coordination methods that are used in reconnaissance, surveillance, and search missions in military operations. The identified attack policies are shown to be more potent than other attack policies, including myopic, heuristic, and Denial of Service (DoS) policies

Adaptive Topologies Against Jamming Attacks In Wireless Networks: A Game-Theoretic Approach

Towards securing wireless networks and ensuring their dependability under jamming attacks, this paper presents and analyzes game-theoretic formulations between an adversary and a defender. The adversary jams a subset of nodes to increase the level of interference in the network, while the defender makes judicious adjustments of the transmission power level of the nodes, thereby continuously adapting the underlying network topology to reduce the impact of the attack. The defender\u27s strategy is based on playing Nash equilibria (NE) strategies securing a worst-case network utility. First, a discrete control set is considered in which the space of strategies of the defender grows exponentially with the network size. Scalable decomposition-based approaches are developed yielding a defense strategy whose performance closely approaches that of the non-decomposed game. Second, we develop a marginal strategy to assign the power levels while satisfying a coverage constraint thereby evading the combinatorial complexity associated with enumerating all pure actions. Third, we generalize the marginal-based strategy by considering a continuous action space for both players. For this setting, we prove the existence of a unique pure Nash equilibrium. The presented numerical results show the effectiveness of the proposed defense approach against various attack policies and demonstrate the assignment strategies on a real wireless network deployed in a three-story building