Cybersecurity in the AI era: analyzing the impact of machine learning on intrusion detection

Knowledge and Information Systems, Год журнала: 2025, Номер unknown

Опубликована: Фев. 19, 2025

Язык: Английский

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Tackling class imbalance in computer vision: a contemporary review

Artificial Intelligence Review, Год журнала: 2023, Номер 56(S1), С. 1279 - 1335

Опубликована: Июль 20, 2023

Язык: Английский

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MalHyStack: A hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis

Intelligent Systems with Applications, Год журнала: 2023, Номер 20, С. 200283 - 200283

Опубликована: Окт. 5, 2023

Since the advent of malware, it has reached a toll in this world that exchanges billions data daily. Millions people are victims it, and numbers not decreasing as year goes by. Malware is various types which obfuscation special kind. Obfuscated malware detection necessary usually detectable prevalent real world. Although numerous works have already been done field so far, most these still need to catch up at some points, considering scope exploration through recent extensions. In addition that, application hybrid classification model yet be popularized field. Thus, paper, novel named, MalHyStack, proposed for detecting such obfuscated within network. This working built incorporating stacked ensemble learning scheme, where conventional machine algorithms namely, Extremely Randomized Trees Classifier (ExtraTrees), Extreme Gradient Boosting (XgBoost) Classifier, Random Forest used first layer then followed by deep second stage. Before utilizing detection, an optimum subset features selected using Pearson correlation analysis improved accuracy more than 2 % multiclass classification. It also reduces time complexity approximately two three times binary classification, respectively. For evaluating performance model, recently published balanced dataset named CIC-MalMem-2022 used. Utilizing dataset, overall experimental results represent superior when compared existing models.

Язык: Английский

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A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms

IEEE Access, Год журнала: 2023, Номер 11, С. 121118 - 121141

Опубликована: Янв. 1, 2023

The increasing sophistication of malware threats has led to growing concerns in the anti-malware community, as poses a significant danger online users despite availability numerous defense solutions. This study aims comprehensively review evolution and current attack trends identify effective mechanisms. It reviews most recent journal articles, conference proceedings, reports, resources published during last five years. We extensively landscape from 1970 present analyze types, operational mechanisms, vectors, vulnerabilities. Furthermore, we explore different defensive strategies developed response these evolving threats. Our findings highlight trends, including surge cryptojacking, attacks on mobile devices, Internet Things ransomware, advanced persistent threats, supply chain attacks, fileless malware, cloud-based exploitation remote employees, edge networks. Defense have also evolved parallel, emphasizing multilayered security measures counter dynamic highlights critical need for robust, combat malware. Despite advancements, some open challenges research gaps remain, which require further innovation. serves valuable guide cybersecurity professionals by identifying key challenges, limitations, future opportunities.

Язык: Английский

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GBERT: A Hybrid Deep Learning Model Based on GPT-BERT for Fake News Detection

Heliyon, Год журнала: 2024, Номер 10(16), С. e35865 - e35865

Опубликована: Авг. 1, 2024

The digital era has expanded social exposure with easy internet access for mobile users, allowing global communication. Now, people can get to know what is going on around the globe just a click; however, this also resulted in issue of fake news. Fake news content that pretends be true but actually false and disseminated defraud. poses threat harmony, politics, economy, public opinion. As result, bogus detection become an emerging research domain identify given piece text as genuine or fraudulent. In paper, new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) proposed leverages combination pre-trained transformer (GPT) (BERT) addresses classification problem. This combines best features both cutting-edge techniques-BERT's deep contextual understanding generative capabilities GPT-to create comprehensive representation text. Both GPT BERT are fine-tuned two real-world benchmark corpora have attained 95.30 % accuracy, 95.13 precision, 97.35 sensitivity, 96.23 F1 score. statistical test results indicate effectiveness suggest it promising approach eradicating landscape.

Язык: Английский

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Image-based malware detection based on convolution neural network with autoencoder in Industrial Internet of Things using Software Defined Networking Honeypot

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108374 - 108374

Опубликована: Апрель 11, 2024

Язык: Английский

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Utilizing customized CNN for brain tumor prediction with explainable AI

Heliyon, Год журнала: 2024, Номер 10(20), С. e38997 - e38997

Опубликована: Окт. 1, 2024

Timely diagnosis of brain tumors using MRI and its potential impact on patient survival are critical issues addressed in this study. Traditional DL models often lack transparency, leading to skepticism among medical experts owing their "black box" nature. This study addresses gap by presenting an innovative approach for tumor detection. It utilizes a customized Convolutional Neural Network (CNN) model empowered three advanced explainable artificial intelligence (XAI) techniques: Shapley Additive Explana-tions (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (Grad-CAM). The utilized the BR35H dataset, which includes 3060 images encompassing both tumorous non-tumorous cases. proposed achieved remarkable training accuracy 100 % validation 98.67 %. Precision, recall, F1 score metrics demonstrated exceptional performance at 98.50 %, confirming Detailed result analysis, including confusion matrix, comparison with existing models, generalizability tests other datasets, establishes superiority sets new benchmark accuracy. By integrating CNN XAI techniques, research enhances trust AI-driven diagnostics offers promising pathway early detection potentially life-saving interventions.

Язык: Английский

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Educational sustainability: A multi-scale elementary school resource distribution variability from China

Heliyon, Год журнала: 2025, Номер 11(2), С. e41846 - e41846

Опубликована: Янв. 1, 2025

The allocation of educational resources is a critical factor in the sustainable development education. This study developed framework for assessing resource indicators integrating multi-scale and multi-dimensional indicators. China's geographic regions periods were analyzed intra-regional variability, inter-regional variability across different periods, spatio-temporal coupling characteristics. reveals that internal primary education eastern, central, western China has significantly decreased, with Gini coefficient region dropping from 0.40 2006 to 0.12 2020. country's macro-policy regulation steadily reduced variation physical between regions. spatiotemporally coupled demonstrate Rural Revitalization Strategy substantially enhanced rural areas. However, regional indicator teachers per capita graduate degrees continues widen. coupling-cumulative variance ratio (CVR) eastern urban areas increased 1836.53 %, difference 1117.58 %. Based on these findings, discusses characteristics each sector, utilizing methods supervision, cooperation, communication, guidance create dynamic mechanism maintaining equity resources. aims provide methodological data recommendations analyzing equitable distribution settings other developing nations

Язык: Английский

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A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile Platforms

ACM Computing Surveys, Год журнала: 2023, Номер 56(6), С. 1 - 41

Опубликована: Дек. 20, 2023

Malware is one of the most common and severe cyber threats today. infects millions devices can perform several malicious activities including compromising sensitive data, encrypting crippling system performance, many more. Hence, malware detection crucial to protect our computers mobile from attacks. Recently, Deep Learning (DL) has emerged as promising technologies for detecting malware. The recent high production variants against desktop platforms makes DL algorithms powerful approaches building scalable advanced models they handle big datasets. This work explores current deep learning attacks on Windows, Linux, Android platforms. Specifically, we present different categories algorithms, network optimizers, regularization methods. Different loss functions, activation frameworks implementing are discussed. We also feature extraction a review DL-based above Furthermore, this presents major research issues future directions further advance knowledge in field.

Язык: Английский

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A Survey on the Applications of Semi-supervised Learning to Cyber-security

ACM Computing Surveys, Год журнала: 2024, Номер 56(10), С. 1 - 41

Опубликована: Апрель 11, 2024

Machine Learning’s widespread application owes to its ability develop accurate and scalable models. In cyber-security, where labeled data is scarce, Semi-Supervised Learning (SSL) emerges as a potential solution. SSL excels at tasks challenging traditional supervised unsupervised algorithms by leveraging limited alongside abundant unlabeled data. This article presents comprehensive survey of in focusing on countering diverse cybercrimes, particularly intrusion detection. Despite potential, notable research gap persists, with few recent studies comprehensively reviewing SSL’s cyber-security. study examines state-of-the-art techniques tailored for cyber-security address this gap. Relevant methods are identified, their effectiveness evaluated empower researchers practitioners insights enhance measures. work sheds light addressing scarcity domains addition outlining new directions advance crucial field. By bridging gap, manuscript paves the way enhanced cyber-threat detection mitigation an increasingly interconnected world.

Язык: Английский

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