Results 51 to 60 of about 512 (176)

CO2‐Assisted Catalytic Co‐Pyrolysis of Sewage Sludge and Mixed Plastics in a Fluidized Bed Reactor

International Journal of Energy Research, Volume 2026, Issue 1, 2026.
Sewage sludge (SS) and waste plastics are abundant waste streams that require effective treatment. Co‐pyrolysis is a promising option that can alleviate disposal burdens while converting these wastes into value‐added, renewable energy products. This study investigated the catalytic co‐pyrolysis of SS, polyethylene (PE), and polypropylene (PP) in a 1 ...
Guan-Bang Chen, Chun-Teng Chen, Abdurrahman Saydut   +2 more
wiley   +1 more source

An Automated Cross-site Scripting Loopholes Discovery Model [PDF]

Jisuanji gongcheng, 2018
Cross-site Scripting(XSS) attacks pose serious threats to web applications.Before the application is released,detecting them can effectively reduce the risk of vulnerabilities.Aiming at the problems in the current detection of cross-site scripting,such ...
MA Futian,QIAN Xuezhong,SONG Wei
doaj   +1 more source

Pentesting LLM Models With an Automated Framework

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
Artificial intelligence (AI) has become an essential tool in modern cybersecurity, enabling faster and more accurate detection, prevention, and response to threats. Within this landscape, large language models (LLMs) have emerged as versatile systems capable of generating code, providing technical guidance, and automating complex tasks.
Juan Luis López-Delgado, Juan Antonio López-Ramos, Richard Murray   +2 more
wiley   +1 more source

Comparative Analysis of Machine Learning Algorithms for Cross-Site Scripting (XSS) Attack Detection

JOIV: International Journal on Informatics Visualization
Cross-Site Scripting (XSS) attacks pose a significant cybersecurity threat by exploiting vulnerabilities in web applications to inject malicious scripts, enabling unauthorized access and execution of malicious code.
Khairatun Hisan Hamzah, Mohd Zamri Osman, Tumusiime Anthony, Mohd Arfian Ismail, Zubaile Abdullah, Alde Alanda   +5 more
doaj   +1 more source

Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks

IEEE Access, 2020
Models based on deep learning are prone to misjudging the results when faced with adversarial examples. In this paper, we propose an MCTS-T algorithm for generating adversarial examples of cross-site scripting (XSS) attacks based on Monte Carlo tree ...
Xueqin Zhang, Yue Zhou, Songwen Pei, Jingjing Zhuge, Jiahao Chen   +4 more
doaj   +1 more source

Developing a Secure Cyberphysical System for Altitude Chambers

Journal of Computer Networks and Communications, Volume 2026, Issue 1, 2026.
Altitude chambers are used to train crews of the military and civil aviation by creating a high‐altitude environment in which the vital signs of the people in the chamber are monitored to assess their health and identify symptoms that indicate there may be a physical condition that needs to be managed. Human observers monitor the process and react when
Jennifer Aguirre, Sergio Cruz, Carlos Lozano-Garzon, Sandra Rueda, Salvatore Monteleone   +4 more
wiley   +1 more source

Efficient Detection of XSS and DDoS Attacks with Bent Functions

Information
In this paper, we investigate the use of Bent functions, particularly the Maiorana–McFarland (M–M) construction, as a nonlinear preprocessing method to enhance machine learning-based detection systems for Distributed Denial of Service (DDoS) and Cross ...
Shahram Miri Kelaniki, Nikos Komninos
doaj   +1 more source

MLPXSS: An Integrated XSS-Based Attack Detection Scheme in Web Applications Using Multilayer Perceptron Technique

IEEE Access, 2019
Dynamic web applications play a vital role in providing resources manipulation and interaction between clients and servers. The features presently supported by browsers have raised business opportunities, by supplying high interactivity in web-based ...
Fawaz Mahiuob Mohammed Mokbal, Wang Dan, Azhar Imran, Lin Jiuchuan, Faheem Akhtar, Wang Xiaoxi   +5 more
doaj   +1 more source

A Deep Learning Approach Using Optimized LSTM for Anomaly‐Based Network Intrusion Detection

Journal of Engineering, Volume 2026, Issue 1, 2026.
With the exponential rise in cyber threats, anomaly‐based network intrusion detection systems (NIDSs) have become critical for maintaining robust cybersecurity. This article proposes an optimized long short‐term memory (LSTM) deep learning (DL) model specifically designed to detect anomalies in network traffic.
Samia Dardouri, Shikha Binwal
wiley   +1 more source

Boosting Intrusion Detection Accuracy With a Multibranch Deep Learning Framework

Journal of Electrical and Computer Engineering, Volume 2026, Issue 1, 2026.
To overcome the limitations of traditional intrusion detection methods in dealing with high‐dimensional sparse features, multiclass attack classification, and model robustness, this paper presents a fused multibranch intrusion detection model (FMB‐IDM). The proposed framework combines three complementary deep learning components.
Yang Li, Shonak Bansal
wiley   +1 more source