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共 8225 条记录，以下是141-150 订阅

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Gadgets of Gadgets in Industrial Control systems: Return Oriented programming Attacks on PLCs

Gadgets of Gadgets in Industrial Control Systems: Return Ori...

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IEEE international symposium on Hardware Oriented Security and Trust (HOST)

作者： Ayub, Adeen Zubair, Nauman Yoo, Hyunguk Jo, Wooyeon Ahmed, Irfan Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA Univ New Orleans Dept Comp Sci New Orleans LA USA

ISBN: (纸本)9798350300628

In industrial control systems (ICS), programmable logic controllers (PLCs) directly control and monitor physical processes in real-time such as nuclear plants, and power grid stations. Adversaries typically transfer malicious control logic to PLCs over the network to sabotage a physical process. These control logic attacks are well-understood containing machine instructions in network packets and are likely to be detected by network intrusion detection systems (IDS). On the other hand, return-oriented programming (ROP) reuses blocks (or gadgets) of existing code in computer memory to create and execute malicious code. It limits or eliminates the need to transfer machine instructions over the network, making it stealthier. Currently, ROP attacks on control logic has never been discussed in the literature to explore it as a practical ICS attack. This paper is the first attempt in this direction to explore challenges for a successful ROP attack on real-world PLCs, including maintaining a continuous (control logic) scan cycle through ROP gadgets, no user input (to cause a buffer overflow) to overwrite the stack for gadget installation, and limited ROP gadgets in a PLC memory to find blocks of instructions equivalent to the high-level constructs of PLC programming languages (such as instruction list, and ladder logic). We identify and utilize typical PLC design features (that we find exploitable) to overcome these challenges, which makes ROP attacks applicable to most PLCs e.g., no stack protection, and remote access to certain PLC memory regions via ICS protocols. We demonstrate two successful ROP attacks on the control logic programs of three fully-functional physical processes, i.e., a belt conveyor system, a four-floor elevator, and a compact traffic light system. The first ROP attack manipulates a PLC's current control logic and has two variants involving either a single or multiple gadgets;the second ROP attack constructs a control logic from scratch using gadgets

关键词： ROP PLCs ICS attacks control logic

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Comparative Study of Modified Dynamic A-Star programming and A-Star for Mobile Robot Path Planning 8th

Comparative Study of Modified Dynamic A-Star Programming and...

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8th IFToMM international symposium on Robotics and Mechatronics (ISRM)

作者： Khlif, Nessrine Khraief, Nahla Belghith, Safya ENIT UTM RISC Lab LR16ES07 Tunis 1002 Tunisia

ISBN: (纸本)9783031598906;9783031598883;9783031598876

Navigating autonomously through complex environments is a critical capability for mobile robots, enabling them to perform various tasks in a wide range of applications. However, navigating effectively in the presence of obstacles presents a significant challenge for these intelligent systems. This paper delves into the intricacies of mobile robot navigation. At the heart of mobile robot navigation lies the ability to construct traversable paths between designated start and goal points. This intricate process involves evaluating various alternative routes and selecting the one that minimizes the overall cost, typically measured in terms of distance or time. Two prominent path planning algorithms, namely A* and modified dynamic A*, are thoroughly investigated within this paper. A* stands out as a widely used and well-established algorithm that employs a heuristic approach to efficiently find optimal paths. However, in scenarios with dynamic environments, where obstacles may appear or disappear, A* can struggle to adapt effectively. To address this limitation, a modified dynamic A* algorithm is proposed, incorporating dynamic programming techniques that enable it to adapt to changes in the environment without compromising path efficiency. To evaluate the performance of these two algorithms, a simulation environment is employed, replicating a scenario with obstacles. The results demonstrate that the modified dynamic A* algorithm consistently outperforms A* in terms of path planning efficiency.

关键词： Mobile robotic Path planning A-star Dynamic programming Reinforcement learning

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Let-Me-In: (Still) Employing In-pointer Bounds Metadata for Fine-grained GPU Memory Safety 31

Let-Me-In: (Still) Employing In-pointer Bounds Metadata for ...

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31st IEEE international symposium on High Performance Computer Architecture, HPCA 2025

作者： Lee, Jaewon Chung, Euijun Singh, Saurabh Na, Seonjin Kim, Yonghae Lee, Jaekyu Kim, Hyesoon Georgia Institute of Technology United States Arm Intel United States

ISBN: (纸本)9798331506476

The importance of ensuring the robustness of GPU systems has grown significantly, especially as GPUs have become vital in critical decision-making systems such as autonomous driving and medical diagnostics. However, GPU programming languages, primarily based on C/C++, inherit memory vulnerabilities that threaten the robustness of GPU applications. The heterogeneous GPU memory hierarchy makes it more difficult to find effective universal solutions. While several studies have proposed advanced GPU memory safety mechanisms, they still grapple with significant challenges, including substantial metadata storage and access overhead, elevated hardware implementation costs, and limited security coverage, particularly regarding fine-grained memory safety. We address this issue with Let-Me-In (LMI), a fine-grained memory safety mechanism specifically designed for GPUs. LMI features an efficient hardware bounds-checking mechanism that ensures negligible impact on performance and hardware costs, even in scenarios where thousands of concurrent threads perform memory operations across buffers in heap and local memory. This is achieved by aligning memory allocation to powers of two and performing static analysis to identify and mark pointer arithmetic instructions. This approach also enables storing metadata inside the unused upper bits of pointers, which are shrinking due to the expansion of the virtual memory address space. The unique characteristics of GPU programs make this approach feasible, unlike in CPU programs, where the inherent complexity of programs poses challenges. Our evaluation shows that LMI incurs only negligible hardware and performance overhead, making it a practical and efficient solution for enhancing GPU memory safety. © 2025 IEEE.

关键词： C++ (programming language)

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Characterizing Reliability of Three -version Traffic Sign Classifier System through Diversity Metrics 34

Characterizing Reliability of Three -version Traffic Sign Cl...

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34th IEEE international symposium on Software Reliability Engineering (ISSRE)

作者： Wen, Qiang Machida, Fumio Univ Tsukuba Dept Comp Sci Tsukuba Ibaraki Japan

ISBN: (纸本)9798350315943

The N-version machine learning (ML) system is an architecture approach to enhance the reliability of ML system outputs by exploiting ML model diversity and input data diversity. While existing studies theoretically show the relation between diversity metrics and system reliability, there is a shortage of empirical studies validating reliability models with diversity parameters in real datasets. In this paper, focusing on traffic sign recognition tasks, we empirically analyze the impact of diversity parameter estimations for predicting the reliability of three-version traffic sign classifier systems. Using five real-world traffic sign datasets, we confirm that the three-version architecture effectively enhances system reliability by applying diverse models and diversified input images. Then, we estimate the diversity parameters and apply them to variants of reliability prediction models. The prediction residuals between the observed reliability and the predicted reliability are mostly less than 0.017 across all data sets, which is half of the residual achieved by the conventional prediction model, except for the architecture of a single model with triple input. As the estimated values of diversity parameters tend to be stable with a relatively small number of samples, we consider that the reliability prediction models using diversity parameters are useful in the early-stage design of ML systems.

关键词： Classification Diversity Machine learning system N-version programming Reliability

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Edge-Parallel Graph Encoder Embedding

Edge-Parallel Graph Encoder Embedding

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1st international Conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Lubonja, Ariel Shen, Cencheng Priebe, Carey Burns, Randal Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Univ Delaware Dept Appl Econ & Stat Newark DE USA Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD USA

ISBN: (纸本)9798350364613;9798350364606

New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations. One-Hot Graph Encoder Embedding (GEE) uses a single, linear pass over edges and produces an embedding that converges asymptotically to the spectral embedding. The scaling and performance benefits of this approach have been limited by a serial implementation in an interpreted language. We refactor GEE into a parallel program in the Ligra graph engine that maps functions over the edges of the graph and uses lock-free atomic instructions to prevent data races. On a graph with 1.86 edges, this results in a 500 times speedup over the original implementation and a 17 times speedup over a just-in-time compiled version.

关键词： graph embedding graph processing parallel programming

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Neon: A Multi-GPU programming Model for Grid-based Computations 36

Neon: A Multi-GPU Programming Model for Grid-based Computati...

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36th IEEE international Parallel and Distributed Processing symposium (IEEE IPDPS)

作者： Meneghin, Massimiliano Mahmoud, Ahmed H. Jayaraman, Pradeep Kumar Morris, Nigel J. W. Autodesk Res Toronto ON Canada Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781665481069

We present Neon, a new programming model for grid-based computation with an intuitive, easy-to-use interface that allows domain experts to take full advantage of single-node multi-GPU systems. Neon decouples data structure from computation and back end configurations, allowing the same user code to operate on a variety of data structures and devices. Neon relies on a set of hierarchical abstractions that allow the user to write their applications as if they were sequential applications, while the runtime handles distribution across multiple GPUs and performs optimizations such as overlapping computation and communication without user intervention. We evaluate our programming model on several applications: a Lattice Boltzmann fluid solver, a finite-difference Poisson solver and a finite-element linear elastic solver. We show that these applications can be implemented concisely and scale well with the number of GPUsachieving more than 99% of ideal efficiency.

关键词： Performance evaluation Neon Runtime Fluids Computational modeling programming Data structures

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Recovering Accuracy of RRAM-based CIM for Binarized Neural Network via Chip-in-the-loop Training

Recovering Accuracy of RRAM-based CIM for Binarized Neural N...

引用

IEEE international symposium on Circuits and systems (ISCAS)

作者： Chong, Yi Sheng Goh, Wang Ling Ong, Yew Soon Nambiar, Vishnu P. Do, Anh Tuan Nanyang Technol Univ NTU Sch Elect & Elect Engn Singapore Singapore Energy Res Inst Interdisciplinary Grad Programme Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore ASTAR Inst Microelect Singapore Singapore

ISBN: (纸本)9781665484855

Resistive random access memory (RRAM) based computing-in-memory (CIM) is attractive for edge artificial intelligence (AI) applications, thanks to its excellent energy efficiency, compactness and high parallelism in matrix vector multiplication (MatVec) operations. However, existing RRAM-based CIM designs often require complex programming scheme to precisely control the RRAM cells to reach the desired resistance states so that the neural network classification accuracy is maintained. This leads to large area and energy overhead as well as low RRAM area utilization. Hence, compact RRAMbased CIM with simple pulse-based programming scheme is thus more desirable. To achieve this, we propose a chip-in-the-loop training approach to compensate for the network performance drop due to the stochastic behavior of the RRAM cells. Note that, although the target RRAM cell here is a two-state RRAM (i.e binary, having only high and low resistance states), their inherent analog resistance values are used in the CIM operation. Our experiment using a 4-layer fully-connected binary neural network (BNN) showed that after retraining, the RRAM-based network accuracy can be recovered, regardless of the RRAM resistance distribution and RHRS RLRS resistance ratio.

关键词： Resistance Training Neural networks Resistive RAM programming Writing Parallel processing

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Sequential Hypothesis Testing for Markov Chains

Sequential Hypothesis Testing for Markov Chains

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2023 IEEE international symposium on Information Theory, ISIT 2023

作者： Fields, Greg Javidi, Tara Uc San Diego United States

ISBN: (纸本)9781665475549

We consider sequential hypothesis tests for Markov chains in which the investigator may adaptively abstain from sampling on each time step in exchange for a reduced sampling cost. This allows the investigator to strategically choose to allow the chain to evolve until it is likely in a state which discriminates well among the hypotheses-when it becomes worth paying the full sampling cost. We first explore the reduction of this problem formulation to several standard hypothesis testing scenarios via an appropriate choice of parameters. Then we derive a dynamic programming characterization of the optimal algorithm in this setting and solve it numerically for several model problems that exhibit the rich, interesting behavior that arise from this modification of the hypothesis testing problem. © 2023 IEEE.

关键词： Dynamic programming

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Reduced Order Modeling of a Heat Exchanger with a Stacking Ensemble to reduce Computational Inefficiencies 8

Reduced Order Modeling of a Heat Exchanger with a Stacking E...

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international symposium on systems Engineering (ISSE)

作者： Chandran, Vinayak Vijaya Adepu, Roopa Collins Aerosp Appl Res & Technol ART Bengaluru India

ISBN: (纸本)9781665481823

Reduced Order Modeling is a technique for reducing the computational complexity of a model while preserving the expected fidelity within a controlled error. One of the techniques used to create a Reduced Order Model ( ROM) is Artificial Neural Networks (ANN). A successful approach to reducing the variance of ANN model prediction is to train multiple models instead of a single model and to combine the predictions from these models, which is commonly called Ensemble learning. When the predictions from the multiple models are combined using another regression model, it is called Stacking ensemble. This paper studies the effectiveness of using Genetic programming algorithm in taking the outputs of each model as input and attempting to learn how to best combine the input predictions to make a better output prediction. The above-mentioned approach is used to create a ROM for a crossflow heat exchanger steady-state component. There are 6 inputs parameters namely Cold & Hot inlet temperature, Cold & Hot outlet pressure and Cold & Hot inlet flow. There are four outputs namely Hot & Cold outlet temperature and Hot & Cold inlet pressure. A multi-input single output ( MISO) ROM is created for each of the outputs. There are 3 different configurations of ANNs used to cover a good range of the Hyperparameter values. The output from each of the ANNs is then combined using Genetic programming Algorithm. The Overall model has an R2 value of above 95% for each of the outputs. The ROM thus created can run simulations at a much faster rate. The ROM of the HX component is a black box and can be shared with third party without any concerns over propriety information loss.

关键词： Artificial Neural Networks Genetic programming Ensemble learning Stacking Ensemble Reduced Order Modeling Random Sampling technique

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Weighted Least-Squares Design of 2-D IIR Filters with Arbitrary Frequency Response using Iterative Second-Order Cone programming

Weighted Least-Squares Design of 2-D IIR Filters with Arbitr...

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IEEE international symposium on Circuits and systems (ISCAS)

作者： Pakiyarajah, Darukeesan Dissanayake, Nadeeshan D. K. Edussooriya, Chamira U. S. Wijenayake, Chamith Madanayake, Arjuna Univ Jaffna Dept Elect & Elect Engn Killinochchi Sri Lanka Univ Moratuwa Dept Elect & Telecommun Engn Moratuwa Sri Lanka Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia Florida Int Univ Dept Elect & Comp Engn Miami FL USA

ISBN: (纸本)9781665484855

Two-dimensional (2-D) infinite-extent impulse response (IIR) filter design is a challenging problem due to the difficulty in verifying stability. Optimization methods proposed so far predominantly consider the design of 2-D IIR filters having quadrantally-symmetric frequency responses, where the transfer functions have separable denominators. In this paper, we propose a weighted least-squares (WLS) design method for 2-D IIR filters having arbitrary frequency response and stable in the practical bounded-input bounded-output (P-BIBO) sense. Our design considers transfer functions with nonseparable numerators and denominators having complex- and real-valued coefficients, respectively. We formulate the WLS design as an iterative second-order cone programming problem, which includes constraints to guarantee the P-BIBO stability. Design examples confirm that the proposed WLS method leads to P-BIBO stable 2-D IIR filters.

关键词： Two-dimensional complex-coefficient IIR filters least-squares design second-order cone programming

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