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An Intrusion Detection System for Web-Based Attacks Using IBM Watson

IEEE LATIN AMERICA TRANSACTIONS 2022年第2期20卷 191-197页

作者： da Silva, Ricardo C. C. Camargo, Marcos P. de O. Quessada, Matheus S. Lopes, Anderson C. Ernesto, Jacinto D. M. da Costa, Kelton A. P. Univ Estadual Paulista Sao Jose Do Rio Preto SP Brazil Univ Estadual Paulista Rio Claro SP Brazil Univ Estadual Paulista Bauru SP Brazil

The internet and web applications have been growing steadily and together with the increasing number of cyber attacks. These attacks are carried out through requests that are considered normal or abnormal (attack requests). Therefore, an intrusion attack can be considered as a classification problem. machine learning algorithms are used as a way to train models to classify these requests in order to increase the security of web systems. The data used to carry out the training and tests in this work come from the CSIC 2010 dataset. The J48, Naive Bayes, OneR, Random Forest and IBM Watson LGBM algorithms were tested. The metrics used were t-rate, precision, recall and f measure. The results showed that the algorithm used by the Watson tool (LGBM) was the one that did the best in all metrics when compared to the other algorithms in the literature.

关键词： Classification algorithms Passwords Measurement machine learning algorithms Intrusion detection IEEE transactions Electronic mail Web applications cyber attacks intrusion attack machine learning classification IBM Watson

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Tracking Buggy Files: New Efficient Adaptive Bug Localization Algorithm

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2021年第7期48卷 2557-2569页

作者： Fejzer, Mikolaj Narebski, Jakub Przymus, Piotr Stencel, Krzysztof Nicolaus Copernicus Univ Torun Fac Math & Comp Sci PL-87100 Torun Poland Nicolaus Copernicus Univ Torun PL-87100 Torun Poland Univ Warsaw PL-00927 Warsaw Poland

Upon receiving a new bug report, developers need to find its cause in the source code. Bug localization can be helped by a tool that ranks all source files according to how likely they include the bug. This problem was thoroughly examined by numerous scientists. We introduce a novel adaptive bug localization algorithm. The concept behind it is based on new feature weighting approaches and an adaptive selection algorithm utilizing pointwise learn-to-rank method. The algorithm is evaluated on publicly available datasets, and is competitive in terms of accuracy and required computational resources compared to state-of-the-art. Additionally, to improve reproducibility we provide extended datasets that include computed features and partial steps, and we also provide the source code.

关键词： Computer bugs Location awareness Software History Software algorithms machine learning algorithms Training Bug reports software maintenance learning to rank

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A Hybrid Method of Feature Extraction for Signatures Verification Using CNN and HOG a Multi-Classification Approach

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IEEE ACCESS 2023年 11卷 21873-21882页

作者： Alsuhimat, Fadi Mohammad Mohamad, Fatma Susilawati Univ Sultan Zainal Abidin Fac Informat & Comp Kuala Terengganu 21300 Malaysia

The offline signature verification system's feature extraction stage is regarded as crucial and has a significant impact on how well these systems perform because the quantity and calibration of the features that are extracted determine how well these systems can distinguish between authentic and fake signatures. In this study, we introduced a hybrid method for extracting features from signature images, wherein a Convolutional Neural Network (CNN) and Histogram of Oriented Gradients (HOG) were used, followed by the feature selection algorithm (Decision Trees) to identify the key features. Finally, the CNN and HOG methods were combined. Three classifiers were employed to evaluate the efficacy of the hybrid method (long short-term memory, support vector machine, and K-nearest Neighbor). The experimental findings indicated that our suggested model executed satisfactorily in terms of efficiency and predictive ability, with accuracies of (95.4%, 95.2%, and 92.7%) the UTSig dataset, and (93.7%, 94.1%, and 91.3%, respectively) with the CEDAR dataset. This accuracy is deemed to be of high significance, particularly given that we checked skilled forged signatures that are more difficult to recognize than other forms of forged signatures like (simple or opposite).

关键词： Feature extraction Convolutional neural networks Forgery Biometrics (access control) Deep learning machine learning algorithms Histograms Offline signature verification CNN HOG deep learning

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Automatic Classification of Algorithm Citation Functions in Scientific Literature

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第10期32卷 1881-1896页

作者： Tuarob, Suppawong Kang, Sung Woo Wettayakorn, Poom Pornprasit, Chanatip Sachati, Tanakitti Hassan, Saeed-Ul Haddawy, Peter Mahidol Univ Fac Informat & Commun Technol Salaya Phutthamonthon 73170 Thailand Inha Univ Coll Engn Incheon 22212 South Korea Informat Technol Univ Lahore Pakistan

Computer sciences and related disciplines evolve around developing, evaluating, and applying algorithms. Typically, an algorithm is not developed from scratch, but uses and builds upon existing ones, which often are proposed and published in scholarly articles. The ability to capture this evolution relationship among these algorithms in scientific literature would not only allow us to understand how a particular algorithm is composed, but also shed light on large-scale analysis of algorithmic evolution through different temporal spans and thematic scales. We propose to capture such evolution relationship between two algorithms by investigating the knowledge represented in citation contexts, where authors explain how cited algorithms are used in their works. A set of heterogeneous ensemble machine-learning methods is proposed, where the combination of two base classifiers trained with heterogeneous feature types is used to automatically identify the algorithm usage relationship. The proposed heterogeneous ensemble methods achieve the best average F1 of 0.749 and 0.905 for fine-grained and binary algorithm citation function classification, respectively. The success of this study will allow us to generate a large-scale algorithm citation network from a collection of scholarly documents representing multiple time spans, venues, and fields of study. Such a network will be used as an instrument not only to answer critical questions in algorithm search, such as identifying the most influential and generalizable algorithms, but also to study the evolution of algorithmic development and trends over time.

关键词： Feature extraction machine learning algorithms Metadata Clustering algorithms Approximation algorithms machine learning Computer science Algorithm citation ensemble machine learning scholarly big data algorithmic evolution

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HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector machine and Hierarchical Clustering

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IEEE ACCESS 2023年 11卷 21404-21416页

作者： Zou, Li Luo, Xuemei Zhang, Yan Yang, Xiao Wang, Xiangwen Gansu Prov Meteorol Informat & Tech Support & Equi Gansu Meteorol Bur Lanzhou 730020 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

Network intrusion detection is an important technology in national cyberspace security strategy and has become a research hotspot in various cyberspace security issues in recent years. The development of effective and efficient intelligent network intrusion detection methods using advanced machine learning algorithms is of great importance for defending against various network intrusions in complex network environments. In this study, a network intrusion detection method based on decision tree twin support vector machine and hierarchical clustering, named HC-DTTWSVM, is proposed, which can effectively detect different categories of network intrusion. First, the hierarchical clustering algorithm is applied to construct the decision tree for network traffic data, where the bottom-up merging approach is used to maximize the separation of the upper nodes of the decision tree, which reduces the error accumulation in the construction of the decision tree. Then, twin support vector machines are embedded in the constructed decision tree to implement the network intrusion detection model, which can effectively detect the network intrusion category in a top-down manner. The detection performance of the proposed HC-DTTWSVM method is evaluated on NSL-KDD and UNSW-NB15 intrusion detection benchmark datasets. Experimental results show that HC-DTTWSVM can effectively detect different categories of network intrusion and achieves comparable detection performance compared to some of the recently proposed network intrusion detection methods.

关键词： Network intrusion detection Decision trees Security Support vector machines machine learning algorithms Cyberspace Training twin support vector machine hierarchical clustering decision tree

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Pattern Reorder for Test Cost Reduction Through Improved SVMRANK Algorithm

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IEEE ACCESS 2020年 8卷 147965-147972页

作者： Song, Tai Liang, Huaguo Ni, Tianming Huang, Zhengfeng Lu, Yingchun Wan, Jinlei Yan, Aibin Hefei Univ Technol Sch Elect Sci & Appl Phys Hefei 230009 Peoples R China Anhui Polytech Univ Coll Elect Engn Wuhu 241000 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

With the growing complexity of integrated circuits (IC), more and more test patterns are added to test set to test more defects, making the number of test pattern and individual test pattern length continues to increase as the size of IC gets larger, boosting test time and consequently test cost. To solve this problem, this paper proposes a kind of valid pattern identification method. The method uses machine learning to reorder the test pattern which can select the most effective patterns, to determine the optimal training set and test set first. Then, by means of the weighted SVMRANK algorithm to find the optimal pattern sequence. Experiment results demonstrate that the method only sacrifices 2% prediction accuracy in exchange for 3.89 times the time saving. The method aims at maximizing the accuracy of test, and minimizing the number of patterns. The proposed idea significantly improves the test time and test efficiency compared to conventional test flows. This is an innovative test cost reduction method with the growing complexity of IC.

关键词： Testing Integrated circuits Pressing Redundancy Complexity theory machine learning machine learning algorithms Adaptive test machine learning test reorder

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A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

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IEEE ACCESS 2021年 9卷 11455-11469页

作者： Apostolo, Guilherme Henrique Bernardini, Flavia Magalhaes, Luiz C. Schara Muchaluat-Saade, Debora Christina MidiaCom Res Lab BR-24210240 Niteroi RJ Brazil Univ Fed Fluminense Inst Comp BR-24210310 Niteroi RJ Brazil Univ Fed Fluminense Telecommun Engn Dept BR-24210240 Niteroi RJ Brazil

People usually spend several hours per day inside buildings, and they require great amounts of energy and resources to operate. Although there are numerous studies about smart buildings, there is still a need for new intelligent techniques for efficient smart building management. This paper proposes the use of Wi-Fi network association information as a basis for the design of intelligent systems for smart buildings. We propose a unified experimental methodology to evaluate machine learning (ML) models on their capacity to accurately predict Wi-Fi access point demand for energy-efficient smart buildings. The evaluation involves the use of multiple classification and regression models using a variety of configurations and algorithms. We conducted an experimental analysis using our proposed methodology to determine which ML models provide the best performance results using data collected from a large scale Wi-Fi network located at Fluminense Federal University (UFF) over a period of 6 months. The proposed methodology enables the user to evaluate and to create ML models for energy efficient smart building management systems. We achieved 86.69% accuracy for occupancy prediction using classification techniques and RMSPE (Root Mean Squared Percentage Error) of 0.29 for occupancy count prediction using regression techniques.

关键词： Wireless fidelity Smart buildings machine learning algorithms machine learning Predictive models HVAC Wireless networks Access point occupancy prediction energy saving machine learning smart buildings Wi-Fi networks

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Can Software Engineering Harness the Benefits of Advanced AI?

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IEEE SOFTWARE 2022年第6期39卷 99-104页

作者： Shaw, Mary Zhu, Liming Carnegie Mellon Univ Comp Sci Pittsburgh PA 15217 USA Univ New South Wales Sydney NSW 2015 Australia

Artificial intelligence (AI) has allowed us to build systems beyond anything deemed possible earlier. Can we evolve existing techniques in software engineering to meet the needs of AI enabled systems or do we need to ... 详细信息

关键词： Artificial Intelligence Software Engineering Software Systems Codes Software Reliability machine learning algorithms Deep learning machine learning

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The role of data frequency and method selection in electricity price estimation: Comparative evidence from Turkey in pre-pandemic and pandemic periods

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RENEWABLE ENERGY 2022年第0期186卷 217-225页

作者： Depren, Serpil Kilic Kartal, Mustafa Tevfik Ertugrul, Hasan Murat Depren, Ozer Yildiz Tech Univ Dept Stat Istanbul Turkey Borsa Istanbul Financial Reporting & Subsidiari Istanbul Turkey Minist Treasury & Finance Ankara Turkey Yapi Kredi Bank Customer Experience Res Ctr Istanbul Turkey

The study examines the role of data frequency and estimation methods in electricity price estimation by applying selected machine learning algorithms and time series econometric models. In this context, Turkey is selected as an emerging country example, seven explanatory variables including COVID-19 pandemic is considered, and daily and weekly data between February 20, 2019 and March 26, 2021 that includes pre-pandemic and pandemic periods are used. The empirical results show that (i) machine learning algorithms perform better than time series econometric models for both pre-pandemic and pandemic periods;(ii) high-frequency data increases the performance of estimation models;(iii) machine learning algorithms perform better with high-frequency (daily) data with regard to low-frequency (weekly) data;(iv) the pandemic causes an adverse effect on the performance of estimation models;(v) energy-related variables are more important than other variables although all are significant;(vi) the share of renewable sources in electricity production is the most important variable on the electricity prices in both periods and data types. Hence, the findings highlight the role of data frequency and method selection in electricity prices estimation. Moreover, policy implications are discussed.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Electricity prices Data frequency machine learning algorithms Time series econometric models Turkey

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Range-Constrained and Stable Federated Charging Scheme for Electricity Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15886-15896页

作者： Zhang, Jiancong Li, Shining Wang, Changhao Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

Due to the increasing number of electric vehicles and limited charging stations, charging service providers need to consider the minimum range of electric vehicles and the congestion level of charging stations to formulate pricing strategies and guide charging vehicles. However, this service faces the challenges of privacy protection and utility coupling between CSs and EVs. Besides, the minimum driving range is also affected by the time-varying data characteristics of vehicles. Therefore, this paper proposes an FL-assisted stable charging matching strategy considering congestion factors and range constraints. Specifically, we employ a federated learning scheme to predict the congestion factor of each charging station by learning the complex hidden characteristics of vehicles under the condition of privacy protection. Then, the pricing strategy and utility function are updated based on the congestion factor. Furthermore, under the driving range constraint, we transform the impact of the congestion factor on the expected utility into a regret value and use the Gale-Shapley algorithm based on regret minimization to achieve stable matching between vehicles and charging stations. Experimental results demonstrate the scheme's effectiveness and that the charging success rate is improved by up to 11.7% compared with mainstream methods.

关键词： Pricing Protection Optimization Couplings machine learning algorithms Prediction algorithms Data privacy Charging congestion intelligent scheduling range anxiety federated learning

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