Replay-based Recovery for Autonomous Robotic Vehicles from Sensor Deception Attacks

Sensors are crucial for autonomous operation in robotic vehicles (RV). Physical attacks on sensors such as sensor tampering or spoofing can feed erroneous values to RVs through physical channels, which results in mission failures. In this paper, we present DeLorean, a comprehensive diagnosis and recovery framework for securing autonomous RVs from physical attacks. We consider a strong form of physical attack called sensor deception attacks (SDAs), in which the adversary targets multiple sensors of different types simultaneously (even including all sensors). Under SDAs, DeLorean inspects the attack induced errors, identifies the targeted sensors, and prevents the erroneous sensor inputs from being used in RV's feedback control loop. DeLorean replays historic state information in the feedback control loop and recovers the RV from attacks. Our evaluation on four real and two simulated RVs shows that DeLorean can recover RVs from different attacks, and ensure mission success in 94% of the cases (on average), without any crashes. DeLorean incurs low performance, memory and battery overheads

Optimal energy portfolio method for regulable hydropower plants under the spot market

The energy allocation method for regulable hydropower plants under the spot market significantly impacts their income. The available studies generally draw on the Conditional Value-at-Risk (CVaR) approach, which typically assumes a fixed risk aversion coefficient for generators. This assumption is based on the assumption that the total energy the power plant can allocate is constant during the decision period. However, the amount of energy that the regulable hydropower plant can generate will be affected by inflow and water level during the decision period, and the assumption of the fixed risk aversion coefficient is only partially consistent with the actual decision behavior of the hydropower plant. In this regard, the time-varying relative risk aversion (TVRRA) based method is proposed for the energy allocation of regulable hydropower plants. That method takes the change value of the hydropower plant’s energy generation as the basis for adjusting the time-varying relative risk aversion coefficient to make the energy allocation results more consistent with the actual decision-making needs of the hydropower plant. A two-layer optimal method is proposed to obtain the income-maximizing energy portfolio based on regulable hydropower plants’ time-varying relative risk aversion coefficient. The inner point method solves the optimal energy portfolio of income and risk in the upper layer. The time-varying relative risk aversion coefficient in the lower layer accurately describes the dynamic risk preference of hydropower plants for each period. The results and comparison show that the proposed method increases the income of the energy portfolio by 31%, and water disposal of regulated hydropower plants is reduced by 2%. The energy portfolio optimization method for regulable hydropower plants proposed in this paper not only improves the economic income of hydropower plants but also improves the utilization rate of hydro energy resources and enhances the market competitiveness of regulable hydropower plants

DeepDyve: Dynamic Verification for Deep Neural Networks

Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, however, suffer from various kinds of threats, such as adversarial example attacks and fault injection attacks. While there are many defense methods proposed against maliciously crafted inputs, solutions against faults presented in the DNN system itself (e.g., parameters and calculations) are far less explored. In this paper, we develop a novel lightweight fault-tolerant solution for DNN-based systems, namely DeepDyve, which employs pre-trained neural networks that are far simpler and smaller than the original DNN for dynamic verification. The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much. We develop efficient and effective architecture and task exploration techniques to achieve optimized risk/overhead trade-off in DeepDyve. Experimental results show that DeepDyve can reduce 90% of the risks at around 10% overhead

Effect of Biocontrol Agent Pseudomonas fluorescens 2P24 on Soil Fungal Community in Cucumber Rhizosphere Using T-RFLP and DGGE

Fungi and fungal community play important roles in the soil ecosystem, and the diversity of fungal community could act as natural antagonists of various plant pathogens. Biological control is a promising method to protect plants as chemical pesticides may cause environment pollution. Pseudomonas fluorescens 2P24 had strong inhibitory on Rastonia solanacearum, Fusarium oxysporum and Rhizoctonia solani, etc., and was isolated from the wheat rhizosphere take-all decline soils in Shandong province, China. However, its potential effect on soil fungal community was still unknown. In this study, the gfp-labeled P. fluorescens 2P24 was inoculated into cucumber rhizosphere, and the survival of 2P24 was monitored weekly. The amount decreased from 108 to 105 CFU/g dry soils. The effect of 2P24 on soil fungal community in cucumber rhizosphere was investigated using T-RFLP and DGGE. In T-RFLP analysis, principle component analysis showed that the soil fungal community was greatly influenced at first, digested with restriction enzyme Hinf I and Taq I. However, there was little difference as digested by different enzymes. DGGE results demonstrated that the soil fungal community was greatly shocked at the beginning, but it recovered slowly with the decline of P. fluorescens 2P24. Four weeks later, there was little difference between the treatment and control. Generally speaking, the effect of P. fluorescens 2P24 on soil fungal community in cucumber rhizosphere was just transient

Understanding and modeling error propagation in programs

Hardware errors are projected to increase in modern computer systems due to shrinking feature sizes and increasing manufacturing variations. The impact of hardware faults on programs can be catastrophic, and can lead to substantial financial and societal consequences. Error propagation is often the leading cause of catastrophic system failures, and hence must be mitigated. Traditional hardware only techniques to avoid error propagation are energy hungry, and hence not suitable for modern computer systems (i.e., commodity systems). Researchers have proposed selective software-based protection techniques to prevent error propagation at lower costs. However, these techniques use expensive fault injection simulations to determine which parts of a program must be protected. Fault injection simulation artificially introduces a fault to program execution and observe failures (if any) upon the completion of the program execution. Thousands of such simulations need to be performed in order to achieve statistical significance. It is time-consuming as even a single program execution of a common application may take a long time. In this dissertation, I first characterize error propagation in programs that lead to different types of failures, proposed both empirical and analytical approaches to identify and mitigate error propagation without expensive fault injections. The key observation is that only a small fraction of states are responsible for almost all error propagation in programs, and the propagation falls into identifiable patterns which can be modeled efficiently. The proposed techniques are nearly as close as fault injection approaches in measuring failure rates of programs, and orders of magnitude faster than fault injections. This allows developers to build low-cost fault-tolerant applications in an extremely efficient manner.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat