An efficient artificial intelligence approach for early detection of cross-site scripting attacks

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 11, P. 100466 - 100466

Published: April 21, 2024

Cross-Site Scripting (XSS) attacks continue to pose a significant threat web applications, compromising the security and integrity of user data. XSS is application vulnerability where malicious scripts are injected into websites, allowing attackers execute arbitrary code in victim's browser. The consequences can be severe, ranging from financial losses sensitive information. enable deface distribute malware, or launch phishing campaigns, trust reputation affected organizations. This study proposes an efficient artificial intelligence approach for early detection attacks, utilizing machine learning deep approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as Term Frequency-Inverse Document Frequency (TFIDF), applied compared evaluate results. We introduce novel named LSTM-TFIDF (LSTF) extraction, which combines temporal TFIDF features cross-site scripting dataset, resulting new set. Extensive research experiments demonstrate that random forest method achieved high performance 0.99, outperforming state-of-the-art approaches using proposed features. A k-fold cross-validation mechanism utilized validate methods, hyperparameter tuning further enhances attack detection. have Explainable Artificial Intelligence (XAI) understand interpretability transparency model detecting attacks. makes valuable contribution growing body knowledge on provides developers practitioners enhance applications.

Language: Английский

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LLM-AE-MP: Web attack detection using a large language model with autoencoder and multilayer perceptron

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126982 - 126982

Published: Feb. 1, 2025

Language: Английский

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Preventing Road Accidents Through Early Detection of Driver Behavior Using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 138457 - 138471

Published: Jan. 1, 2023

Driver behavior refers to the actions and attitudes of individuals behind wheel a vehicle. Poor driving can have serious consequences, including accidents, injuries, fatalities. One main disadvantages poor is increased risk road higher insurance premiums, fines, even criminal charges. The primary aim our study detect driver early with high-performance scores. publicly available smartphone motion sensor data utilized conduct experiments. A novel LR-RFC (Logistic Regression Random Forest Classifier) method proposed for feature engineering. combines logistic regression random forest classifier engineering from data. original input into method, generating new probabilistic features. newly extracted features are then applied machine learning methods predicting behavior. results show that approach achieves highest performance score. Extensive experiments demonstrate achieved score 99% using method. validated k-fold cross-validation hyperparameter optimization. Our has potential revolutionize detection avoid accidents.

Language: Английский

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Automated Network Incident Identification through Genetic Algorithm-Driven Feature Selection

Electronics, Journal Year: 2024, Volume and Issue: 13(2), P. 293 - 293

Published: Jan. 9, 2024

The cybersecurity landscape presents daunting challenges, particularly in the face of Denial Service (DoS) attacks such as DoS Http Unbearable Load King (HULK) and GoldenEye attacks. These malicious tactics are designed to disrupt critical services by overwhelming web servers with requests. In contrast attacks, there exists nefarious Operating System (OS) scanning, which exploits vulnerabilities target systems. To provide further context, it is essential clarify that NMAP, a widely utilized tool for identifying host OSes vulnerabilities, not inherently but dual-use legitimate applications, asset inventory company networks. Additionally, Domain Name (DNS) botnets can be incredibly damaging they harness numerous compromised devices inundate DNS traffic. This online services, leading downtime, financial losses, reputational damage. Furthermore, used other activities like data exfiltration, spreading malware, or launching cyberattacks, making them versatile cybercriminals. As attackers continually adapt modify specific attributes evade detection, our paper introduces an automated detection method requires no expert input. innovative approach identifies distinct characteristics botnet HULK OS-Scanning, explicitly using NMAP tool, even when alter their tactics. By harnessing representative dataset, proposed ensures robust against varying attack parameters behavioral shifts. heightened resilience significantly raises bar attempting conceal activities. Significantly, delivered outstanding outcomes, mid 95% accuracy categorizing OS scanning 100% proficiently discerning between malevolent harmless network packets. Our code dataset made publicly available.

Language: Английский

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A novel meta learning based stacked approach for diagnosis of thyroid syndrome

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(11), P. e0312313 - e0312313

Published: Nov. 1, 2024

Thyroid syndrome, a complex endocrine disorder, involves the dysregulation of thyroid gland, impacting vital physiological functions. Common causes include autoimmune disorders, iodine deficiency, and genetic predispositions. The effects syndrome extend beyond itself, affecting metabolism, energy levels, overall well-being. is associated with severe cases dysfunction, highlighting potentially life-threatening consequences untreated or inadequately managed disorders. This research aims to propose an advanced meta-learning approach for timely detection syndrome. We used standard thyroid-balanced dataset containing 7,000 patient records apply machine-learning methods. proposed novel model based on unique stack K-Neighbors (KN) Random Forest (RF) models. Then, Logistic Regression (LR) built collective experience stacked For first time, KRL (KN-RF-LR) method employed effective diagnosis Extensive experiments illustrated that outperformed state-of-the-art approaches, achieving impressive performance accuracy 98%. vindicated scores through k-fold cross-validation enhanced using hyperparameter tuning. Our revolutionized contributing enhancement human life by reducing mortality rates.

Language: Английский

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Sql injection detection algorithm based on Bi-LSTM and integrated feature selection

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(4)

Published: March 12, 2025

Language: Английский

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Towards lightweight model using non-local-based graph convolution neural network for SQL injection detection

Egyptian Informatics Journal, Journal Year: 2025, Volume and Issue: 30, P. 100684 - 100684

Published: May 1, 2025

Language: Английский

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Efficient and Secure Data Storage for Future Networks: Review and Future Opportunities

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 102619 - 102636

Published: Jan. 1, 2024

With the increase in number of Internet Things (IoT) applications, reliance on robust networking, reliable communications, and efficient secure data storage is increasing day by day. Many these applications such as cellular transportation networks Medical generate huge volumes that need to be stored analyzed insights make decisions related applications. Security a paramount concern, particularly current cyber security threat landscape. It important design novel techniques algorithms protect against upcoming attacks. This paper provides brief overview challenges faced mechanisms. In comparison existing review papers this area, work identifies three major for conducts comprehensive recent advancements techniques. These include Blockchain algorithms, encryption mechanisms, deduplication storage. Finally, highlights future opportunities networks.

Language: Английский

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A comprehensive survey on intrusion detection algorithms

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 121, P. 109863 - 109863

Published: Nov. 23, 2024

Language: Английский

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Advancements in Machine Learning Techniques for Detecting Structured Query Language Injection Attacks – a Systematic Literature Review

Published: Jan. 1, 2024

Language: Английский

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