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Field-Based Security Testing of SDN Configuration Updates

IEEE TRANSACTIONS ON RELIABILITY 2025年

作者： Malik, Jahanzaib Pastore, Fabrizio Univ Luxembourg SnT Ctr L-4365 Esch Sur Alzette Luxembourg

software-defined systems revolutionized the management of hardware devices but introduced quality assurance challenges that remain to be tackled. For example, software defined networks (SDNs) became a key technology for the prompt reconfigurations of network services in many sectors including telecommunications, data centers, financial services, cloud providers, and manufacturing industry. Unfortunately, reconfigurations may lead to mistakes that compromise the dependability of the provided services. In this article, we focus on the reconfigurations of network services in the satellite communication sector, and target security requirements, which are often hard to verify;for example, although connectivity may function properly, confidentiality may be broken by packets forwarded to a wrong destination. We propose an approach for FIeld-based Security Testing of SDN Configurations Updates (FISTS). First, it probes the network before and after configuration updates. Then, using the collected data, it relies on unsupervised machine learning algorithms to prioritize the inspection of suspicious node responses, after identifying the network nodes that likely match across the two configurations. Our empirical evaluation has been conducted with network data from simulated and real SDN configuration updates for our industry partner, a world-leading satellite operator. Our results show that, when combined with K-Nearest Neighbor, FISTS leads to best results (up to 0.95 precision and 1.00 recall). Further, we demonstrated its scalability.

关键词： Clustering algorithms Testing Security software Nearest neighbor methods Anomaly detection Partitioning algorithms Inspection software algorithms Machine learning algorithms Field-based testing software defined network (SDN) security testing upgrades testing

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What Machine Learning Can Learn From software Modularity

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COMPUTER 2022年第9期55卷 35-42页

作者： Kriens, Peter Verbelen, Tim aQute Ikoyi Ile Nigeria Univ Ghent imec B-9062 Ghent Belgium

More program functions are no longer written in code but learned from a huge number of data samples using a machine learning (ML) algorithm. We present an overview of current techniques to manage complex software and ... 详细信息

关键词： Machine Learning algorithms Codes Computational Modeling software algorithms Machine Learning software Development Management

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Optimizing Residential Energy Costs: A Novel Machine Learning Approach for Solar PV and EV Integration Through Heuristic Price Signal Dispatch

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第3期61卷 4684-4694页

作者： Irfan, Muhammad Tahir, Tayyab Huang, Shujuan Deilami, Sara Veettil, Binesh Puthen Macquarie Univ Sch Engn Sydney NSW 2113 Australia Univ Toulon & Var Dept SeaTech MIR Erasmus Mundus Program F-83130 La Garde France

The integration of solar Photovoltaic (PV) systems with Electric Vehicle (EV) technology is emerging as a sustainable and promising method to cope with increasing energy demands, mitigate environmental impact, and reduce carbon emissions within residential and transportation sectors. This study focuses on the residential application of a grid-connected "PV+EV" system in Sydney, Australia, underscoring the benefits of using bi-directional vehicle batteries in conjunction with rooftop PV systems. In addition to heuristic price signal dispatch algorithms in the System Advisor Model (SAM) software tool, which rely on manual dispatch and peak shaving analyses, a novel Q-Learning-Based-Model (QLBM) algorithm within the domain of machine learning methodology is employed to enhance the understanding of system dynamics. This novel approach is designed to predict optimal energy efficiency by prioritizing the most cost-effective energy source, thereby alleviating grid stress, minimizing energy costs. The results are then compared to other techniques employed in this paper, affirming the superiority of the proposed algorithm.

关键词： Costs Electricity Batteries Prediction algorithms Mathematical models software algorithms Home appliances Vehicle-to-grid Load modeling Heuristic algorithms Carbon emissions electric vehicle (EV) energy efficiency machine learning residential sector solar photovoltaic (PV)

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Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing

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IEEE ACCESS 2024年 12卷 130444-130459页

作者： Szostak, Hadar Cohen, Kobi Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel

We consider a decentralized formulation of the active hypothesis testing (AHT) problem, where multiple agents gather noisy observations from the environment with the purpose of identifying the correct hypothesis. At each time step, agents have the option to select a sampling action. These different actions result in observations drawn from various distributions, each associated with a specific hypothesis. The agents collaborate to accomplish the task, where message exchanges between agents are allowed over a rate-limited communications channel. The objective is to devise a multi-agent policy that minimizes the Bayes risk. This risk comprises both the cost of sampling and the joint terminal cost incurred by the agents upon making a hypothesis declaration. Deriving optimal structured policies for AHT problems is generally mathematically intractable, even in the context of a single agent. As a result, recent efforts have turned to deep learning methodologies to address these problems, which have exhibited significant success in single-agent learning scenarios. In this paper, we tackle the multi-agent AHT formulation by introducing a novel algorithm rooted in the framework of deep multi-agent reinforcement learning. This algorithm, named Multi-Agent Reinforcement Learning for AHT (MARLA), operates at each time step by having each agent map its state to an action (sampling rule or stopping rule) using a trained deep neural network with the goal of minimizing the Bayes risk. We present a comprehensive set of experimental results that effectively showcase the agents' ability to learn collaborative strategies and enhance performance using MARLA. Furthermore, we demonstrate the superiority of MARLA over single-agent learning approaches. Finally, we provide an open-source implementation of the MARLA framework, for the benefit of researchers and developers in related domains.

关键词： Costs Testing Bayes methods Collaboration Task analysis software algorithms Noise measurement Deep reinforcement learning Multi-agent systems Active hypothesis testing (AHT) controlled sensing for multihypothesis testing decentralized inference deep reinforcement learning (DRL) multi-agent learning

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Ensuring High Visibility of Passive Markers at Fixed Camera Gain Using Divide and Conquer Method

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第11期9卷 9741-9748页

作者： Retinger, Marek Michalski, Jacek Kozierski, Piotr Drapikowski, Pawel Poznan Univ Tech Inst Robot & Machine Intelligence PL-60965 Poznan Poland

The letter presents a novel solution to determine exposure and threshold values for cameras in motion capture systems without excessive interaction with the user. The solution is based on the divide and conquer method, which ensures a fast and efficient search of the values. As the results have shown, users without specialist knowledge can significantly improve the tracking capabilities of the motion capture system, especially for smaller passive markers. The tests have proved that for spherical markers with a diameter of 7.9 mm, the full time tracking capabilities can be ensured based on the settings determined with the proposed method, what is difficult to achieve with the default settings. Moreover, the cameras utilisation can be increased, which should have a positive effect on the overall tracking quality. This makes it possible to use smaller and lighter-weight markers, which is desirable for small flying units with payload capacity of a few grams only. The primary tests were performed in the laboratory equipped with 12 OptiTrack Prime(x) 13W cameras. The dedicated programming interfaces (Motive API and Camera SDK) were used. The validation tests included a DJI Tello EDU unit with four markers attached. In addition to analysis and considerations, the document includes pseudocodes that clearly explain the idea behind the algorithms and allow for an easy implementation of the solution.

关键词： Cameras Indexes Robot vision systems Motion capture Tracking Accuracy software algorithms Visual tracking methods and tools for robot system design motion capture divide and conquer algorithm

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A Scalable t-Wise Coverage Estimator: algorithms and Applications

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IEEE TRANSACTIONS ON software ENGINEERING 2024年第8期50卷 2021-2039页

作者： Baranov, Eduard Chakraborty, Sourav Legay, Axel Meel, Kuldeep S. Vinodchandran, N. Variyam Catholic Univ Louvain UClouvain B-1348 Louvain La Neuve Belgium Indian Stat Inst Kolkata 700108 West Bengal India Univ Toronto Toronto ON M5S 1A1 Canada Univ Nebraska Lincoln Lincoln NE 68588 USA

Owing to the pervasiveness of software in our modern lives, software systems have evolved to be highly configurable. Combinatorial testing has emerged as a dominant paradigm for testing highly configurable systems. Often constraints are employed to define the environments where a given system is expected to work. Therefore, there has been a sustained interest in designing constraint-based test suite generation techniques. A significant goal of test suite generation techniques is to achieve t-wise coverage for higher values of t. Therefore, designing scalable techniques that can estimate t-wise coverage for a given set of tests and/or the estimation of maximum achievable t-wise coverage under a given set of constraints is of crucial importance. The existing estimation techniques face significant scalability hurdles. We designed scalable algorithms with mathematical guarantees to estimate (i) t-wise coverage for a given set of tests, and (ii) maximum t-wise coverage for a given set of constraints. In particular, ApproxCov takes in a test set U and returns an estimate of the t-wise coverage of U that is guaranteed to be within (1 +/- epsilon)-factor of the ground truth with probability at least 1-delta for a given tolerance parameter epsilon and a confidence parameter delta. A scalable framework ApproxMaxCov for a given formula F outputs an approximation which is guaranteed to be within (1 +/- epsilon) factor of the maximum achievable t-wise coverage under F , with probability >= 1 - delta for a given tolerance parameter epsilon and a confidence parameter delta. Our comprehensive evaluation demonstrates that ApproxCov and ApproxMaxCov can handle benchmarks that are beyond the reach of current state-of-the-art approaches. In this paper we present proofs of correctness of t.

关键词： Scalability Computational modeling Combinatorial testing Benchmark testing software systems Estimation software algorithms Configurable software t-wise coverage approximation

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Falcon/Kyber and Dilithium/Kyber Network Stack on Nvidia’s Data Processing Unit Platform

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IEEE ACCESS 2024年 12卷 38048-38056页

作者： Lawo, D. C. Frantz, R. Aguilera, A. Cano Clemente, X. Arnal, I Podles, M. P. Imana, Jose L. Monroy, I. Tafur Olmos, J. J. Vegas Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands NVIDIA Corp Software Architecture IL-6121002 Tel Aviv Israel Univ Complutense Madrid Dept Comp Architecture & Automation Madrid 28040 Spain

Commercially available quantum computers are expected to reshape the world in the near future. They are said to break conventional cryptographic security mechanisms that are deeply embedded in our today's communication. Symmetric cryptography, such as AES, will withstand quantum attacks as long as the key sizes are doubled compared to today's key lengths. Asymmetric cryptographic procedures, e.g. RSA, however are broken. It is therefore necessary to change the way we assure our privacy by adopting and moving towards post-quantum cryptography (PQC) principles. In this work, we benchmark three PQC algorithms, Falcon, Dilithium, and Kyber. Moreover, we present an implementation of a PQC stack consisting of the algorithms Dilithium/Kyber and Falcon/Kyber which use hardware accelerators for some key functions and evaluate their performance and resource utilization. Regarding a classic server-client model, the computational load of the Dilithium/Kyber stack is distributed more equally among server and client. The stack Falcon/Kyber biases the computational challenges towards the server, hence relieving the client of performing costly operations. We found that Dilithium's advantage over Falcon is that Dilithium's execution is faster while the workload to be performed is distributed equally among client and server, whereas Falcon's advantage over Dilithium lies within the small signature sizes and the unequally distributed computational tasks. In a client server model with a performance limited client (i.e. Internet-of-Things - IoT - environments) Falcon could proof useful for it constrains the computational hard tasks to the server and leaves a minimal workload to the client. Furthermore, Falcon requires smaller bandwidth, making it a strong candidate for deep-edge or IoT applications.

关键词： Cryptography Servers software algorithms Security Cloud computing Field programmable gate arrays software Quantum computing Client-server systems Data processing units Dilithium falcon Kyber post-quantum cryptography

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Simplifying and Isolating Failure-Inducing Input: A Retrospective on Delta Debugging

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IEEE TRANSACTIONS ON software ENGINEERING 2025年第3期51卷 820-824页

作者： Zeller, Andreas Hildebrandt, Ralf CISPA Helmholtz Ctr Informat Secur D-66133 Saarbrucken Germany Charite D-10117 Berlin Germany

Delta Debugging is a technique to simplify and isolate failure-inducing changes. Its most popular application is on program inputs, where it reduces a failure-inducing input to a minimal input that still triggers the failure. This paper provides a retrospective on Delta Debugging, discussing its origins, applications, and impact. We also discuss the limitations of Delta Debugging and suggest directions for future research.

关键词： Debugging Codes Python software Servers History Computer bugs software algorithms Search problems Manuals Delta debugging debugging software testing fault localization failure-inducing inputs

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A Retrospective on How Developers Seek, Relate, and Collect Information About Code

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IEEE TRANSACTIONS ON software ENGINEERING 2025年第3期51卷 848-851页

作者： Ko, Amy J. Myers, Brad A. Coblenz, Michael Aung, Htet Htet Univ Washington Seattle WA 98195 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif San Diego San Diego CA 92093 USA HHAeXchange Reston VA 20191 USA

In the early 2000s, software development was shifting from offline to online, and from command line to IDE. We discuss our 2004 paper examining the impact of this shift on developers' program comprehension behaviors, our motivation for the work, and it's impact on the last twenty years of empirical studies and developer tool innovations. We end with a discussion of the possible unintended impacts LLMs have on program comprehension in the coming decades.

关键词： Codes Navigation Debugging Cognitive science Cognition Technological innovation Market research Testing software algorithms software software evolution programming environments

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Virtual Testing Methods of Cyber-Physical Systems: A Framework for Testing Instrumentation and Measurement Systems

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2024年第8期27卷 11-15页

作者： Saleh, Mahdi Elhajj, Imad H. Asmar, Daniel Univ Manchester Dept Elect & Elect Engn Manchester England Univ Lincoln Sch Engn Lincoln England Amer Univ Beirut AUB Dept Elect & Comp Engn Beirut Lebanon Amer Univ Beirut AUB Maroun Semaan Fac Engn & Architecture Acad Transformat Beirut Lebanon Amer Univ Beirut Mech Engn Beirut Lebanon

The development of some instrumentation and measurement systems poses significant challenges due to their continuous interaction with environments that are both harsh and highly dynamic. They are often described as "Untestable" because their testing is sometimes expensive, time-consuming, and infeasible. One example is oil-spill measurement systems that aim to measure the thickness of oil floating on the water surface in open water environments. In contrast to analog sensors relying on calibration functions, such integrated measurement systems use algorithms with multiple inputs to produce their measurement. Intending to facilitate the development of such systems, we shed light on virtual testing methods designed for testing Cyber-physical Systems (CPSs). CPSs are smart and autonomous systems composed of collaborating computational elements (software) that control physical entities (hardware). Effective validation and verification techniques are required to confirm their correctness. These methods were applied to test continuous controllers in the automotive domain. In this article, we review some of these testing methods and provide a framework for applying them to measurement systems that are difficult to test in real life. We provide a case study based on an oil spill measurement system that relies on multiple sensors to estimate the oil thickness in open water environments. Applying this approach creates a reduced set of test cases to be applied in real field testing, reducing its cost and time.

关键词： Reviews software algorithms Cyber-physical systems Sensor systems software Vehicle dynamics Intelligent sensors Testing Thickness measurement Autonomous systems Water resources Water monitoring

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