Intelligent Systems with Applications最新文献

{"title":"Detecting unknown intrusions from large heterogeneous data through ensemble learning","authors":"Farah Jemili,&nbsp;Khaled Jouini,&nbsp;Ouajdi Korbaa","doi":"10.1016/j.iswa.2024.200465","DOIUrl":"10.1016/j.iswa.2024.200465","url":null,"abstract":"<div><div>The rapid expansion of data volumes, technological advancements, and the emergence of the Internet of Things (IoT) have heightened concerns regarding the detection of unknown intrusions based on singular sources of network traffic. This progression has led to the generation of vast and diverse datasets originating from various sources including IoT devices, web applications, and web services. Effectively discerning attacks within such a heterogeneous network traffic landscape necessitates the identification of underlying security behaviors, essential for developing an efficient analysis information system.</div><div>This paper aims to establish a comprehensive framework for network intrusion detection. The proposed methodology involves the synthesis of network features into a universal security database through the utilization of Term Frequency-Inverse Document Frequency Terms (TF-IDF) and semantic Cosine similarity. By amalgamating a diverse array of data flows, a set of universal features is generated, facilitating storage within the newly devised universal representation. Subsequently, Principal Component Analysis (PCA) is employed to reduce the dimensionality of the extensive universal security database while preserving essential information. Leveraging Ensemble Learning, a novel method is introduced for the detection of unknown attacks.</div><div>The efficacy of the developed database is evaluated using various Machine Learning algorithms, including Naïve Bayes, K-Nearest Neighbor, Logistic Regression, Decision Tree, and Random Forest. Furthermore, Ensemble Learning methods are assessed under two distinct scenarios. Experimental findings, conducted on datasets such as CICIDS 2017, NSL-KDD, and UNSW, demonstrate the universality, versatility, and effectiveness of the proposed approach, particularly in accommodating datasets with diverse structures.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200465"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic generated data for intelligent corrosion classification in oil and gas pipelines","authors":"Leo Thomas Ramos ,&nbsp;Edmundo Casas ,&nbsp;Francklin Rivas-Echeverría","doi":"10.1016/j.iswa.2024.200463","DOIUrl":"10.1016/j.iswa.2024.200463","url":null,"abstract":"<div><div>This research presents the K-Pipelines dataset, a pioneering synthetic image collection designed specifically for the classification of corrosion in oil and gas pipelines. Instead of training custom generative architectures, our research used an online image generation tool powered by Stable Diffusion. This choice leveraged the platform’s robust capability to quickly produce a high volume of diverse and detailed images, saving significant time and resources. The dataset was carefully constructed using a sequence of refined prompts, derived from a review of pipeline characteristics including material types, environments, and corrosion forms. K-Pipelines consist of 600 PNG images of 512 × 512 resolution. Furthermore, an augmented version was developed, totaling 1080 images. Our evaluation employed state-of-the-art deep learning classifiers, specifically VGG16, ResNet50, EfficientNet, InceptionV3, MobileNetV2, and ConvNeXt-base, to test the integrity of the K-pipelines dataset. These models showcased its robustness by consistently achieving accuracies around the 90% mark, illustrating the dataset’s substantial promise as a resource for both AI research and real-world applications in the oil and gas industry. The dataset is publicly available for access and use within the scientific community.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200463"},"PeriodicalIF":0.0,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating imperceptibility of adversarial attacks on tabular data: An empirical analysis","authors":"Zhipeng He ,&nbsp;Chun Ouyang ,&nbsp;Laith Alzubaidi ,&nbsp;Alistair Barros ,&nbsp;Catarina Moreira","doi":"10.1016/j.iswa.2024.200461","DOIUrl":"10.1016/j.iswa.2024.200461","url":null,"abstract":"<div><div>Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks have been extensively studied in unstructured data like images, applying them to structured data, such as tabular data, presents new challenges. These challenges arise from the inherent heterogeneity and complex feature interdependencies in tabular data, which differ from the characteristics of image data. To account for this distinction, it is necessary to establish tailored imperceptibility criteria specific to tabular data. However, there is currently a lack of standardised metrics for assessing the imperceptibility of adversarial attacks on tabular data.</div><div>To address this gap, we propose a set of key properties and corresponding metrics designed to comprehensively characterise imperceptible adversarial attacks on tabular data. These are: <em>proximity</em> to the original input, <em>sparsity</em> of altered features, <em>deviation</em> from the original data distribution, <em>sensitivity</em> in perturbing features with narrow distribution, <em>immutability</em> of certain features that should remain unchanged, <em>feasibility</em> of specific feature values that should not go beyond valid practical ranges, and <em>feature interdependencies</em> capturing complex relationships between data attributes.</div><div>We evaluate the imperceptibility of five adversarial attacks, including both bounded attacks and unbounded attacks, on tabular data using the proposed imperceptibility metrics. The results reveal a <em>trade-off</em> between the imperceptibility and effectiveness of these attacks. The study also identifies limitations in current attack algorithms, offering insights that can guide future research in the area. The findings gained from this empirical analysis provide valuable direction for enhancing the design of adversarial attack algorithms, thereby advancing adversarial machine learning on tabular data.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200461"},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion of residual network and t-SNE-CS for 2D visualization of open set recognition","authors":"Tong Xu","doi":"10.1016/j.iswa.2024.200464","DOIUrl":"10.1016/j.iswa.2024.200464","url":null,"abstract":"<div><div>Open set recognition (OSR) is a technique employed to ascertain whether unknown data belongs to a class in a database when the training class is incomplete. In addressing the OSR challenge associated with ADS-B leading pulse signals, this paper proposes a two-dimensional visualization of open set recognition (VOSR) approach that encompasses the stages of feature extraction, feature selection, and feature learning levels. At the feature extraction level, the I/Q features and phase features of the signal are selected; at the feature selection level, feature similarity analysis and mean decrease impurity-based random forest are employed; at the feature learning level, the framework of fusion residual network and the t-distributed stochastic neighbor embedding and circular surfaces (t-SNE-CS) strategy is constructed, and experiments are carried out on the close set data containing 20 classes of 10,229 samples and open set data containing 10 classes of 1,688 samples. Results show that the accuracy of the optimal combination of the residual network and the constructed features is 94.63% for the test set for the close set classification task. For the VOSR task, the accuracy of the test set is 93.69%, the open set recognition accuracy is 53.97% and Macro-F1 scores is 91.8%.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200464"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computationally Efficient Deep Federated Learning with Optimized Feature Selection for IoT Botnet Attack Detection","authors":"Lambert Kofi Gyan Danquah ,&nbsp;Stanley Yaw Appiah ,&nbsp;Victoria Adzovi Mantey ,&nbsp;Iddrisu Danlard ,&nbsp;Emmanuel Kofi Akowuah","doi":"10.1016/j.iswa.2024.200462","DOIUrl":"10.1016/j.iswa.2024.200462","url":null,"abstract":"<div><div>Internet of Things (IoT) is a technology that has revolutionized various fields, offering numerous benefits, such as remote patient monitoring, enhanced energy efficiency, and automation of routine tasks in homes. However, unsecured IoT devices are susceptible to botnet-based attacks such as distributed denial of Service (DDoS). Conventional machine learning models used for detecting these attacks compromise data privacy, prompting the adoption of federated learning (FL) to improve privacy. Yet, most FL-based cyberattack detection models proposed for IoT environments do not address computational complexity to suit their deployment on resource-constrained IoT edge devices. This paper introduces an FL model with low computational complexity, designed for detecting IoT botnet attacks. The study employs feature selection and dimensionality reduction to minimize computational complexity while maintaining high accuracy. First, an extreme gradient boosting model, trained with repeated stratified k-fold cross-validation, is used to select the optimal features of the botnet dataset based on feature importance. Principal component analysis is then used to reduce the dimensionality of these features. Finally, a differentially private multi-layer perceptron is trained locally by four FL clients and aggregated through federated averaging (FedAvg) to form a global Mirai botnet attack detection model. The model achieved an accuracy, precision, recall, and F1-score of 99.93 %, an area under the curve of 1.0, and 8,612 floating-point operations, contributing to 87.34 % reduction in computational complexity compared to the previous work. The proposed model is well-suited for detecting botnet attacks in smart homes, smart grids, and environments where resource-constrained IoT edge devices are deployed.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200462"},"PeriodicalIF":0.0,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MapReduce teaching learning based optimization algorithm for solving CEC-2013 LSGO benchmark Testsuit","authors":"A.J. Umbarkar ,&nbsp;P.M. Sheth ,&nbsp;Wei-Chiang Hong ,&nbsp;S.M. Jagdeo","doi":"10.1016/j.iswa.2024.200460","DOIUrl":"10.1016/j.iswa.2024.200460","url":null,"abstract":"<div><div>Teaching Learning Based Optimization (TLBO) algorithm, introduced in 2011 is widely used in optimization problems across various domains. It is a powerful tool that is capable of solving complex, multidimensional, linear, and nonlinear problems. MapReduce is a distributed programming model developed by Google. It is widely used for processing large datasets in parallel way. This paper proposes the use of the MapReduce programming paradigm for the implementation of the TLBO algorithm on distributed systems, creating a novel approach known as MapReduce Teaching Learning Based Optimization (MRTLBO). The proposed MRTLBO algorithm is tested on Congress of Evolutionary Computations (CEC)-2013 Large-Scale Global Optimization Benchmark Problems dataset, and its performance is compared with sequential TLBO algorithm on the same dataset. The experimental output exhibits that the MRTLBO algorithm is effective in working with high-dimensional problems, and it outperforms the sequential TLBO algorithm with respect to the final result, and speedup. Overall, the proposed MRTLBO algorithm gives a scalable and effective optimization strategy for working on optimization problems in distributed systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200460"},"PeriodicalIF":0.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent gear decision method for vehicle automatic transmission system based on data mining","authors":"Yong Wang,&nbsp;Jianfeng Zeng,&nbsp;Pengfei Du,&nbsp;Huachao Xu","doi":"10.1016/j.iswa.2024.200459","DOIUrl":"10.1016/j.iswa.2024.200459","url":null,"abstract":"<div><div>The gear decision logic of automatic transmission directly affects the vehicle's dynamic, fuel economic, and comfort performance. This study employs data mining techniques to address the issues of low adaptability and low recognition rate in the intelligent gear decision of vehicle automatic transmission systems. The research further proposes the utilization of Kalman filter, Hidden Markov Models, and Long Short-Term Memory networks for condition feature recognition and time series classification. Subsequently, dynamic programming algorithms are employed to optimize intelligent gear decisions. Combining driver intent and driving environment, an intelligent gear decision method is formulated. The results indicate that, during a 430 s driving segment, the intelligent gear decision method consumes only 464 mL of fuel, closely resembling the economic strategy's 457 mL, with a gear shift frequency of 53, significantly better than the 79 shifts in the economic strategy. Moreover, the error rate for slope condition recognition is only 0.062 %. In a 200 s coupled condition, the intelligent gear decision results in fuel consumption of 207 mL, approximating the actual vehicle's 219 mL, while power-shifting consumes 316 mL, and economic shifting only 202mL. This study not only improves the accuracy of gear decisions but also effectively enhances vehicle operational efficiency, providing valuable insights for future automatic transmission systems with significant practical value.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200459"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and implementation of EventsKG for situational monitoring and security intelligence in India: An open-source intelligence gathering approach","authors":"Hashmy Hassan ,&nbsp;Sudheep Elayidom ,&nbsp;M.R. Irshad ,&nbsp;Christophe Chesneau","doi":"10.1016/j.iswa.2024.200458","DOIUrl":"10.1016/j.iswa.2024.200458","url":null,"abstract":"<div><div>This paper presents a method to construct and implement an Events Knowledge Graph (EventsKG) for security-related open-source intelligence gathering, focusing on event exploration for situation monitoring in India. The EventsKG is designed to process news articles, extract events of national security significance, and represent them in a consistent and intuitive manner. This method utilizes state-of-the-art natural language understanding techniques and the capabilities of graph databases to extract and organize events. A domain-specific ontology is created for effective storage and retrieval. In addition, we provide a user-friendly dashboard for querying and a complete visualization of events across India. The effectiveness of the EventsKG is assessed through a human evaluation of the information retrieval quality. Our approach contributes to rapid data availability and decision-making through a comprehensive understanding of events, including local events, from every part of India in just a few clicks. The system is evaluated against a manually annotated dataset and by involving human evaluators through a feedback survey, and it has shown good retrieval accuracy. The EventsKG can also be used for other applications such as threat intelligence, incident response, and situational awareness.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200458"},"PeriodicalIF":0.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ideological orientation and extremism detection in online social networking sites: A systematic review","authors":"Kamalakkannan Ravi,&nbsp;Jiann-Shiun Yuan","doi":"10.1016/j.iswa.2024.200456","DOIUrl":"10.1016/j.iswa.2024.200456","url":null,"abstract":"<div><div>The rise of social networking sites has reshaped digital interactions, becoming fertile grounds for extremist ideologies, notably in the United States. Despite previous research, understanding and tackling online ideological extremism remains challenging. In this context, we conduct a systematic literature review to comprehensively analyze existing research and offer insights for both researchers and policymakers. Spanning from 2005 to 2023, our review includes 110 primary research articles across platforms like Twitter (X), Facebook, Reddit, TikTok, Telegram, and Parler. We observe a diverse array of methodologies, including natural language processing (NLP), machine learning (ML), deep learning (DL), graph-based methods, dictionary-based methods, and statistical approaches. Through synthesis, we aim to advance understanding and provide actionable recommendations for combating ideological extremism effectively on online social networking sites.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200456"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization of power networks integrating electric vehicles and wind energy","authors":"Peifang Liu ,&nbsp;Jiang Guo ,&nbsp;Fangqing Zhang ,&nbsp;Ye Zou ,&nbsp;Junjie Tang","doi":"10.1016/j.iswa.2024.200452","DOIUrl":"10.1016/j.iswa.2024.200452","url":null,"abstract":"<div><div>In the ever-evolving landscape of power networks, the integration of diverse sources, including electric vehicles (EVs) and renewable energies like wind power, has gained prominence. With the rapid proliferation of plug-in electric vehicles (PEVs), their optimal utilization hinges on reconciling conflicting and adaptable targets, including facilitating vehicle-to-grid (V2 G) connectivity or harmonizing with the broader energy ecosystem. Simultaneously, the inexorable integration of wind resources into power networks underscores the critical need for multi-purpose planning to optimize production and reduce costs. This study tackles this multifaceted challenge, incorporating the presence of EVs and a probabilistic wind resource model. Addressing the complexity of the issue, we devise a multi-purpose method grounded in collective competition, effectively reducing computational complexity and creatively enhancing the model's performance with a Pareto front optimality point. To discern the ideal response, fuzzy theory is employed. The suggested pattern is rigorously tested on two well-established IEEE power networks (30- and 118-bus) in diverse scenarios featuring windmills and PEV producers, with outcomes showcasing the remarkable excellence of our multi-purpose framework in addressing this intricate issue while accommodating uncertainty.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200452"},"PeriodicalIF":0.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}