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Enhancing cyber threat detection with an improved artificial neural network model

Data science and Management 2025年第1期8卷 107-115页

作者： Toluwase Sunday Oyinloye Micheal Olaolu Arowolo Rajesh Prasad Department of Computer Science African University of Science and TechnologyAbuja900107Nigeria Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65201USA

Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for *** overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning *** study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced *** to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard *** the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.

关键词： Cybersecurity Intrusion detection Deep learning Artificial neural network Imbalanced data classification

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Machine learning in crime prediction

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2887-2913页

作者： Jenga, Karabo Catal, Cagatay Kar, Gorkem Department of Computer Engineering Bahcesehir University Istanbul Turkey Department of Computer Science and Engineering Qatar University Doha Qatar

Predicting crimes before they occur can save lives and losses of property. With the help of machine learning, many researchers have studied predicting crimes extensively. In this paper, we evaluate state-of-the-art crime prediction techniques that are available in the last decade, discuss possible challenges, and provide a discussion about the future work that could be conducted in the field of crime prediction. Although many works aim to predict crimes, the datasets they used and methods that are applied are numerous. Using a Systematic Literature Review (SLR) methodology, we aim to collect and synthesize the required knowledge regarding machine learning-based crime prediction and help both law enforcement authorities and scientists to mitigate and prevent future crime occurrences. We focus primarily on 68 selected machine learning papers that predict crime. We formulate eight research questions and observe that the majority of the papers used a supervised machine learning approach, assuming that there is prior labeled data, and however in some cases, there is no labeled data in real-world scenarios. We have also discussed the main challenges found while conducting some of the studies by the researchers. We consider that this research paves the way for further research to help governments and countries fight crime and decrease this for better safety and security. © 2023, The Author(s).

关键词： Forecasting

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Pentago SnW:An Improved Spray and Wait Protocol for Delay Tolerant Wireless Sensor Networks

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China Communications 2025年第3期22卷 104-114页

作者： Idris Afzal Shah Mushtaq Ahmed Raghavendra Singh Department of Computer Science and Engineering Malaviya National Institute of TechnologyJaipur302017India

Delay tolerant wireless sensor networks(DTWSN)is a class of wireless network that finds its deployment in those application scenarios which demand for high packet delivery ratio while maintaining minimal overhead in order to prolong network lifetime;owing to resource-constrained nature of *** fundamental requirement of any network is routing a packet from its source to *** of a routing algorithm depends on the number of network parameters utilized by that routing *** the recent years,various routing protocol has been developed for the delay tolerant networks(DTN).A routing protocol known as spray and wait(SnW)is one of the most widely used routing algorithms for *** this paper,we study the SnW routing protocol and propose a modified version of it referred to as Pentago SnW which is based on pentagonal number *** to binary SnW shows promising results through simulation using real-life scenarios of cars and pedestrians randomly moving on a map.

关键词： binary SnW delay tolerant network Pentago SnW spray and wait routing

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Generalized Simultaneous Perturbation-Based Gradient Search With Reduced Estimator Bias

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IEEE Transactions on Automatic Control 2025年第7期70卷 4687-4702页

作者： Pachalyl, Soumen Bhatnagarz, Shalabh Prashanth, L.A. TCS Research Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Indian Institute of Science Department of Computer Science and Automation The Robert Bosch Centre for Cyber Physical Systems Bangalore India Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

We present a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator is guided by the desired level of accuracy. We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation (GSPSA) estimators and later present the balanced versions (B-GSPSA) of these. We extend this idea further and present the generalized smoothed functional (GSF) and generalized random directions stochastic approximation (GRDSA) estimators, respectively, as well as their balanced variants. We show that estimators within any specified class requiring more number of function measurements result in lower estimator bias. We present a detailed analysis of both the asymptotic and non-asymptotic convergence of the resulting stochastic approximation schemes. We further present a series of experimental results with the various GSPGS estimators on the Rastrigin and quadratic function objectives. Our experiments are seen to validate our theoretical findings. © 1963-2012 IEEE.

关键词： Stochastic systems

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Computation of Graph Fourier Transform Centrality Using Graph Filter

IEEE Open Journal of Circuits and Systems

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IEEE Open Journal of Circuits and Systems 2024年 5卷 69-80页

作者： Tseng, Chien-Cheng Lee, Su-Ling National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung824005 Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan711301 Taiwan

In this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational complexity of GFTC, a linear algebra method based on Frobenius norm of error matrix is applied to convert the spectral-domain GFTC computation task to vertex-domain one such that GFTC can be computed by using polynomial graph filtering method. There are two kinds of designs of graph filters to be studied. One is the graph-aware method;the other is the graph-unaware method. The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. Finally, the centrality computations of social network, metro network and sensor network are used to demonstrate the effectiveness of the proposed GFTC computation method using graph filter. © 2020 IEEE.

关键词： Complex networks

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MCM-Llama: A Fine-Tuned Large Language Model for Real-Time Threat Detection through Security Event Correlation 4

MCM-Llama: A Fine-Tuned Large Language Model for Real-Time T...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Diakhame, Mouhamadou Lamine Diallo, Cherif Mejri, Mohamed Gaston Berger University Department of Computer Science Saint Louis Senegal Laval University Department of Computer Science & Software Engineering Quebec Canada

ISBN: (纸本)9798350395914

The correlation of security alerts is crucial for effective threat detection and mitigation in modern cybersecurity landscapes. With the exponential growth in the volume of generated alerts, there arises an imperative to continually develop new, efficient methods for alert correlation. The use of Large Language Models (LLMs) presents new avenues for enhancing real-time threat detection in the field of cybersecurity. In this paper, we introduce MCM-Llama, a fine-tuned language model aimed at improving security event correlation for real-time threat detection. Building upon our prior research, we augment the current framework by integrating MCM-Llama and introducing a novel architecture to practically implement our proposition. In doing so, we replace the correlation module previously based on Named Entity Recognition (NER) and semantic similarity with a fine-tuned Large Language Model (LLM). This transition allows us to capitalize on the advanced capabilities of LLM in comprehending and correlating security events dynamically. Through this enhancement, we aim to significantly improve the accuracy and efficiency of our threat detection system while leveraging the full potential of LLM technology in cybersecurity. Our experimental results validate the efficacy of the proposed approach by significantly improving the correlation rate, thereby reinforcing the pivotal role of Large Language Models (LLMs) in advancing security event correlation and threat detection methodologies. © 2024 IEEE.

关键词： Semantics

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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Quick Hidden Layer Size Tuning in ELM for Classification Problems

Mendel

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Mendel 2024年第1期30卷 1-14页

作者： Albtoush, Audi Fernandez-Delgado, Manuel Maarouf, Haitham Al Nawaiseh, Asmaa Jameel Faculty of Computer Science and Information Technology Jerash University Jordan Santiago De Compostela University Spain Software Engineering Department Mut’ah university Jordan

The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98% with equal or better generalization ability. © 2024, Brno University of Technology. All rights reserved.

关键词： Knowledge acquisition

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Optimal feature with modified bi-directional long short-term memory for big data classification in healthcare application

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4441-4450页

作者： Kamble, Subhash Arunalatha, J.S. Venugopal, K.R. Computer Science and Engineering Bangalore University Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore University

Artificial intelligence together with its applications are advancing in all fields, particularly medical science. A considerable quantity of clinical data is available, yet the vast majority of it is wasted. It will be valuable in diagnosing human life early if it is applied effectively. An excellent classification system assists medical practitioners in providing a suitable diagnosis at a younger stage. Medical data typically contain a large number of features, and including all of them in decision making may result in over fitting of the classification model, lowering accuracy. As a result, an effective dimensionality reduction strategy that minimizes the amount of features while also improving classification accuracy is required. Therefore in this study, efficient dimension reduction-based medical data classification is proposed. The proposed approach is made up of three stages namely, pre-processing, feature selection, and classification. Initially, the medical data are collected and pre-processed. Then, to minimize computational complexity and time consumption, optimal features are selected using the adaptive rat swarm optimization (ARWO) algorithm. Then, the selected features are given to the modified bi-directional long short-term memory (MBi-LSTM) classifier to classify data as normal or abnormal. Then, the performance of the model is evaluated using different datasets and the effectiveness of the proposed model is analyzed in terms of different metrics. The proposed model implemented in python. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ARWO Big data MBi-LSTM Medical data Optimal features

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software development processes in ocean system modeling

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第2期13卷 47-69页

作者： Reiner Jung Sven Gundlach Wilhelm Hasselbring Software Engineering Group Department of Computer Science Kiel UniversityKielGermany

Scientific modeling provides mathematical abstractions of real-world systems and builds software as implementations of these mathematical *** science is a multidisciplinary discipline developing scientific models and simulations as ocean sys-tem models that are an essential research *** software engineering and information systems research,modeling is also an essential *** particular,business process modeling for business process management and systems engineering is the activity of representing processes of an enterprise,so that the current process may be analyzed,improved and *** this paper,we employ process modeling for analyzing sci-entific software development in ocean science to advance the state in engineering of ocean system models and to better understand how ocean system models are developed and maintained in ocean *** interviewed domain experts in semi-structured inter-views,analyzed the results via thematic analysis,and modeled the results via the Busi-ness Process Modeling Notation(BPMN).The processes modeled as a result describe an aspired state of software development in the domain,which are often not(yet)*** enables existing processes in simulation-based system engineering to be improved with the help of these process models.

关键词： Scientific modeling software development processes process modeling the-matic analysis

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