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Version-Wide software Birthmark via Machine Learning

IEEE ACCESS 2021年 9卷 110811-110825页

作者： Chung, Chih-Ko Wang, Pi-Chung Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 402 Taiwan

Identifying the credibility of executable files is critical for the security of an operating system. Modern operating systems rely on code signing, which uses a default-valid trust model, for executable files to identify their publishers. A malware could pass software validation of operating systems and security software by using counterfeit code-signing certificates. Although the counterfeit certificates can be revoked by CAs, the previous research showed that the revocation delay takes as long as 5.6 months. In this paper, we attempt to identify the credibility of software with multiple-version executable files without relying on public key infrastructure (PKI), where a new-version executable file is usually developed incrementally based on the previous versions. The sharing features among different versions can be extracted for identifying the software. Accordingly, we present a software-birthmark scheme to serve our purpose. Our scheme generates a cross-version software birthmark for executable files of the same software. The proposed software birthmark is a binary-classification model of a machine learning algorithm based on imported and exported function names extracted from different-version executable files. To evaluate the performance of version-wide software birthmarks, our experiments include 138 versions of Windows *** and 545 versions of ***. We also use multiple machine learning algorithms for performance comparisons. The results show that proposed software birthmark can effectively identify the derivations of these executable files. The proposed software birthmark can be used by operating systems or security software to evaluate the credibility of executable files with suspicious certificates.

关键词： software Malware Operating systems software algorithms Digital signatures Security Feature extraction software birthmark executable file format Authenticode digital signature machine learning content digest import export address table

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Evaluation Test and Improvement of Load Balancing algorithms of Nginx

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IEEE ACCESS 2022年 10卷 14311-14324页

作者： Ma, Chen Chi, Yuhong Xijing Univ Sch Informat Engn Xian 710123 Peoples R China

Today most people can't live without electronic devices. And more and more new devices are coming to the market every day. Apps running on these devices often connect to one or several web-based server side applications, which in turn put a lot of load and management pressure on the servers and clusters serving these web applications. Technologies such as Nginx and Keepalived were invented to address the load issues faced by these high concurrency applications. This paper tested a server cluster environment based on Nginx and Keepalived, evaluated the performance of Nginx based algorithms such as WRR, IP_HASH and LEAST_CONN, and designed an optimized version of IP_HASH (named as NEW_HASH). Compared to the original IP_HASH, the NEW_HASH reduces the probability of hash collisions and improves the performance of searching back-end nodes. The test showed NEW_HASH outperforms the original method of IP_HASH, with reduced response time, lower failure rates and increased throughput. Overall, the server cluster performed better under the high load pressure using NEW_HASH.

关键词： Servers Clustering algorithms Load management Peer-to-peer computing Protocols software algorithms software High concurrency IP_HASH load balancing Nginx

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A Novel Hyperheuristic Algorithm Based on Evolutionary Strategy for Complex Mission Planning of AUVs in Marine Environment

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IEEE JOURNAL OF OCEANIC ENGINEERING 2022年第4期47卷 936-949页

作者： Wei, Dunwen Ma, Hongjiao Yu, Huijun Dai, Xiaolin Wang, Gang Peng, Bei Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Autonomous underwater vehicles (AUVs) have been widely implemented to explore marine resources or perform marine missions;meanwhile, complex mission planning has been expected to enhance the intelligence level, improve energy efficiency, and expand AUVs' application areas. The mission planning model needs to consider energy consumption, mission quality, and mission quantity of long-term working in the marine environment. In this article, one unified and robust model was proposed based on the above requirements in the complex marine environment to improve the automaticity of AUV. However, solving this mission planning model is a nontrivial problem, especially when high robustness, high efficiency, fast response, and near-optimal results are required. Therefore, we propose a novel hyperheuristic algorithm based on evolutionary strategy (ES-HH). The proposed ES-HH combines a metaheuristic framework with a selection function to evaluate the performance of low-level heuristic operators online. This evolutionary strategy endows the hyperheuristic algorithm with the online learning feature, giving the ES-HH algorithm better computing efficiency, robustness, and near-optimal results. The experiment results show that the ES-HH algorithm can achieve better convergence and higher robustness than other algorithms, such as ant colony optimization and biogeography-based optimization algorithms. Compared with ACO and BBO algorithms, the proposed ES-HH algorithm can improve the mission completion rate by 3.42% and reduce the average energy consumption of a single task by 8.02%.

关键词： Heuristic algorithms Planning Task analysis Costs software algorithms Energy consumption Robustness Autonomous underwater vehicles (AUVs) evolu-tionary strategy hyperheuristic algorithm (HHA) metaheuristic mission planning

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System and Experiments of Model-Driven Motion Planning and Control for Autonomous Vehicles

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第9期52卷 5975-5988页

作者： Xu, Shaobing Zidek, Robert Cao, Zhong Lu, Pingping Wang, Xinpeng Li, Boqi Peng, Huei Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Toyota Res Inst Ann Arbor MI 48105 USA Tsinghua Univ Sch Vehicle & Mobil Beijing 10083 Peoples R China

This article presents a model-based motion planning and control system for autonomous vehicles and its experimental validation. The system consists of four modules: 1) global routing;2) behavior planner;3) local trajectory generation;and 4) trajectory tracking. The algorithm and software of each module are detailed, including a behavior planner with unified models to handle typical scenarios in both highway and urban driving, a deterministic sampling algorithm for robust responsive trajectory generation, and a dynamics-and-delay-aware preview algorithm to achieve accurate trajectory tracking. The developed system is implemented and tested at the Mcity test facility with a full-size automated car and a dozen of challenging traffic scenarios.

关键词： Trajectory Planning Routing Heuristic algorithms software algorithms Roads Autonomous vehicles Autonomous vehicles behavior planner deterministic sampling motion planning preview control

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A Survey of Polynomial Multiplication With RSA-ECC Coprocessors and Implementations of NIST PQC Round3 KEM algorithms in Exynos2100

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IEEE ACCESS 2022年 10卷 2546-2563页

作者： Park, Jong-Yeon Moon, Yong-Hyuk Lee, Won-Il Kim, Sung-Hyun Sakurai, Kouichi Samsung Elect Syst LSI Hwaseong si 16677 Gyeonggi Do South Korea Univ Sci & Technol Dept Comp Software Daejeon 305350 South Korea Elect & Telecommun Res Inst Daejeon 34129 South Korea Kyushu Univ Dept Informat Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan

Polynomial multiplication is one of the heaviest operations for a lattice-based public key algorithm in Post-Quantum Cryptography (PQC). Many studies have been done to accelerate polynomial multiplication with newly developed hardware accelerators or special CPU instructions. However, another method utilizes previously implemented and commercial hardware accelerators for RSA/elliptic curve cryptography (ECC). Reusing an existing hardware accelerator is advantageous, not only for the cost benefit but also for the improvement in performance. In this case, the developer should adopt the most efficient implementation method for the functions provided by a given legacy hardware accelerator. It is difficult to find an optimized implementation for a given hardware accelerator because there are a variety of methods, and each method depends on the functions provided by the given accelerator. In order to solve the problem, we survey methods for polynomial multiplication using RSA/ECC coprocessors and their application for Learning With Error (LWE)-based KEM algorithms of National Institute of Standards and Technology (NIST) PQC round 3 candidates. We implement all known methods for polynomial multiplication with RSA/ECC coprocessors in a platform, commercial mobile system-on-chip (SoC), the Exynos2100 Smart Secure Platform (SSP). We present and analyze the simulation results for various legacy hardware accelerators and give guidance for optimized implementation.

关键词： Hardware acceleration software algorithms software Elliptic curve cryptography Cryptography Coprocessors Simulation Polynomial multiplication RSA ECC coprocessor Kronecker substitution PQC post-quantum cryptography Lattice-based cryptosystem

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Nautilus: An Interactive Plugand-Play Search-Based software Engineering Framework

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IEEE software 2021年第5期38卷 73-82页

作者： Ferreira, Thiago Nascimento Vergilio, Silvia Regina Kessentini, Marouane Univ Fed Parana Curitiba Parana Brazil Univ Fed Parana Comp Sci Dept Software Engn SE BR-81531980 Curitiba Parana Brazil Univ Michigan Ann Arbor MI 48109 USA

Nautilus Framework allows practitioners to develop and experiment with several multi- and many-objective evolutionary algorithms-guided (or not) by human participation-in a few steps with a minimum required background... 详细信息

关键词： Evolutionary Computation Search Problems software Engineering Nautilus Framework Many Objective Evolutionary algorithms Search Based algorithms Human Participation Interactive Plug And Play Search Based software Engineering Framework Optimization software algorithms Search Problems software Engineering Linear Programming Encoding Visualization Preference And Search Based software Engineering Many Objective Optimization Plugin And Play Framework

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On-Street Parking Occupancy Inference Based on Payment Transactions

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 10680-10691页

作者： Assemi, Behrang Paz, Alexander Baker, Douglas Queensland Univ Technol QUT Gardens Point Sch Architecture & Built Environm Brisbane Qld 4000 Australia Queensland Univ Technol QUT Gardens Point Sch Civil & Environm Engn Brisbane Qld 4000 Australia

A record of spatial and temporal parking occupancy is critical to optimize on-street parking resources and to develop effective parking policies. Such data are often obtained through advanced and costly occupancy monitoring technologies. Moreover, it is usually challenging to integrate bay-level occupancies and parking data from other systems. Accurate occupancy-payment data are required for a wide range of analytical and practical purposes, including but not limited to investigating payment behavior, estimating and forecasting occupancy, and evaluating the efficiency and effectiveness of enforcement policies. This study proposes a metaheuristic optimization algorithm to integrate snapshots of bay-level parking occupancy, captured using simple cameras, with transactions from a conventional parking payment management system. The resulting integrated data were used to develop, calibrate and validate a parking occupancy estimation method utilizing parking payment data only. Details of the design, implementation, and validation of the proposed algorithm and modelling technique are provided. Logistic regression analysis was used to tune parameters of the data integration algorithm. Deep learning, gradient boosting and random forests were used to develop a model of parking occupancy. Evaluation of the algorithm indicated an accuracy of 76% of correct data integration;that is, individual bay occupancies integrated with the correct corresponding payment transactions. The best occupancy estimation model also showed a very high accuracy, with an R-2 above 94% and a root mean square error (RMSE) of 1.2 (occupied bays), when tested with a random sample from the integrated data.

关键词： Monitoring Estimation Data models Cameras Reliability software algorithms Prediction algorithms Learning (artificial intelligence) prediction algorithms software algorithms inference algorithms heuristic algorithms

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ANFIS-Based Optimum Design of Real Power Transmission Towers with Size, Shape and Panel Design Variables using BBO Algorithm

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IEEE TRANSACTIONS ON POWER DELIVERY 2022年第1期37卷 29-39页

作者： Hosseini, Neda Ghasemi, Mohammad Reza Dizangian, Babak Univ Sistan & Baluchestan Zahedan *** Iran Velayat Univ Iranshahr *** Iran

The present study aims to provide a suitable approach to optimize transmission line towers with size, shape and panel design variables. MSTOWER software was used for modeling, analysis and design of the transmission tower. The design requirements applied to the structure were in accordance with ASCE10-97 standard. Transmission line towers are optimized in two ways. The first method combines Biogeography based Optimization (BBO) algorithm with MSTOWER software called BBO-MSTOWER and the second method, called BBO-ANFIS, uses an adaptive fuzzy neural inference system (ANFIS). To analyze the structure in the optimization process with the aim of reducing the computations and time of the optimization process. In order to evaluate the proposed method, two full-scale transmission towers were optimized as numerical models using the above two methods. Finally, the optimal design and time required in the two optimization methods were compared with each other as well as with the initial design of the tower and the results reported in previous studies. The results showed that with acceptable accuracy, ANFIS resulted in a significant reduction in the calculation time of the entire optimization process.

关键词： Poles and towers Optimization Steel Shape Lattices Power transmission lines software algorithms Adaptive neuro fuzzy inference system biogeography based optimization power transmission line steel lattice tower

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HOPE: software Defect Prediction Model Construction Method via Homomorphic Encryption

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IEEE ACCESS 2021年 9卷 69405-69417页

作者： Yu, Chi Ding, Zixuan Chen, Xiang Nantong Univ Sch Informat Sci & Technol Nantong 226001 Peoples R China Hangzhou Normal Univ Sch Informat Sci & Engn Hangzhou 310018 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Safety Crit Software Minist Ind & Informat Technol Nanjing 210016 Peoples R China

software defect prediction can predict the defective modules in the project in advance, which is helpful to optimize the allocation of test resources. Recently, privacy protection for datasets and models has gradually attracted the attention of researchers. In this study, we are the first to apply homomorphic encryption to software defect prediction model construction and propose a novel method HOPE. Specifically, we adopt an algorithm approximation strategy to approximate the sigmoid function and select the Paillier homomorphic encryption algorithm for Logistical regression. In our case study, we choose the MORPH dataset gathered from real-world open-source projects as our experimental subjects. Then we design three control groups to simulate three different scenarios based on whether the client sends the encrypted data to the server and whether the server uses the HOPE method. The final results show that if the server uses the original Logistic regression to construct the model on the encrypted data, the performance of the trained model is similar to random guess, which can guarantee the privacy protection of the data. Moreover, compared with the original Logistical regression method, the method HOPE only needs a small amount of computational cost, but there is no obvious performance decrease. We share our implementation scripts and datasets to encourage researchers to conduct more studies on this research direction.

关键词： software Encryption Predictive models Cryptography software algorithms Servers Prediction algorithms software defect prediction homomorphic encryption logistic regression privacy protection

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A New Topology Control Algorithm in software Defined Wireless Rechargeable Sensor Networks

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IEEE ACCESS 2021年 9卷 101003-101012页

作者： Huang, Ruoyu Dong, Ying Bao, Guangjiu Liu, Yuhong Wei, Ming Lu, Jiaxuan Huo, Yuxin Jilin Univ Coll Elect Sci & Engn Changchun 130012 Peoples R China Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China

In wireless networks, the network coverage and sustainable operations are closely interlinked. These are the most critical problems in any wireless sensor networks (WSNs), which are based on software defined networks. However, in previous literature, these problems are always considered separately. Consequently, these problems are not addressed in an efficient manner. In this work, we focus on new network structures known as software defined wireless rechargeable sensor networks (SDWRSNs) to ensure long-term operations and full coverage of the network simultaneously. In this work, we propose the least nodes deployment and charging algorithm (LNDCA) based on the homology theory. In the proposed LNDCA, the SDN controller requests the mobile chargers to replenish the energy for the node with the lowest energy. Additionally, the algorithm fully covers the whole network by using minimum number of nodes and ensures continuous operations in the network. The simulation results and analysis conducted in this work show that the proposed algorithm performs well in terms of energy consumption, coverage, and sustainable operations.

关键词： Topology Network topology Wireless sensor networks Wireless communication software algorithms software Heuristic algorithms Wireless rechargeable sensor networks software defined networks homology theory topology control

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