Machine Learning Based Malicious URL Detection (original) (raw)

Malicious URL Detection based on Machine Learning

International Journal of Scientific Research in Science, Engineering and Technology, 2023

Currently, the risk of network information in security is increasing rapidly in number and level of danger. The methods mostly used by hackers to day are to attack end to end technology and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs). As a results, malicious URL detection is of great interest now a days. There have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques. In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behavior sand attributes. Moreover, bigdata technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviors. In short, the proposed detection system consists of a new set of URLs features and behaviors, a machine learning algorithm, and a big data technology. The experimental results show that the proposed URL attributes and behavior can help improve the ability to detect malicious URL significantly. This is suggested the proposed system may be considered as an optimized and friendly used solution for malicious URL detection.

Malicious URL's Detection using Machine Learning

Internet underpins a wide range of crimes, for example, spreading of Malwares and Misrepresentation of data usage. In spite of the fact that the exact inspirations driving these plans may vary, the shared factor lies in the way that clueless clients visit their locales. These visits can be driven by email, web list items or connections from other website pages. In all cases, in any case, the client is required to make some move, for example, tapping on an ideal Uniform Resource Locator (URL). In this paper, we address the identification of pernicious URL's using various machine learning algorithms specifically Support Vector Machines, Decision Trees, Random Forest and k-Nearest Neighbours and logistic regression. Besides, we embraced an open dataset including various URLs (examples) and their corresponding labels. Specifically, Random Forest and Support Vector Machines achieve the most astounding precision. The phishing issue is tremendous and there does not exist just a single answer to limit all vulnerabilities viably, hence the systems are actualized and implemented.

Detecting Malicious URL using Machine Learning: A Survey

international journal for research in applied science and engineering technology ijraset, 2020

Malicious Uniform Resource Locator/malicious websites are a high threat to cyber security. Malicious URLs host uninvited content like malware, spam, drive by downloads phishing, etc. Users become victims of scams like financial loss, thieving of personal data, malware installation, and causes losses of millions of dollars every year. There is a need to detect those threats in a very efficient and timely manner. Several studies have examined different techniques to handle the problem; the foremost used approach remains blacklisting. The most obstacle to using blacklist is that the difficulties in maintaining an up-to-date list of URLs. So here we proposed the Machine learning approach to detect the malicious URLs. We also discussed various methods for malicious URL detection, feature representations, and finally discussed various algorithms for the classification and feature extraction.

Review Paper on Detection of Malicious URLs Using Machine Learning Techniques

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Malicious websites are most serious threats over the Web. Ever since the inception of the internet, there has been a rise in malicious content over the web such has terrorism, financial fraud, phishing and hacking that targets user's personal information. Till today the various systems have been invent for the detection of a malicious website based on keywords and data content of the websites. This existing method have some drawbacks results into numbers of victims to increase. Hence we developed a system which helps the user to identify whether the website is malicious or not. Our system identifies whether the site is malicious or not through URL. The proposed system is fast and more accurate compared to current system. The classifier is trained with datasets of 1000 malicious sites and 1000 legitimate site URLs. Trained classifier is used for detection of malicious URLs.

A Review on Malicious URL Detection using Machine Learning Systems

– Malicious web sites pretendsignificant danger to desktop security and privacy.These links become instrumental in giving partial or full system control to the attackers. This results in victim systems, which get easily infected and, attackers can utilize systems for various cyber-crimes such as stealing credentials, spamming, phishing, denial-of-service and many more such attack. Detection of such website is difficult because of thephishing campaigns and the efforts to avoid blacklists.To look for malicious URLs, the first step is usually to gather URLs that are liveon the Internet. There are various stages to detect this URLs such as collection of dataset, extracting feature using different feature extraction techniques and Classification of extracted feature. This paper focus on comparative analysis of malicious URL detection techniques.

Machine Learning for Malicious URL Detection

Advances in Intelligent Systems and Computing, 2020

In recent years, Web-based attacks have become one of the most common threats. Threat actors tend to use malicious URLs to intentionally deceive users and launch attacks. Several approaches such as blacklisting have been implemented to detect malicious URLs. These unreliable approaches were also accompanied with strenuous task of maintaining an up-to-date blacklist URL database. To detect malicious URLs, machine learning techniques have been explored in recent years. This method analyzes different features of a URL and trains a prediction model on an already existing dataset of both malicious and benign URLs. This paper proposes a MuD (Malicious URL Detection) model which utilizes three supervised machine learning classifiers-support vector machine, logistic regression and Naive Bayesto effectively and accurately detect malicious URLs. The preliminary results indicate that Naïve Bayes algorithm produced best results.

Implementation Paper on Detection of Malicious URLs Using Machine Learning Techniques

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Detecting and preventing the user from the malicious site attacks are significant tasks. A huge number of attacks have been observed in last few years. Malicious attack detection and prevention system plays an immense role against these attacks by protecting the system’s critical information.

A Study of Malicious URL Detection Using Machine Learning and Heuristic Approaches

Learning and Analytics in Intelligent Systems, 2019

Malicious URL is a typical and genuine threat to cybersecurity. A Malicious URL has an assortment of spontaneous content in the form of phishing, spam in order to launch attacks. Innocent users visit such web sites move toward becoming casualties of various sorts of scams, including monetary loss, theft of private information (identity, credit-cards, etc.). It is essential to identify and follow up on such dangers in an opportune way. In this paper we had studies different techniques for detecting malicious URL and discussing each and every technique their merits and demerits.

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

IEEE Access

In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers' inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user's system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis, we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.

Survey on Malicious URL Detection Techniques

o 6th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 778-781). IEEE. (2022), 2022

Crimes in the cyberspace are increasing day by day. Recent cyber threat defense reports states that 80.7% of the systems are compromised at least once in 2020. Cyber criminals taking the pandemic situation as an opportunity for the mass attack through malicious URL circulated by email or text messages in social media. Performing cyber-attacks through malicious URLs is the handy method for the cyber criminals. Protecting from such attacks requires proper awareness and solid defense system. Some of the common approaches followed by the cybercriminals to deceive the victims are 1. Phishing URLs which is very similar to the legitimate URLs. 2. Redirecting URLs 3. Using JavaScript, redirects to the phishing URL when user interacts with webpage 4. Social engineering etc. As soon as the novice internet users clicks on the malicious URL link, cyber criminals can easily steal personal information or install malware on their device to get additional access. Recently malicious URLs are generated algorithmically and uses URL shortening service to evade the existing security setup such as firewall and web filters. In literature, the researchers have proposed several ways to detect the malicious URLs but, new attack vectors that are introduced by the cyber criminals can easily bypass the security system. The purpose of this paper is to provide an overview of various malicious URL detection techniques which includes blacklist based, rules based, machine learning and deep learning-based techniques. Most importantly, the paper discusses the common features used by the detection system from webpages to classify the URL as malicious or benign and various performance metrics. This will encourage the new researchers to bring out the innovative solutions.