Scripting Attacks Research Articles

Security Analysis of Village Website Against Cross-Site Scripting Attacks

Security Analysis of Village Website Against Cross-Site Scripting Attacks

AUDIT DAN ANALISIS WEBSITE PEMERINTAH MENGGUNAKAN PENGUJIAN PENETRASI SQL INJECTION DAN CROSS SITE SCRIPTING (XSS)

This study aims to analyze the security of government websites, focusing on vulnerabilities caused by SQL Injection and Cross Site Scripting (XSS) attacks. In accordance with Presidential Instruction No. 3 of 2003 on National Policy and Strategy for E-Government Development, government agencies are required to provide digital services through official websites. However, this increase in digitalization presents challenges in the context of cybersecurity. The research applies penetration testing methods to several government websites in East Java, using the OWASP Top 10 as the primary guide. The results reveal that many government websites are vulnerable to SQL Injection and XSS attacks, which could lead to data theft and information manipulation. Recommendations for enhancing security include implementing input validation techniques and regularly updating software. This research contributes to raising cybersecurity awareness in the governmental sector.

Development of Detection and Mitigation of Advanced Persistent Threats Using Artificial Intelligence and Multi-Layer Security on Cloud Computing Infrastructure

This research proposes a novel approach for detecting and mitigating Advanced Persistent Threats (APTs) in cloud computing infrastruc ture, offering more comprehensive protection compared to previous methods. By integrating detection and mitigation, this study addresses the shortcomings of prior research that focused solely on detection. Based on the conducted research, Artificial Intelligence (AI) detected Cross-Site Scripting (XSS) attacks with an accuracy of 0.9951, SQL Injection (SQLI) at 0.9964, and Remote Code Execution (RCE) at 0.9876. In trials against new attacks, the detection success rates reached 70% for XSS, 98% for SQLI, and 100% for RCE. During the deployment phase, the system successfully identified 23.040 out of 108.394 requests as XSS attacks, 2.684 out of 128.750 as SQLI attacks, and 1.135 out of 46.450 as RCE attacks. The detection and mitigation methods were directly tested on cloud server experiencing APT attacks. The daily attacks on the server reached 1.980, with 663.000 requests. Additionally, the number of attacks directed at authentication or sensitive pages reached 17.913.701. Attack mitigation was tested through seven layers of security, including DNS Protection, Config Server Firewall (CSF), OWASP ModSecurity, HTTP middleware, data filter or sanitizer, template engine, and manual mitigation successfully blocking million of persistent attacks. The DNS protection layer successfully mitigated 59,000 out of a total of 19 million requests. The CSF layer mitigated 173 sources IP of DDoS attacks. The ModSecurity layer mitigated 17,916,204 attacks. All attacks were successfully mitigated before reaching the HTTP Middleware stage or next layer. The use of NIST 2.0 standards helps manage security risks through identification, protection, detection, response, and recovery. Test results indicate that this multi-layered system is more efficient and effective in detecting and mitigating attacks compared to traditional methods. However, the complexity of implementation and maintenance poses challenges that must be addressed. This research significantly contributes to a more adaptive and sustainable cybersecurity strategy.

Advancing XSS Detection in IoT over 5G: A Cutting-Edge Artificial Neural Network Approach

The rapid expansion of the Internet of Things (IoT) and the advancement of 5G technology require strong cybersecurity measures within IoT frameworks. Traditional security methods are insufficient due to the wide variety and large number of IoT devices and their limited computational capabilities. With 5G enabling faster data transmission, security risks have increased, making effective protective measures essential. Cross-Site Scripting (XSS) attacks present a significant threat to IoT security. In response, we have developed a new approach using Artificial Neural Networks (ANNs) to identify and prevent XSS breaches in IoT systems over 5G networks. We significantly improved our model’s predictive performance by using filter and wrapper feature selection methods. We validated our approach using two datasets, NF-ToN-IoT-v2 and Edge-IIoTset, ensuring its strength and adaptability across different IoT environments. For the NF-ToN-IoT-v2 dataset with filter feature selection, our Bilayered Neural Network (2 × 10) achieved the highest accuracy of 99.84%. For the Edge-IIoTset dataset with filtered feature selection, the Trilayered Neural Network (3 × 10) achieved the best accuracy of 99.79%. We used ANOVA tests to address the sensitivity of neural network performance to initial conditions, confirming statistically significant improvements in detection accuracy. The ANOVA results validated the enhancements across different feature selection methods, demonstrating the consistency and reliability of our approach. Our method demonstrates outstanding accuracy and robustness, highlighting its potential as a reliable solution for enhancing IoT security in the era of 5G networks.

Machine Learning-Driven Detection of Cross-Site Scripting Attacks

The ever-growing web application landscape, fueled by technological advancements, introduces new vulnerabilities to cyberattacks. Cross-site scripting (XSS) attacks pose a significant threat, exploiting the difficulty of distinguishing between benign and malicious scripts within web applications. Traditional detection methods struggle with high false-positive (FP) and false-negative (FN) rates. This research proposes a novel machine learning (ML)-based approach for robust XSS attack detection. We evaluate various models including Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVMs), Decision Trees (DTs), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and ensemble learning. The models are trained on a real-world dataset categorized into benign and malicious traffic, incorporating feature selection methods like Information Gain (IG) and Analysis of Variance (ANOVA) for optimal performance. Our findings reveal exceptional accuracy, with the RF model achieving 99.78% and ensemble models exceeding 99.64%. These results surpass existing methods, demonstrating the effectiveness of the proposed approach in securing web applications while minimizing FPs and FNs. This research offers a significant contribution to the field of web application security by providing a highly accurate and robust ML-based solution for XSS attack detection.

Development of Mathematical Algorithm for Detecting XSS Attacks on Web Applications

The widespread usage of web applications has led to an increase in security threats, with Cross-Site Scripting (XSS) attacks being one of the most prevalent and damaging. Detecting and mitigating XSS attacks is crucial to ensure the integrity and confidentiality of sensitive user data. This article presents the mathematical algorithm and a way to identify XSS attacks using a bounded function from below, which depends on the input string, and highlights its potential impact in bolstering web application security. To construct this function, we used special characters and keywords that are frequently found in the construction of XSS attacks.

DoubleR: Effective XSS attacking reality detection

Cross-site scripting (XSS) attack has been one of the most dangerous attacks in cyberspace security. Traditional methods essentially discover XSS attack by detecting malicious payloads in requests, which is unable to distinguish blind&random scanning with the attacking reality. Moreover, it also brings tens of thousands of worthless security alerts to administrators, as well as unfriendly experience to users. In this paper, we propose DoubleR, a bi-directional framework which detects both Requests and Responses to discover XSS attacking reality. On the basis of conventional detection of malicious requests, DoubleR collects responses from web server and trains a bagging based PU learning model to determine whether the vulnerability is truly triggered. To validate our proposed framework, experiments are performed on 5 popular Web applications with 11 specified CVE recorded vulnerabilities. Results show that DoubleR effectively distinguishes attacking reality from attacking attempts, reduce the worthless security alarms, and at the same time works well on other web attacks of the same type.

Swift Detection of XSS Attacks: Enhancing XSS Attack Detection by Leveraging Hybrid Semantic Embeddings and AI Techniques

AbstractCross-Site Scripting (XSS) attacks continue to be a significant threat to web application security, necessitating robust detection mechanisms to safeguard user data and ensure system integrity. In this study, we present a novel approach for detecting XSS attacks that harnesses the combined capabilities of the Universal Sentence Encoder (USE) and Word2Vec embeddings as a feature extractor, aiming to enhance the performance of machine learning and deep learning techniques. By leveraging the semantic understanding of sentences offered by USE and the word-level representations from Word2Vec, we obtain a comprehensive feature representation for XSS attack payloads. Our proposed approach aims to capture both fine-grained word meanings and broader sentence contexts, leading to enhanced feature extraction and improved model performance. We conducted extensive experiments utilizing machine learning and deep learning architectures to evaluate the effectiveness of our approach. The obtained results demonstrate that our combined embeddings approach outperforms traditional methods, achieving superior accuracy, precision, recall, ROC, and F1-score in detecting XSS attacks. This study not only advances XSS attack detection but also highlights the potential of state-of-the-art natural language processing techniques in web security applications. Our findings offer valuable insights for the development of more robust and effective security measures against XSS attacks.

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

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

A PU‐learning based approach for cross‐site scripting attacking reality detection

AbstractCross‐site scripting (XSS) attack has been one of the most dangerous attacks in cyberspace security. Traditional methods essentially discover XSS attack by detecting malicious payloads in requests, which is unable to distinguish attacking attempts with the attacking reality. The authors collect responses from a web server and train a bagging‐based PU learning model to determine whether the XSS vulnerability is truly triggered. To validate the authors’ proposed framework, experiments are performed on 5 popular web applications with 11 specified CVE recorded vulnerabilities and 32 vulnerable inputs. Results show that the authors’ approach outperforms existing research studies, effectively identifies the attacking reality from attacking attempts, and meanwhile reduces the number of worthless security alarms.

Review on Detection and Prevention Techniques of Scripting Attacks: Gaps, Challenges and Suggestions

Abstract: Web apps hold important information, such as login tokens and individual data, and cybercriminals repeatedly target attackers. Cross-site scripting is one of the most frequent vulnerabilities in web apps. Several techniques and patents are used to mitigate these vulnerabilities. Several 100 articles from a review of research papers published between 2005 and 2023 were considered. This paper reviewed different techniques and tools to detect cross-site scripting attacks, and it will be helpful to understand, analyze, and develop a strategy to deal with them. This paper focuses on different methods and tools for identifying cross-site scripting (XSS) attacks. Also, it depicts the strengths and shortcomings of the existing proposed method. Additionally, it will help to understand existing open issues or challenges faced by previous researchers.

Few-shot graph classification on cross-site scripting attacks detection

Cross-site scripting (XSS) payloads are an important part of XSS attacks, which contain malicious code and are injected into Web pages. There have been many research results based on machine learning and deep learning for the detection of XSS attacks. However, the current widely used datasets suffer from a serious data imbalance in the field of XSS attack detection, with a very limited number of samples for most XSS payload categories. Unfortunately, this problem has been overlooked in existing research. Although existing methods generally show high detection performance, they have been experimentally proven to have poor detection and generalization performance for some XSS payload categories. However, since such samples have a sparse number, their classification errors do not significantly affect the overall performance. To solve this problem, a few-shot graph classification method FSXSS applicable to XSS attacks detection is proposed. FSXSS reduces obfuscated malicious code by anti-obfuscation means such as circular decoding and code stitching, and then transforms the sample data into homomorphic graphs using contextual relationships and external word embeddings. These homomorphic graphs are used as input to obtain a vector representation of the graph through graph representation learning, and then the classifier classifies the samples by computing the similarity between them and the prototype. In addition, since there is no publicly available dataset for the few-shot XSS attack detection problem, we processed and labeled the data from the XSSED project to create the dataset FSXSSED. Experiments proved that FSXSS has excellent capabilities for the few-shot XSS attack detection problem.

Securing web applications against XSS and SQLi attacks using a novel deep learning approach

Modern web application development involves handling enormous amounts of sensitive and consequential data. Security is, therefore, a crucial component of developing web applications. A web application's security is concerned with safeguarding the data it processes. The web application framework must have safeguards to stop and find application vulnerabilities. Among all web application attacks, SQL injection and XSS attacks are common, which may lead to severe damage to Web application data or web functionalities. Currently, there are many solutions provided by various study for SQLi and XSS attack detection, but most of the work shown have used either SQL/XSS payload-based detection or HTTP request-based detection. Few solutions available can detect SQLi and XSS attacks, but these methods provide very high false positive rates, and the accuracy of these models can further be improved. We proposed a novel approach for securing web applications from both cross-site scripting attacks and SQL injection attacks using decoding and standardization of SQL and XSS payloads and HTTP requests and trained our model using hybrid deep learning networks in this paper. The proposed hybrid DL model combines the strengths of CNNs in extracting features from input data and LSTMs in capturing temporal dependencies in sequential data. The soundness of our approach lies in the use of deep learning techniques that can identify subtle patterns in the data that traditional machine learning-based methods might miss. We have created a testbed dataset of Normal and SQLi/XSS HTTP requests and evaluated the performance of our model on this dataset. We have also trained and evaluated the proposed model on the Benchmark dataset HTTP CSIC 2010 and another SQL/XSS payload dataset. The experimental findings show that our proposed approach effectively identifies these attacks with high accuracy and a low percentage of false positives. Additionally, our model performed better than traditional machine learning-based methods. This soundness approach can be applied to various network security applications such as intrusion detection systems and web application firewalls. Using our model, we achieved an accuracy of 99.84%, 99.23% and 99.77% on the SQL-XSS Payload dataset, Testbed dataset and HTTP CSIC 2010 dataset, respectively.

Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting (XSS) Attack Detection

Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting (XSS) Attack Detection

Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis

Models for text-to-image synthesis, such as DALL-E 2 and Stable Diffusion, have recently drawn a lot of interest from academia and the general public. These models are capable of producing high-quality images that depict a variety of concepts and styles when conditioned on textual descriptions. However, these models adopt cultural characteristics associated with specific Unicode scripts from their vast amount of training data, which may not be immediately apparent. We show that by simply inserting single non-Latin characters in the textual description, common models reflect cultural biases in their generated images. We analyze this behavior both qualitatively and quantitatively and identify a model’s text encoder as the root cause of the phenomenon. Such behavior can be interpreted as a model feature, offering users a simple way to customize the image generation and reflect their own cultural background. Yet, malicious users or service providers may also try to intentionally bias the image generation. One goal might be to create racist stereotypes by replacing Latin characters with similarly-looking characters from non-Latin scripts, so-called homoglyphs. To mitigate such unnoticed script attacks, we propose a novel homoglyph unlearning method to fine-tune a text encoder, making it robust against homoglyph manipulations.

A multilayer stacking classifier based on nature-inspired optimization for detecting cross-site scripting attack

A multilayer stacking classifier based on nature-inspired optimization for detecting cross-site scripting attack

An LSTM based cross-site scripting attack detection scheme for Cloud Computing environments

Cloud Computing plays a pivotal role in facilitating the Internet of Things (IoT) and its diverse applications. Users frequently access and store data on remote servers in Cloud Computing environments through web browsers. Consequently, attackers may exploit vulnerabilities in web browsing to embed malicious code into web pages, enabling them to launch attacks on remote servers in Cloud Computing environments. Due to its complexity, prevalence, and significant impact, XSS has consistently been recognized as one of the top ten web security vulnerabilities by OWASP. The existing XSS detection technology requires optimization: manual feature extraction is time-consuming and heavily reliant on domain knowledge, while the current confusion technology and complex code logic contribute to a decline in the identification of XSS attacks. This paper proposes a character-level bidirectional long-term and short-term memory network model based on a multi-attention mechanism. The bidirectional long-term and short-term memory network ensures the association of current features with preceding and subsequent text, while the multi-attention mechanism extracts additional features from different feature subspaces to enhance the understanding of text semantics. Experimental results demonstrate the effectiveness of the proposed model for XSS detection, with an F1 score of 98.71%.

Pengembangan Engine Web Crawler Sebagai Pencari Jejak Serangan Cyber Stored Cross-Site Scripting

Cross-site Scripting (XSS) vulnerability has long been a concern in web application security and is included in the OWASP Top 10 list. In 2017, XSS ranked 6th, but in 2021, it rose to the 4th position in the Injection category. This vulnerability exploits poorly validated input forms. This study aims to identify web pages that are vulnerable to Stored Cross-site Scripting attacks. The research is conducted by performing a search at three levels of depth. Web scraping is used to extract data from web pages, and the source code of the web pages is compared to Stored Cross-site Scripting attack patterns using the Knuth-Morris-Pratt algorithm. The results of the study indicate that some web pages exhibit detected attack patterns and traces of attacks, while others only show attack patterns without visible traces of attacks. Based on manual analysis of 56 randomly selected data from the research, it was found that 5 web pages had true positive values, indicating the presence of attack patterns and traces of attacks. Meanwhile, 49 other web pages had true negative values, where attack patterns were detected but no traces of attacks were found. This research provides insights into web pages vulnerable to Stored Cross-site Scripting attacks. The findings can be used to enhance web application security and reduce the potential for Cross-site Scripting attacks in the future.

Cost-effective detection system of cross-site scripting attacks using hybrid learning approach

Cross-Site Scripting (XSS) attacks inject malicious code payloads into web application logs, triggering stored cross-site scripting execution when accessing the view-logs interface. The destruction produced by the XSS injection susceptibilities is especially significant since the attacker can steal sensitive data such as the stored user's cookies and tokens or control the host remotely by using remote code execution of XSS. For example, if an attacker manages to obtain the cookies of the website administrator, the whole website can be taken over. In this paper, we develop and evaluate the performance of a machine-learning-based XSS detection system for website applications. Particularly, we investigate using three supervised machine learning: optimizable k-nearest neighbours, optimizable naïve bays, and hybrid (ensemble) learning of decision trees. To validate the system's efficacy, we employed the XSS-Attacks-2019 dataset consisting of modern real-world traffic-subjected types of classes normal (benign) or anomaly (XSS attack). To verify the performance evaluation, we have used several conventional metrics, including the confusion matrix analysis, the detection accuracy, the detection precision, the detection sensitivity, the harmonic detection means, and the detection time. The experimental results demonstrated the predominance of the hybrid learning-based XSS detection system. The best performance indicators peaked at 99.8% (accuracy, precision, and sensitivity) with a very short detection time of 103.1 μSec. Conclusively, the proposed hybrid model outpaced several recent XSS-attacks detection systems in the same study area.

Do Not Trust the Clouds Easily: The Insecurity of Content Security Policy Based on Object Storage

Content Security Policy (CSP) is a World Wide Web Consortium (W3C) standard, designed to prevent and mitigate security vulnerabilities such as Cross-Site Scripting attacks (XSS), data injection attacks and clickjacking attacks on websites. In this paper, we present a newly discovered front-end web attack that uses the current object storage services vulnerability of cloud vendors to bypass CSP. We selected the object storage services from two cloud vendors with the most users, i.e, Google and Amazon, to conduct systematic and large-scale research and analysis. Three cyberspace search engines are used to retrieve data, from which we analyze the consequence and damage range of this security breach. We focus on reporting four key-aspects of this security breach: 1.how to use object storage services to bypass CSP; 2.analysis on the existences of such vulnerability in real-world websites; 3.analysis on the existing security vulnerabilities in current object storage services; 4.the new strategy on object storage services that we propose to use to eliminate the discovered security threat.