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Enhanced Intrusion Detection with Advanced Deep Features and Ensemble Classifier Techniques

SN computer science 2025年第4期6卷 1-17页

作者： Toralkar, Pawan Mainalli, Kavita Allagi, Shridhar Debnath, Sanjoy Kumar Bagchi, Susama Leong, Wai Yie Khan, Muhammad Numan Ali Department of Artificial Intelligence Madanapalle Institute of Technology and Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering KLEIT: KLE Institute of Technology Karnataka Hubballi India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India INTI International University and Colleges Negeri Sembilan Nilai Malaysia College of Computing and Applied Sciences University of Technology and Applied Sciences Shinas Oman

Intrusion detection is crucial for modern cyber-security, aiming to detect and mitigate unauthorized access and malicious activities within networks. Traditional systems often rely on linear models, which may fail to capture complex patterns in advanced threats. This study proposes a heuristic approach to select the best deep features through an ensemble framework consisting of Random Forest (RF) and Support Vector Machine (SVM) classifiers using soft voting. The novelty of this work is the deep feature extraction through Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Autoencoder for intrusion detection. These extracted features were fed separately for classification. In order to balance the explainability and accuracy, the ensemble classifier consisting of RF and SVM, uses feature importance ranking (RF) and unambiguous decision bounds (SVM) to provide partial interpretability. Evaluating the NSL-KDD and CICDDoS2019 datasets, our ensemble model with CNN features achieves near-perfect accuracy of 99.99% and 99.91% respectively. RNN and Autoencoder models also show high performance, each reaching 99.3% accuracy on the CICDDoS2019 dataset with Soft Voting classifiers. This work highlights the importance of the ensemble method with better feature selection in intrusion detection. The proposed system outperformed the standard methods by providing a robust, scalable solution for organizational security. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution neural network (CNN) Intrusion detection Random forest (RF) Recurrent neural network (RNN) Voting classification (Voting)

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Randomized Local Search vs. NSGA-II vs. Frequency Fitness Assignment on The Traveling Tournament Problem 16th

Randomized Local Search vs. NSGA-II vs. Frequency Fitness As...

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16th International Joint Conference on Computational intelligence, IJCCI 2024

作者： Xiang, Cao Wu, Zhize Den Berg, Daan van Weise, Thomas Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Jinxiu Dadao 99 Anhui Hefei230601 China Department of Computer Science Vrije Universiteit Amsterdam De Boelelaan 1111 Amsterdam1081 HV Netherlands

ISBN: (纸本)9789897587214

The classical compact double-round robin traveling tournament problem (TTP) asks us to schedule the games of n teams in a tournament such that each team plays against every other team twice, once at home and once away (doubleRoundRobin constraint). The maxStreak constraint prevents teams from having more than three consecutive home or away games. The noRepeat constraint demands that, before two teams can play against each other the second time, they must at least play one other game in between. The goal is to find a game plan observing all of these constraints and having the overall shortest travel length. We define a gamepermutation based encoding that allows for representing game plans with arbitrary numbers of constraint violations and tackle the TTP as a bi-objective problem minimizing both the number of constraint violations and the travel length by applying the well-known NSGA-II. We combine both objectives in a lexicographic prioritization scheme and also apply the randomized local search RLS to this single-objective variant of the problem. We realize that Frequency Fitness Assignment (FFA), which makes algorithms invariant under all injective transformations of the objective function value, would also make optimization algorithms invariant under all lexicographic prioritization schemes for multi-objective problems. The FRLS, i.e., the RLS with FFA plugged in, would therefore solve both possible prioritizations of our TTP variants at once. We thus also explore its performance on the TTP. We find that RLS performs surprisingly well and can find game plans without constraint violations reliably until a scale of 36 teams, whereas FRLS and NSGA-II have an advantage on small- and mid-scale problems. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Local search (optimization)

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Extending adversarial attacks to produce adversarial class probability distributions

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 491-532页

作者： Jon Vadillo Roberto Santana Jose A. Lozano Department of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Donostia Spain Department of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Donostia Spain and Basque Center for Applied Mathematics Bilbao Spain

Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models can be easily fooled by the addition of imperceptible yet malicious perturbations to natural inputs. These altered inputs are known in the literature as adversarial examples. In this paper, we propose a novel probabilistic framework to generalize and extend adversarial attacks in order to produce a desired probability distribution for the classes when we apply the attack method to a large number of inputs. This novel attack paradigm provides the adversary with greater control over the target model, thereby exposing, in a wide range of scenarios, threats against deep learning models that cannot be conducted by the conventional paradigms. We introduce four different strategies to efficiently generate such attacks, and illustrate our approach by extending multiple adversarial attack algorithms. We also experimentally validate our approach for the spoken command classification task and the Tweet emotion classification task, two exemplary machine learning problems in the audio and text domain, respectively. Our results demonstrate that we can closely approximate any probability distribution for the classes while maintaining a high fooling rate and even prevent the attacks from being detected by label-shift detection methods.

关键词： adversarial examples deep neural networks robust classification class probability distributions linear programming

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Italian-Chinese Neural Machine Translation: results and lessons learnt 23

Italian-Chinese Neural Machine Translation: results and less...

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3rd ACM Conference on Information Technology for Social Good, GoodIT 2023

作者： Delnevo, Giovanni Im, Marcus Tse, Rita Lam, Chan-Tong Tang, Su-Kit Salomoni, Paola Pau, Giovanni Ghini, Vittorio Mirri, Silvia Department of Computer Science and Engineering University of Bologna Cesena Italy Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University China

ISBN: (纸本)9798400701160

Today, access to the Internet provides access to various forms of knowledge like free online lecture series offered by prestigious universities, massive open online courses, films and books, and Wikipedia. In addition, it is possible to join online communities on any topic of interest, get to know people with common interests, exchange thoughts and participate in debates. To enable access to these unprecedented knowledge bases, it is crucial to be able to translate texts into any language known by users. For this reason, Machine Translation has been a very active research field for the last thirty years. In this paper, we investigate the task of Chinese-Italian translations by exploiting Neural Machine Translation approaches. We trained several deep neural networks starting from two already available datasets containing Chinese-Italian parallel corpora. Then, we compared their performance against some of the most common machine translation services freely available online. In particular, we take advantage of Microsoft Translator, Google Translate, DeepL, and ModernMT. © 2023 Owner/Author.

关键词： Neural machine translation

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TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models

arXiv

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arXiv 2025年

作者： Bulathwela, Sahan Van Niekerk, Daniel Shipton, Jarrod Perez-Ortiz, Maria Rosman, Benjamin Shawe-Taylor, John Centre for Artificial Intelligence Department of Computer Science University College London United Kingdom School of Computer Science and Applied Mathematics University of The Witwatersrand South Africa

Personalised education is one of the domains that can greatly benefit from the most recent advances in artificial intelligence (AI) and Large Language Models (LLM). However, it is also one of the most challenging applications due to the cognitive complexity teaching effectively while personalising the learning experience to suite independent learners. We hypothesise that one promising approach to excelling such demanding use-cases is using a society of minds. In this chapter, we present TrueReason, an exemplar personalised learning system that integrates a multitude of specialised AI models that can mimic micro skills that are composed together by a LLM to operationalise planning and reasoning. The architecture of the initial prototype is presented while describing two micro skills that have been incorporated in the prototype. The proposed system demonstrates the first step in building sophisticated AI systems that can take up very complex cognitive tasks that are demanded by domains such as education. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT

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computers, Materials & Continua 2024年第11期81卷 3395-3412页

作者： Arar Al Tawil Laiali Almazaydeh Doaa Qawasmeh Baraah Qawasmeh Mohammad Alshinwan Khaled Elleithy Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan College of Engineering Abu Dhabi UniversityAbu DhabiAl AinP.O.Box 1790United Arab Emirates Faculty of Artificial Intelligence Al-Balqa Applied UniversitySalt19117Jordan Department of Civil and Construction Engineering Western Michigan UniversityKalamazooMI 49008USA MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science and Engineering University of BridgeportBridgeportCT 06604USA

Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as *** study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing *** study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron *** best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching *** study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.

关键词： Attacks email phishing machine learning security representations from transformers(BERT) text classifeir natural language processing(NLP)

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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A segment-wise extraction of multivariate time-series features for Grassmann clustering

A segment-wise extraction of multivariate time-series featur...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Heo, Sebin Nuriye, Bezawit Habtamu Oh, Beomseok Gyeongsang National University Dept. of Applied Life Chemistry Jinju Korea Republic of Gyeongsang National University Dept. of Computer Science Jinju Korea Republic of Seoul National University of Science and Technology Dept. of Applied Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350320213

In this paper, a novel approach of extracting features from multivariate time-series (MTS) with different time lengths, is proposed to enhance the clustering accuracy. Particularly, the feature extraction is conducted on time-sample segments of MTS, in which several segments are defined without overlapping. As for feature extractor, the conventional two-dimensional principal component analysis (2DPCA) is deployed due to its proven effectiveness in feature representation. Our experimental results show that the proposed segment-wise extraction of 2DPCA features is helpful in enhancing the clustering accuracy. © 2023 IEEE.

关键词： Clustering Feature Extraction Grassmann Manifold Multivariate Time-Series

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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CheXNet for the Evidence of Covid-19 Using 2.3K Positive Chest X-rays 4th

CheXNet for the Evidence of Covid-19 Using 2.3K Positive ...

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4th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021

作者： Santosh, Kc Ghosh, Supriti 2AI: Applied Artificial Intelligence Research Lab Computer Science University of South Dakota VermillionSD57069 United States

ISBN: (纸本)9783031070044

CheXNet is not a surprise for Deep Learning (DL) community as it was primarily designed for radiologist-level pneumonia detection in Chest X-rays (CXRs). In this paper, we study CheXNet to analyze CXRs to detect the evidence of Covid-19. On a dataset of size 4, 600 CXRs (2, 300 Covid-19 positive cases and 2, 300 non-Covid cases (Healthy and Pneumonia cases)) and with k(=5) fold cross-validation technique, we achieve the following performance scores: accuracy of 0.98, AUC of 0.99, specificity of 0.98 and sensitivity of 0.99. On such a large dataset, our results can be compared with state-of-the-art results. © 2022, Springer Nature Switzerland AG.

关键词： Large dataset

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