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Revealing inputs causing web API performance latency using response-time-guided genetic algorithm fuzzing

Artificial Life and Robotics 2024年第4期29卷 459-472页

作者： Huang, Ying-Tzu Lee, Shin-Jie Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

Web APIs are integral to modern web development, enabling service integration and automation. Ensuring their performance and functionality is critical, yet performance testing is less explored due to the difficulty in detecting performance bugs. This paper presents a response time-guided genetic algorithm (GA) fuzzing approach to uncover web API performance latency in a black-box setting. Unlike traditional random input generation, our method uses GA to refine inputs through crossover and mutation, guided by response time-based fitness. We propose two seed generation methods: pairwise combinatorial testing using Mircosoft’s Pairwise Independent Combinatorial Testing (PICT) and randomly paired combinations. We compared our method with classic random fuzzing. Experiments on five real-world web APIs show that our approach significantly outperforms classic random fuzzing, identifying inputs with response times 1.5 to 26.3 times longer. Additionally, PICT-generated seeds demonstrated superior performance compared to randomly-paired combinations in 2 out of 5 APIs. Our findings highlight the potential of GA-based fuzzing to reveal web API performance latency, advocating for further research in this area. © International Society of Artificial Life and Robotics (ISAROB) 2024.

关键词： Genetic algorithms

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Dynamic performance modeling framework for QoS-aware 5G-based IoT-edge systems

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International Journal of Information technology (Singapore) 2024年第4期16卷 2149-2160页

作者： Bebortta, Sujit Pati, Bibudhendu Panigrahi, Chhabi Rani Senapati, Dilip Department of Computer Science Ravenshaw University Odisha Cuttack 753003 India Department of Computer Science Rama Devi Women’s University Odisha Bhubaneswar 751022 India Department of Computer Science University of Delhi Delhi 110007 India

The development of fifth-generation (5G) mobile networks, which enable new levels of near real-time automation of remote Internet of Things (IoT) devices. The current IoT concept will be strengthened by this advancement by broadening its potential uses, notably in the industrial and healthcare sector among others. IoT has a lot of potential for sectors like healthcare, manufacturing, mining, education, and autonomous vehicles when combined with the edge computing paradigms. To this end, Quality of service (QoS) and quality of experience (QoE) are key criteria for user satisfaction since in the majority of 5G-based IoT applications, the tolerable latency will be less than one millisecond. The usage of complex and intelligent methods between end-nodes, including edge and fog computing, is essential towards the success of 5G-driven IoT systems. A middleware, such as edge computing, can play a key role in realising the potential of IoT by allocating resources according to the QoS/QoE requirements of each service. This research proposes a theoretical framework by adopting the multi-servers queuing system for obtaining a QoS-aware implementation to favor dynamic resource allocation in IoT applications. We compare the proposed framework with some state-of-the-art benchmark strategies to validate its efficacy for IoT-Edge systems under different performance considerations. The numerical findings emphasise the model’s significance in the context of 5G mobile networks and IoT by depicting its sensitivity analysis over several QoS parameters. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G mobile networks Edge computing Internet of Things Performance evaluation Quality of service Queuing theory

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems science & Engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities 38

Kernel Language Entropy: Fine-grained Uncertainty Quantifica...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Nikitin, Alexander Kossen, Jannik Gal, Yarin Marttinen, Pekka Department of Computer Science Aalto University Finland OATML Department of Computer Science University of Oxford United Kingdom

Uncertainty quantification in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. In particular, uncertainty can be used to improve the trustworthiness of LLMs by detecting factually incorrect model responses, commonly called hallucinations. Critically, one should seek to capture the model's semantic uncertainty, i.e., the uncertainty over the meanings of LLM outputs, rather than uncertainty over lexical or syntactic variations that do not affect answer correctness. To address this problem, we propose Kernel Language Entropy (KLE), a novel method for uncertainty estimation in white- and black-box LLMs. KLE defines positive semidefinite unit trace kernels to encode the semantic similarities of LLM outputs and quantifies uncertainty using the von Neumann entropy. It considers pairwise semantic dependencies between answers (or semantic clusters), providing more fine-grained uncertainty estimates than previous methods based on hard clustering of answers. We theoretically prove that KLE generalizes the previous state-of-the-art method called semantic entropy and empirically demonstrate that it improves uncertainty quantification performance across multiple natural language generation datasets and LLM architectures. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhanced Hill Cipher Algorithm with Novel Encoding and Key Generation 2

Enhanced Hill Cipher Algorithm with Novel Encoding and Key G...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Kalpana, P. Sumathi, P. Jose, Teena Deepa, S. Priya Stella Mary, I. Manimekala, B. Department of Computer Science Christ University Bengaluru India Department of Computer Science Vysya College Salem India

ISBN: (纸本)9798331504960

This research article presents a modified Hill Cipher algorithm that incorporates a novel alphanumeric encoding scheme, for the extended character set that includes alphabets, digits, and space characters. It employs a novel non-sequential encoding scheme aims to provide enhanced security by avoiding predictable character mapping. It also uses a novel approach to key generation, employing a random selection of numbers within a specific range of digits, which significantly accelerates the key generation process faster than the traditional Hill Cipher algorithm. With a character set of 37 (alphabets (26), digits (10) and space), the prime modulus 37 is used in the Hill Cipher, which eliminates the need for the GCD check, that helps in lowering the computational complexity. Further, it has been found that the proposed Hill Cipher provides enhanced efficiency, security, and versatility by generating varied cipher texts for the same character and same cipher text for different plaintext characters. © 2024 IEEE.

关键词： Ciphertext

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IoT Device Identification based on Network Traffic Analysis and Machine Learning

IoT Device Identification based on Network Traffic Analysis ...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Opoku, Stephanie M. Louafi, Habib Mouhoub, Malek University of Regina Department of Computer Science Regina Canada Teluq University Department of Science of Technology Montreal Canada

ISBN: (纸本)9798350364910

As Internet of Things (IoT) technology rapidly evolves, the widespread use and diversity of IoT devices present new challenges for device identification (often called finger-printing). Nevertheless, traditional methods for identifying IoT devices face several problems. This paper presents an identification solution capable of detecting the IoT device identities by analyzing the network traffic they generate and machine learning approaches. The solution we propose is tested on three well-known IoT traffic datasets, and showed higher prediction performance in identifying IoT device types. It is also compared against existing solutions and showed far better results, in terms of both prediction accuracy and temporal complexity. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence-Driven Approach for Predicting Maternal Health Risk Factors 9

Artificial Intelligence-Driven Approach for Predicting Mater...

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9th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Hosaain, Mohammad Mobarak Kashem, Mohammod Abdul Nayan, Nasim Mahmud Department of Computer Science and Engineering Gazipur1707 Bangladesh Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350342482

The development of machine learning has the potential to significantly improve the identification and treatment of pregnancy-related risks in maternal health. This work uses an extensive dataset to create reliable models that can predict possible health issues with accuracy and reliability. Our models are highly predictive and have transparency and interpretability in their results because of our creative feature engineering, such as featurewiz and chi-square test, and meticulous data processing. The models underwent extensive validation procedures, and in three different categories, each proved better evaluation metrics results alongside accuracy levels above 99% in each class. Ensuring the durability and stability of the models, this remarkable degree of accuracy is further enhanced by ensemble techniques and hyperparameter tuning. Incorporating Explainable AI (XAI) methods, such as LIME and SHAP, has proven crucial in elucidating the models' decision-making process, fostering trust, and enabling their possible use in healthcare environments. The results highlight the potential of ML in predicting maternal health risks and establishing solid foundations for future integration with Medical Cyber-Physical Systems (MCPS), which has the potential to transform the field of predictive analytics in prenatal care completely. © 2024 IEEE.

关键词： Adversarial machine learning

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AIO: Automating I/O Optimization Pipeline for Data-Intensive Applications in HPC 22

AIO: Automating I/O Optimization Pipeline for Data-Intensive...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Wanxin Wu, Huijun Yang, Lihua Liu, Zhangyu Zheng, Bin Changsha University of Science and Technology School of Computer and Communication Engineering China National University of Defense Technology College of Computer Science and Technology China

ISBN: (纸本)9798331509712

High-performance computing (HPC) systems has entered the exascale era, but I/O performance has lagged behind due to storage hardware limitations, creating a "storage wall effect"that hinders HPC systems full potential. Modern HPC storage systems employ a layered storage architecture with local node storage, burst buffers, and global parallel file systems to improve I/O performance. However, application workloads vary, and fast storage layers may not always outperform global parallel file systems, while a static storage software stack configuration may not achieve optimal performance across different applications. Traditional I/O optimization is time-consuming, laborintensive, and prone to human error. To address this issue, this paper proposes AIO, a data-driven I/O optimization method. AIO automatically selects the optimal storage hierarchy and software stack based on the task's I/O patterns, and further enhances performance by identifying and applying the most efficient configuration settings. We evaluated AIO with eight applications in a cluster that features shared burst buffers, local burst buffers, and a global shared file system. The experiments demonstrate that AIO effectively optimizes I/O performance, achieving an overall speedup of 2.74. © 2024 IEEE.

关键词： Buffer storage

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Area Efficient 0.009-mm2 28.1-ppm/°C 11.3-MHz ALL-MOS Relaxation Oscillator

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2024年第10期32卷 1900-1907页

作者： Wijaya, Joshua Adiel Chen, Poki Kumar Pradhan, Lucky Shahid Bhatti, Ahmad Kajihara, Seiji National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan Kyushu Institute of Technology Fukuoka804-0015 Japan

This article presents an ultrasmall area on-chip relaxation oscillator with low-temperature sensitivity. In this design, a virtual resistor mainly composed of a complementary to absolute temperature (CTAT) voltage reference circuit is implemented to replace the real resistor for efficient temperature compensation, which counterbalances the inherent proportional to absolute temperature (PTAT) property of the original relaxation circuit of the oscillator. The conventional capacitor is also replaced with a MOS capacitor to complete the ALL-MOS oscillator circuit with two prime advantages, one of which is larger capacitance to area density, and the other is better matching with critical MOSFETs. Implemented in a 0.18-μm TSMC standard CMOS process, the proposed relaxation oscillator has achieved a temperature coefficient of 28.17 ppm/°C over the temperature range from -25°C to + 125°C at 11.39-MHz oscillation frequency. This circuit consumes 243.1μW under 1.3-V power supply. Along with the abovementioned excellent performance, the oscillator achieves an ultrasmall core chip area of 0.009 mm2, which is almost one order less than most of the prior arts' in the same process. © 1993-2012 IEEE.

关键词： Temperature

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Classification of Skin Cancer Using Deep Learning 8th

Classification of Skin Cancer Using Deep Learning

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Nagarjun, A. Manju, N. Aradhya, V. N. Manjunath Shruthi, N. Malapriya, S. Department of Information Science and Engineering JSS Science and Technology University Mysuru India Department Computer Applications JSS Science and Technology University Mysuru India

ISBN: (纸本)9789819976324

Skin cancer is one of the most rapidly spreading illnesses in the world and because of the limited resources available, early detection of skin cancer is crucial accurate diagnosis of skin cancer identification for preventive approach in general. One of these models, Convolutional Neural Networks (CNN), has surpassed all others in object detection and classification tests. The dataset is screened from MNIST: HAM10000 which consists of seven different types of skin lesions with the sample size of 10,015. Transfer learning technique is used to train the model to obtain outstanding results. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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