Relevant bibliographies by topics / Drive-by Download Detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Drive-by Download Detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Drive-by Download Detection"

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Aldwairi, Monther, Musaab Hasan, and Zayed Balbahaith. "Detection of Drive-by Download Attacks Using Machine Learning Approach." International Journal of Information Security and Privacy 11, no. 4 (October 2017): 16–28. http://dx.doi.org/10.4018/ijisp.2017100102.

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Drive-by download refers to attacks that automatically download malwares to user's computer without his knowledge or consent. This type of attack is accomplished by exploiting web browsers and plugins vulnerabilities. The damage may include data leakage leading to financial loss. Traditional antivirus and intrusion detection systems are not efficient against such attacks. Researchers proposed plenty of detection approaches mostly passive blacklisting. However, a few proposed dynamic classification techniques, which suffer from clear shortcomings. In this paper, we propose a novel approach to detect drive-by download infected web pages based on extracted features from their source code. We test 23 different machine learning classifiers using data set of 5435 webpages and based on the detection accuracy we selected the top five to build our detection model. The approach is expected to serve as a base for implementing and developing anti drive-by download programs. We develop a graphical user interface program to allow the end user to examine the URL before visiting the website. The Bagged Trees classifier exhibited the highest accuracy of 90.1% and reported 96.24% true positive and 26.07% false positive rate.
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De Santis, Alfredo, Giancarlo De Maio, and Umberto Ferraro Petrillo. "Using HTML5 to prevent detection of drive-by-download web malware." Security and Communication Networks 8, no. 7 (August 21, 2014): 1237–55. http://dx.doi.org/10.1002/sec.1077.

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Han, KyungHyun, and Seong Oun Hwang. "Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens." Applied Sciences 10, no. 17 (September 3, 2020): 6116. http://dx.doi.org/10.3390/app10176116.

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Attackers use a variety of techniques to insert redirection JavaScript that leads a user to a malicious webpage, where a drive-by-download attack is executed. In particular, the redirection JavaScript in the landing site is obfuscated to avoid detection systems. In this paper, we propose a lightweight detection system based on static analysis to classify the obfuscation type and to promptly detect the obfuscated redirection JavaScript. The proposed model detects the obfuscated redirection JavaScript by converting the JavaScript into an abstract syntax tree (AST). Then, the structure and token information are extracted. Specifically, we propose a lightweight AST to identify the obfuscation type and the revised term frequency-inverse document frequency to efficiently detect the malicious redirection JavaScript. This approach enables rapid identification of the obfuscated redirection JavaScript and proactive blocking of the webpages that are used in drive-by-download attacks.
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P., Tatwadarshi, J. W. Bakal, and Neha Jain. "A Brief Survey of Detection and Mitigation Techniques for Clickjacking and Drive-by Download Attacks." International Journal of Computer Applications 138, no. 2 (March 17, 2016): 44–48. http://dx.doi.org/10.5120/ijca2016908785.

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Song, Xuyan, Chen Chen, Baojiang Cui, and Junsong Fu. "Malicious JavaScript Detection Based on Bidirectional LSTM Model." Applied Sciences 10, no. 10 (May 16, 2020): 3440. http://dx.doi.org/10.3390/app10103440.

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JavaScript has been widely used on the Internet because of its powerful features, and almost all the websites use it to provide dynamic functions. However, these dynamic natures also carry potential risks. The authors of the malicious scripts started using JavaScript to launch various attacks, such as Cross-Site Scripting (XSS), Cross-site Request Forgery (CSRF), and drive-by download attack. Traditional malicious script detection relies on expert knowledge, but even for experts, this is an error-prone task. To solve this problem, many learning-based methods for malicious JavaScript detection are being explored. In this paper, we propose a novel deep learning-based method for malicious JavaScript detection. In order to extract semantic information from JavaScript programs, we construct the Program Dependency Graph (PDG) and generate semantic slices, which preserve rich semantic information and are easy to transform into vectors. Then, a malicious JavaScript detection model based on the Bidirectional Long Short-Term Memory (BLSTM) neural network is proposed. Experimental results show that, in comparison with the other five methods, our model achieved the best performance, with an accuracy of 97.71% and an F1-score of 98.29%.
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Bux, Khuda, Muhammad Yousaf, Akhtar Hussain Jalbani, and Komal Batool. "Detection of Malicious Servers for Preventing Client-Side Attacks." January 2021 40, no. 1 (January 1, 2021): 230–40. http://dx.doi.org/10.22581/muet1982.2101.20.

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The number of client-side attacks is increasing day-by-day. These attacks are launched by using various methods like phishing, drive-by downloads, click-frauds, social engineering, scareware, and ransomware. To get more advantage with less exertion and time, the attackers are focus on the clients, rather than servers which are more secured as compared to the clients. This makes clients as an easy target for the attackers on the Internet. A number of systems/tools have been created by the security community with various functions for detection of client-side attacks. The discovery of malicious servers that launch the client side attacks can be characterized in two types. First to detect malicious servers with passive detection which is often signature based. Second to detect the malicious servers with active detection often with dynamic malware analysis. Current systems or tools have more focus on identifying malicious servers rather than preventing the clients from those malicious servers. In this paper, we have proposed a solution for the detection and prevention of malicious servers that use the Bro Intrusion Detection System (IDS) and VirusTotal API 2.0. The detected malicious link is then blocked at the gateway.
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Rai, Ankush, and Jagadeesh Kannan R. "MICROTUBULE BASED NEURO-FUZZY NESTED FRAMEWORK FOR SECURITY OF CYBER PHYSICAL SYSTEM." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 230. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19646.

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Network and system security of cyber physical system is of vital significance in the present information correspondence environment. Hackers and network intruders can make numerous fruitful endeavors to bring crashing of the networks and web services by unapproved interruption. Computing systems connected to the Internet are stood up to with a plenty of security threats, running from exemplary computer worms to impart drive by downloads and bot networks. In the most recent years these threats have achieved another nature of automation and sophistication, rendering most defenses inadequate. Ordinary security measures that depend on the manual investigation of security incidents and attack advancement intrinsically neglect to give an assurance from these threats. As an outcome, computer systems regularly stay unprotected over longer time frames. This study presents a network intrusion detection based on machine learning as a perfect match for this issue, as learning strategies give the capacity to naturally dissect data and backing early detection of threats. The results from the study have created practical results so far and there is eminent wariness in the community about learning based defenses. Machine learning based Intrusion Detection and Network Security Systems are one of these solutions. It dissects and predicts the practices of clients, and after that these practices will be viewed as an attack or a typical conduct.
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"ELPA: Emulation-Based Linked Page Map Analysis for the Detection of Drive-by Download Attacks." Journal of Information Processing Systems, 2015. http://dx.doi.org/10.3745/jips.03.0045.

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Chonofsky, Mark, Saulo H. P. de Oliveira, Konrad Krawczyk, and Charlotte M. Deane. "The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing." Bioinformatics, November 6, 2019. http://dx.doi.org/10.1093/bioinformatics/btz816.

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Abstract Motivation Over the last few years, the field of protein structure prediction has been transformed by increasingly-accurate contact prediction software. These methods are based on the detection of coevolutionary relationships between residues from multiple sequence alignments. However, despite speculation, there is little evidence of a link between contact prediction and the physico-chemical interactions which drive amino-acid coevolution. Furthermore, existing protocols predict only a fraction of all protein contacts and it is not clear why some contacts are favoured over others. Using a dataset of 863 protein domains, we assessed the physico-chemical interactions of contacts predicted by CCMpred, MetaPSICOV, and DNCON2, as examples of direct coupling analysis, meta-prediction, and deep learning. Results We considered correctly-predicted contacts and compared their properties against the protein contacts that were not predicted. Predicted contacts tend to form more bonds than non-predicted contacts, which suggests these contacts may be more important than contacts that were not predicted. Comparing the contacts predicted by each method, we found that metaPSICOV and DNCON2 favour accuracy whereas CCMPred detects contacts with more bonds. This suggests that the push for higher accuracy may lead to a loss of physico-chemically important contacts. These results underscore the connection between protein physico-chemistry and the coevolutionary couplings that can be derived from multiple sequence alignments. This relationship is likely to be relevant to protein structure prediction and functional analysis of protein structure and may be key to understanding their utility for different problems in structural biology. Availability We use publicly-available databases. Our code is available for download at http://opig.stats.ox.ac.uk/. Supplementary information Supplementary information is available at Bioinformatics online.
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"Detection of Malicious Uniform Resource Locator." International Journal of Recent Technology and Engineering 8, no. 2 (July 30, 2019): 41–47. http://dx.doi.org/10.35940/ijrte.a1265.078219.

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With the growing use of internet across the world ,the threats posed by it are numerous. The information you get and share across the internet is accessible, can be tracked and modified. Malicious websites play a pivotal role in effecting your system. These websites reach users through emails, text messages, pop ups or devious advertisements. The outcome of these websites or Uniform Resource Locators (URLs) would often be a downloaded malware, spyware, ransomware and compromised accounts. A malicious website or URL requires action on the users side, however in the case of drive by only downloads, the website will attempt to install software on the computer without asking users permission first. We put forward a model to forecast a URL is malicious or benign, based on the application layer and network characteristics. Machine learning algorithms for classification are used to develop a classifier using the targeted dataset. The targeted dataset is divided into training and validation sets. These sets are used to train and validate the classifier model. The hyper parameters are tuned to refine the model and generate better results
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Dissertations / Theses on the topic "Drive-by Download Detection"

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Xu, Kui. "Anomaly Detection Through System and Program Behavior Modeling." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/51140.

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Various vulnerabilities in software applications become easy targets for attackers. The trend constantly being observed in the evolution of advanced modern exploits is their growing sophistication in stealthy attacks. Code-reuse attacks such as return-oriented programming allow intruders to execute mal-intended instruction sequences on a victim machine without injecting external code. Successful exploitation leads to hijacked applications or the download of malicious software (drive-by download attack), which usually happens without the notice or permission from users. In this dissertation, we address the problem of host-based system anomaly detection, specifically by predicting expected behaviors of programs and detecting run-time deviations and anomalies. We first introduce an approach for detecting the drive-by download attack, which is one of the major vectors for malware infection. Our tool enforces the dependencies between user actions and system events, such as file-system access and process execution. It can be used to provide real time protection of a personal computer, as well as for diagnosing and evaluating untrusted websites for forensic purposes. We perform extensive experimental evaluation, including a user study with 21 participants, thousands of legitimate websites (for testing false alarms), 84 malicious websites in the wild, as well as lab reproduced exploits. Our solution demonstrates a usable host-based framework for controlling and enforcing the access of system resources. Secondly, we present a new anomaly-based detection technique that probabilistically models and learns a program's control flows for high-precision behavioral reasoning and monitoring. Existing solutions suffer from either incomplete behavioral modeling (for dynamic models) or overestimating the likelihood of call occurrences (for static models). We introduce a new probabilistic anomaly detection method for modeling program behaviors. Its uniqueness is the ability to quantify the static control flow in programs and to integrate the control flow information in probabilistic machine learning algorithms. The advantage of our technique is the significantly improved detection accuracy. We observed 11 up to 28-fold of improvement in detection accuracy compared to the state-of-the-art HMM-based anomaly models. We further integrate context information into our detection model, which achieves both strong flow-sensitivity and context-sensitivity. Our context-sensitive approach gives on average over 10 times of improvement for system call monitoring, and 3 orders of magnitude for library call monitoring, over existing regular HMM methods. Evaluated with a large amount of program traces and real-world exploits, our findings confirm that the probabilistic modeling of program dependences provides a significant source of behavior information for building high-precision models for real-time system monitoring. Abnormal traces (obtained through reproducing exploits and synthesized abnormal traces) can be well distinguished from normal traces by our model.
Ph. D.
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Nelms, Terry Lee. "Improving detection and annotation of malware downloads and infections through deep packet inspection." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54941.

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Malware continues to be one of the primary tools employed by attackers. It is used in attacks ranging from click fraud to nation state espionage. Malware infects hosts over the network through drive-by downloads and social engineering. These infected hosts communicate with remote command and control (C&C) servers to perform tasks and exfiltrate data. Malware's reliance on the network provides an opportunity for the detection and annotation of malicious communication. This thesis presents four main contributions. First, we design and implement a novel incident investigation system, named WebWitness. It automatically traces back and labels the sequence of events (e.g., visited web pages) preceding malware downloads to highlight how users reach attack pages on the web; providing a better understanding of current attack trends and aiding in the development of more effective defenses. Second, we conduct the first systematic study of modern web based social engineering malware download attacks. From this study we develop a categorization system for classifying social engineering downloads and use it to measure attack properties. From these measurements we show that it is possible to detect the majority of social engineering downloads using features from the download path. Third, we design and implement ExecScent, a novel system for mining new malware C&C domains from live networks. ExecScent automatically learns C&C traffic models that can adapt to the deployment network's traffic. This adaptive approach allows us to greatly reduce the false positives while maintaining a high number of true positives. Lastly, we develop a new packet scheduling algorithm for deep packet inspection that maximizes throughput by optimizing for cache affinity. By scheduling for cache affinity, we are able to deploy our systems on multi-gigabit networks.
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Šulák, Ladislav. "Detekce škodlivých webových stránek pomocí strojového učení." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385990.

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Táto práca sa zaoberá problematikou škodlivého kódu na webe so zameraním na analýzu a detekciu škodlivého JavaScriptu umiestneného na strane klienta s využitím strojového učenia. Navrhnutý prístup využíva známe i nové pozorovania s ohľadom na rozdiely medzi škodlivými a legitímnymi vzorkami. Tento prístup má potenciál detekovať nové exploity i zero-day útoky. Systém pre takúto detekciu bol implementovaný a využíva modely strojového učenia. Výkon modelov bol evaluovaný pomocou F1-skóre na základe niekoľkých experimentov. Použitie rozhodovacích stromov sa podľa experimentov ukázalo ako najefektívnejšia možnosť. Najefektívnejším modelom sa ukázal byť Adaboost klasifikátor s dosiahnutým F1-skóre až 99.16 %. Tento model pracoval s 200 inštanciami randomizovaného rozhodovacieho stromu založeného na algoritme Extra-Trees. Viacvrstvový perceptrón bol druhým najlepším modelom s dosiahnutým F1-skóre 97.94 %.
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Huang, Jhe-Jhun, and 黃哲諄. "Detecting Drive-by Download Based on Reputation System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/76445739504789070296.

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碩士
國立中山大學
資訊管理學系研究所
100
Drive-by download is a sort of network attack which uses different techniques to plant malicious codes in their computers. It makes the traditional intrusion detection systems and firewalls nonfunctional in the reason that those devices could not detect web-based threats. The Crawler-based approach has been proposed by many studies to discover drive-by download sites. However, the Crawler-based approach could not simulate the real user behavior of web browsing when drive-by download attack happens. Therefore, this study proposes a new approach to detect drive-by download by sniffing HTTP flow. This study uses reputation system to improve the efficiency of client honeypots, and adjusts client honeypots to process the raw data of HTTP flow. In the experiment conducted in real network environment, this study show the performance of a single client honeypot could reach average 560,000 HTTP success access log per day. Even in the peak traffic, this mechanism reduced the process time to 22 hours, and detected drive-by download sites that users were actually browsing. Reputation system in this study is applicable to varieties of domain names because it does not refer to online WHOIS database. It established classification model on machine learning in 12 features. The correct classification rate of the reputation system applied in this study is 90.9%. Compared with other Reputation System studies, this study not only extract features from DNS A-Type but also extract features from DNS NS-Type. The experiment results show the Error Rate of the new features from DNS NS-Type is only 19.03%.
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Chiang, Ming-Chung, and 江明駿. "A Hierarchical Classifier on Web Proxies for Detecting Drive-by Download Attacks." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/01860436264285021593.

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Book chapters on the topic "Drive-by Download Detection"

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Ghafir, Ibrahim, and Vaclav Prenosil. "Malicious File Hash Detection and Drive-by Download Attacks." In Advances in Intelligent Systems and Computing, 661–69. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2517-1_63.

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Nappa, Antonio, M. Zubair Rafique, and Juan Caballero. "Driving in the Cloud: An Analysis of Drive-by Download Operations and Abuse Reporting." In Detection of Intrusions and Malware, and Vulnerability Assessment, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39235-1_1.

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Vyawahare, Madhura, and Madhumita Chatterjee. "Survey on Detection and Prediction Techniques of Drive-by Download Attack in OSN." In Algorithms for Intelligent Systems, 453–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_42.

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Endicott-Popovsky, Barbara, Julia Narvaez, Christian Seifert, Deborah A. Frincke, Lori Ross O’Neil, and Chiraag Aval. "Use of Deception to Improve Client Honeypot Detection of Drive-by-Download Attacks." In Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience, 138–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02812-0_17.

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Poornachandran, Prabaharan, S. Praveen, Aravind Ashok, Manu R. Krishnan, and K. P. Soman. "Drive-by-Download Malware Detection in Hosts by Analyzing System Resource Utilization Using One Class Support Vector Machines." In Advances in Intelligent Systems and Computing, 129–37. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3156-4_13.

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Ibrahim, Saeed, Nawwaf Al Herami, Ebrahim Al Naqbi, and Monther Aldwairi. "Detection and Analysis of Drive-by Downloads and Malicious Websites." In Communications in Computer and Information Science, 72–86. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4825-3_6.

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Egele, Manuel, Peter Wurzinger, Christopher Kruegel, and Engin Kirda. "Defending Browsers against Drive-by Downloads: Mitigating Heap-Spraying Code Injection Attacks." In Detection of Intrusions and Malware, and Vulnerability Assessment, 88–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02918-9_6.

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Zhang, Haibo, Chaoshun Zuo, Shanqing Guo, Lizhen Cui, and Jun Chen. "SafeBrowsingCloud: Detecting Drive-by-Downloads Attack Using Cloud Computing Environment." In Lecture Notes in Computer Science, 292–303. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11167-4_29.

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Aldwairi, Monther, Musaab Hasan, and Zayed Balbahaith. "Detection of Drive-by Download Attacks Using Machine Learning Approach." In Cognitive Analytics, 1598–611. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch082.

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Drive-by download refers to attacks that automatically download malwares to user's computer without his knowledge or consent. This type of attack is accomplished by exploiting web browsers and plugins vulnerabilities. The damage may include data leakage leading to financial loss. Traditional antivirus and intrusion detection systems are not efficient against such attacks. Researchers proposed plenty of detection approaches mostly passive blacklisting. However, a few proposed dynamic classification techniques, which suffer from clear shortcomings. In this paper, we propose a novel approach to detect drive-by download infected web pages based on extracted features from their source code. We test 23 different machine learning classifiers using data set of 5435 webpages and based on the detection accuracy we selected the top five to build our detection model. The approach is expected to serve as a base for implementing and developing anti drive-by download programs. We develop a graphical user interface program to allow the end user to examine the URL before visiting the website. The Bagged Trees classifier exhibited the highest accuracy of 90.1% and reported 96.24% true positive and 26.07% false positive rate.
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Conference papers on the topic "Drive-by Download Detection"

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Kikuchi, Hiroaki, Hiroaki Matsumoto, and Hiroshi Ishii. "Automated Detection of Drive-By Download Attack." In 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). IEEE, 2015. http://dx.doi.org/10.1109/imis.2015.71.

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Cherukuri, Manoj, Srinivas Mukkamala, and Dongwan Shin. "Detection of Plugin Misuse Drive-By Download Attacks using Kernel Machines." In 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. ICST, 2014. http://dx.doi.org/10.4108/icst.collaboratecom.2014.257749.

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Cova, Marco, Christopher Kruegel, and Giovanni Vigna. "Detection and analysis of drive-by-download attacks and malicious JavaScript code." In the 19th international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1772690.1772720.

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Jodavi, Mehran, Mahdi Abadi, and Elham Parhizkar. "DbDHunter: An ensemble-based anomaly detection approach to detect drive-by download attacks." In 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, 2015. http://dx.doi.org/10.1109/iccke.2015.7365841.

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Takada, Tetsuji, and Katsuhiro Amako. "A Visual Approach to Detecting Drive-by Download Attacks." In VINCI '15: The 8th International Symposium on Visual Information Communication and Interaction. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2801040.2801070.

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Tyagi, Akshay, Laxmi Ahuja, Sunil Kumar Khatri, and Subhranil Som. "Prevention of Drive by Download Attack (URL Malware Detector)." In 2019 Third International Conference on Inventive Systems and Control (ICISC). IEEE, 2019. http://dx.doi.org/10.1109/icisc44355.2019.9036341.

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AL-Taharwa, Ismail Adel, Hahn-Ming Lee, Albert B. Jeng, Cheng-Seen Ho, Kuo-Ping Wu, and Shyi-Ming Chen. "Drive-by Disclosure: A Large-Scale Detector of Drive-by Downloads Based on Latent Behavior Prediction." In 2015 IEEE Trustcom/BigDataSE/ISPA. IEEE, 2015. http://dx.doi.org/10.1109/trustcom.2015.392.

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Matsunaka, Takashi, Junpei Urakawa, and Ayumu Kubota. "Detecting and Preventing Drive-By Download Attack via Participative Monitoring of the Web." In 2013 Eighth Asia Joint Conference on Information Security (ASIA JCIS). IEEE, 2013. http://dx.doi.org/10.1109/asiajcis.2013.15.

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Van Lam Le, Ian Welch, Xiaoying Gao, and Peter Komisarczuk. "Detecting heap-spray attacks in drive-by downloads: Giving attackers a hand." In 38th Annual IEEE Conference on Local Computer Networks (LCN 2013). IEEE, 2013. http://dx.doi.org/10.1109/lcn.2013.6761254.

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