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Self-Adaptive Dual-Layer DDoS Mitigation using Autoencoder and Reinforcement Learning

Self-Adaptive Dual-Layer DDoS Mitigation using Autoencoder a...

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SEAMS international Workshop on software engineering for Adaptive and Self-Managing systems, ICSE

作者： Qi Duan Ehab Al-Shaer David Garlan Software and Societal Systems Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798331501815

ISBN: (纸本)9798331501822

distributed Denial of Service (DDoS) attacks have grown in complexity, with attackers dynamically adapting their strategies to maximize disruption. Dynamic DDoS adversaries evolve their attacks by changing targets, modifying botnet infrastructure, or altering traffic patterns to evade detection and maintain attack effectiveness. This dynamic nature poses significant challenges for DDoS defense, particularly in developing scalable and robust adaptive systems capable of real-time response. This paper introduces a novel, robust, multilayered defense system called DosSink that integrates detection and mitigation through variational autoencoders (VAE) and actor-critic deep reinforcement learning (DRL). The VAE effectively reduces the feature space which may make the learning intractable and characterizes traffic to estimate the risk score for each flow. At the same time, the DRL agent uses these risk scores to optimize mitigation policies that include traffic limiting, flow redirection, or puzzle-based source verification actions. Feedback from puzzle inquiries refines VAE risk assessments, enhancing detection accuracy. Key innovations of this framework include (1) the VAE's adaptability as an anomaly detector that evolves with DRL actions, avoiding reliance on static rules or predefined thresholds and enhancing the robustness of the overall system adaptation; (2) the separation of traffic characterization (VAE) and decisionmaking (DRL), improving scalability by reducing the state space; and (3) real-time adaptability to evolving attackers' strategies through dynamic collaboration between the VAE and DRL. Our evaluation experiments show that this framework accurately identifies malicious traffic flows, with a true positive rate of over 98 % and a false positive rate below 1 %. Moreover, it efficiently learns the optimal mitigation strategy in under 20,000 episodes across most experimental settings.

关键词： Technological innovation Adaptive systems Prevention and mitigation Autoencoders Computer architecture Traffic control Deep reinforcement learning Real-time systems Computer crime Tuning

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Packet Format Detection and Modulation Classification of Wireless LAN Using distributed Convolutional Neural Network

Packet Format Detection and Modulation Classification of Wir...

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2021 international symposium on Intelligent Signal Processing and Communication systems, ISPACS 2021

作者： Putra, Dody Ichwana Pratama, Muhammad Harry Bintang Lanante, Leonardo Grad, Hiroshi Ochi Kyushu Institute of Technology School of Computer Science and System Engineering Iizuka Japan

ISBN: (纸本)9781665419512

This paper proposes a method to detect the packet format and classify signal modulation type of wireless LAN signals using the distributed model of Convolutional Neural Network (CNN). The main advantages of this method compared to the conventional are the high accuracy of the model and the flexibility for lowering the complexity. These are achieved because the CNN models are trained to perform multiple small classification tasks instead of a single big classification task. This method makes retraining much easier. The five-fold cross-validation is applied to assess the performance for training and testing the model. To validate the model, actual Wi-Fi signals are used to test the model using software Define Radio (SDR). The result shows the proposed method can accurately classify different packet formats and signal modulation types above 90%, even when different timing offsets occur. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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PLC Testbench: a modular tool for the study and comparison of audio Packet Loss Concealment algorithms

PLC Testbench: a modular tool for the study and comparison o...

引用

Internet of Sounds, international symposium on the

作者： Luca Vignati Stefano Dallona Luca Turchet Dept. of Information Engineering and Computer Science University of Trento Trento Italy

Whenever time-critical applications supporting re-mote interactions are deployed on the Internet they may face the issue of packet loss. This is particularly true for remote interactions that are audio-based and require low latency and high reliability, such as those enabled by networked music performance systems, where packet losses are detrimental to the quality of experience of the users. Packet Loss Concealment (PLC) for audio signals is then mandatory to cope with such an issue. PLC is a technique that aims at masking the effect of packet loss on human auditory perception. Many PLC algorithms have been developed over the last three decades, initially focusing on speech and more recently addressing broadband audio. However, to date, developers of PLC methods face the issue of the lack of tools supporting the design, comparison, and testing of such methods. To bridge this gap, this paper introduces PLC Testbench, a software tool that enables researchers to study, compare, and evaluate various PLC algorithms for audio. The software is modular and comprises a Python backend and a web user interface. It is open-source and is conveniently distributed as a docker container.

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vTrain: A Simulation Framework for Evaluating Cost-Effective and Compute-Optimal Large Language Model Training

vTrain: A Simulation Framework for Evaluating Cost-Effective...

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IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Jehyeon Bang Yujeong Choi Myeongwoo Kim Yongdeok Kim Minsoo Rhu KAIST School of Electrical Engineering Samsung Advanced Institute of Technology

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

As large language models (LLMs) become widespread in various application domains, a critical challenge the AI community is facing is how to train these large AI models in a cost-effective manner. Existing LLM training plans typically employ a heuristic based parallel training strategy which is based on empirical observations rather than grounded upon a thorough examination of the search space of LLM parallelization. Such limitation renders existing systems to leave significant performance left on the table, wasting millions of dollars worth of training cost. This paper presents our profiling-driven simulator called vTrain, providing AI practitioners a fast yet accurate software framework to determine an efficient and cost-effective LLM training system configuration. We demonstrate vTrain's practicality through several case studies, e.g., effectively evaluating optimal training parallelization strategies that balances training time and its associated training cost, efficient multi-tenant GPU cluster schedulers targeting multiple LLM training jobs, and determining a compute-optimal LLM model architecture given a fixed compute budget.

关键词： Training Costs Microarchitecture Accuracy Scheduling algorithms Computational modeling Large language models Graphics processing units Computer architecture software

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Inducing Lattices in Non-Lattice-Linear Problems

Inducing Lattices in Non-Lattice-Linear Problems

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Reliable distributed systems

作者： Arya Tanmay Gupta Sandeep S Kulkarni Computer Science and Engineering Michigan State University

Lattice-linearity was introduced as modelling problems using predicates that induce a lattice among the global states (Garg, SPAA 2020). Such modelling enables permitting asynchronous execution in multiprocessor systems. A key property of the predicate representing such problems is that it induces one lattice in the state space. Such representation guarantees the execution to be correct even if nodes execute asynchronously. However, many interesting problems do not exhibit lattice-linearity. This issue was alleviated with the introduction of eventually lattice-linear algorithms (Gupta and Kulkarni, SSS 2021). They induce single or multiple lattices in a subset of the state space even when the problem cannot be defined by a predicate under which the states form a lattice. In this paper, we focus on analyzing and differentiating between lattice-linear problems and algorithms. We introduce a new class of algorithms called fully lattice-linear algorithms. These algorithms partition the entire reachable state space into one or more lattices. For illustration, we present lattice-linear self-stabilizing algorithms for minimal dominating set (MDS) and graph colouring (GC) problems, and a parallel processing lattice-linear 2-approximation algorithm for vertex cover (VC). The algorithms for MDS and GC converge in $n$ moves and $n+2m$ moves respectively. These algorithms preserve this time complexity while allowing the nodes to execute asynchronously, where these nodes may execute based on old or inconsistent information about their neighbours. The algorithm for VC is the first lattice-linear approximation algorithm for an NP-Hard problem; it converges in $n$ moves.

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Big data collaborative artificial intelligence and high-performance computing to drive physical design of fusion

Big data collaborative artificial intelligence and high-perf...

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2022 international Conference on Cloud Computing, Big Data Applications and software engineering, CBASE 2022

作者： Liu, Pan Liu, Gongjie Liu, Jie Liu, Chencheng Xiong, Wenbin Tang, Zhangcun Zhou, Yinzhao Wang, Yonggang Yuan, Zhouming Beijng Paratera Technology Corp. Ltd Beijing100000 China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha410073 China National University of Defense Technology Laboratory of Software Engineering for Complex Systems Changsha410073 China Central South University of Forestry and Technology Changsha410000 China Nuclear and Radiation Safety Center Mee Beijing100000 China University of Electronic Science and Technology of China Chengdu610000 China Tianfu Innovation Energy Establishment Chengdu610000 China

ISBN: (纸本)9798350396393

Controlled thermonuclear fusion has always been a dream pursued by mankind. However, the physical processes of controlled thermonuclear fusion are complex, requiring numerical simulations with high performance computing, and the amount of data generated by the physical processes on spatial, temporal and temperature scales is too large to be captured, managed, processed and collated in a reasonable time frame by mainstream software tools to achieve more aggressive fusion physical design. The data are too large to be captured, managed, processed, and collated into more aggressive targets for fusion physical design in a reasonable time by mainstream software tools. At the same time, the failure of fusion ignition can be caused by the distortion of various key physical quantities, and only by decomposing the process step by step and clarifying the changes of key physical quantities in the fusion physics process, can an effective mechanism be formed to prevent the distortion of key physical quantities from causing ignition failure in experimental physics. Big data in collaboration with artificial intelligence and high performance computing to drive the physical design of fusion is a novel avenue. By data acquisition with and pre-processing, this allows the creation of small sample libraries and deep learning. With supervised learning function convergence, incorporating solid/fluid computational methods, further network layering and cell expansion, the parameters of the physical model conforming to Lawson's criterion will become experimental physical parameters, and we find the new approach in Data capacity, Model combination approach, Type of material, Calculation speed, Optimisation of design iteration times, etc. are superior to the traditional approach. © 2022 IEEE.

关键词： Big data

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A Message Passing Interface Library for High-Level Synthesis on Multi-FPGA systems

A Message Passing Interface Library for High-Level Synthesis...

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IEEE international symposium on Embedded Multicore Socs (MCSoC)

作者： Kazuei Hironaka Kensuke Iizuka Hideharu Amano Dept. of Information and Computer Science Keio University Yokohama Japan

ISBN: (纸本)9781665465007

One obstacle to application development on multi-FPGA systems with high-level synthesis (HLS) is a lack of support for a programming interface. Implementing and debugging an application on multiple FPGA boards is difficult without a standard interface. Message Passing Interface (MPI) is a standard parallel programming interface commonly used in distributed memory systems. This paper presents a tool-independent MPI library called FiC-MPI that can be used in HLS for multi-FPGA systems in which each FPGA node is connected directly. By using FiC-MPI, various parallel software, including a general-purpose benchmark, can be easily implemented. FiC-MPI was implemented and evaluated on the M-KUBOS cluster consisting of Zynq MPSoC boards connected with a static time-division multiplexing network. By using the FiC-MPI simulator, parallel programs can be debugged before implementing on real machines. As a case study, the Himeno-BMT benchmark was implemented with FiC-MPI. It achieved 178.7 MFLOPS with a single node and scaled to 643.7 MFLOPS with four nodes, and 896.9 MFLOPS with six nodes of the M-KUBOS cluster. Through the implementation, the easiness of developing parallel programs with FiC-MPI on multi-FPGA systems was demonstrated.

关键词： Multiplexing parallel programming Multicore processing Message passing Benchmark testing Libraries software

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distributed Online Service Coordination Using Deep Reinforcement Learning 41

Distributed Online Service Coordination Using Deep Reinforce...

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41st IEEE international Conference on distributed Computing systems (ICDCS)

作者： Schneider, Stefan Qarawlus, Haydar Karl, Holger Paderborn Univ Paderborn Germany Fraunhofer ISST Dortmund Germany

ISBN: (纸本)9781665445139

Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).

关键词： Training Knowledge engineering Network topology Pipelines Reinforcement learning Quality of service Manuals

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A Full-reference Video Quality Assessment Method for 4K UHD Video based on Multi-Feature Fusion

A Full-reference Video Quality Assessment Method for 4K UHD ...

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ACIS international Conference on software engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Geng Yi Shi Ping Pan Da School of Information and Communication Engineering Communication University of China Beijing China

Video quality assessment plays an important role in the quality control of video transmission and the development of video processing equipment and algorithms. With the popularity of UHD TV, the demand for UHD video quality assessment is becoming more and more urgent. In this paper, we propose a method for 4K UHD video quality assessment based on multi-feature fusion (MFF- VQA). First, we select eight frame-level features which could better reflect the perceived video quality through a series of ablation experiments. Then, we present a scheme which can fuse the eight features into a quality score. Experimental results show that, compared with other similar methods, the proposed method can achieve better performance even with lower algorithm complexity and fewer video frames.

关键词： Visualization TV software algorithms Quality control Streaming media Real-time systems Quality assessment

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LWSA: A Learning-Based Workflow Scheduling Algorithm for Energy-Efficient UAV Delivery System

LWSA: A Learning-Based Workflow Scheduling Algorithm for Ene...

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international Conference on parallel and distributed systems (ICPADS)

作者： Jiali Wang Yutong Ye Ting Wang Mingsong Chen MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in delivery networks face significant challenges due to limited battery life and computational capabilities, particularly for tasks that entail intensive computing workflows. In this context, Multi-access Edge Computing (MEC), which provides computing resources in close proximity to mobile terminal devices, has emerged as a promising solution. UAVs can offload computing tasks to MEC resources across diverse Internet of Things (IoT) environments. Although task offloading can enhance their task processing capability, it simultaneously brings additional costs, encompassing data transmission time and energy consumption. To address these issues, this paper proposes a novel workflow scheduling method based on the Proximal Policy Optimization (PPO) algorithm, aimed at optimizing UAV energy consumption within MEC environments. The proposed approach establishes a learning-based workflow scheduling strategy harnessing the adaptability of the PPO algorithm to manage dynamic and intricate scenarios, which facilitates efficient task allocation to optimal computational resources while accounting for flight time constraints. Extensive experiments conducted on various well-known scientific workflow benchmarks in real-world UAV delivery networks validate the effectiveness of our method. Compared with state-of-the-art methods, our approach significantly reduces UAV energy consumption and task completion time, simultaneously increasing UAV’s effective payload capacity.

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