Biblio

With the recent boom in the cryptocurrency market, hackers have been on the lookout to find novel ways of commandeering users' machine for covert and stealthy mining operations. In an attempt to expose such under-the-hood practices, this paper explores the issue of browser cryptojacking, whereby miners are secretly deployed inside browser code without the knowledge of the user. To this end, we analyze the top 50k websites from Alexa and find a noticeable percentage of sites that are indulging in this exploitative exercise often using heavily obfuscated code. Furthermore, mining prevention plug-ins, such as NoMiner, fail to flag such cleverly concealed instances. Hence, we propose a machine learning solution based on hardware-assisted profiling of browser code in real-time. A fine-grained micro-architectural footprint allows us to classify mining applications with \textbackslashtextgreater99% accuracy and even flags them if the mining code has been heavily obfuscated or encrypted. We build our own browser extension and show that it outperforms other plug-ins. The proposed design has negligible overhead on the user's machine and works for all standard off-the-shelf CPUs.

Cryptojacking is the permissionless use of a target device to covertly mine cryptocurrencies. With cryptojacking attackers use malicious JavaScript codes to force web browsers into solving proof-of-work puzzles, thus making money by exploiting resources of the website visitors. To understand and counter such attacks, we systematically analyze the static, dynamic, and economic aspects of in-browser cryptojacking. For static analysis, we perform content-, currency-, and code-based categorization of cryptojacking samples to 1) measure their distribution across websites, 2) highlight their platform affinities, and 3) study their code complexities. We apply unsupervised learning to distinguish cryptojacking scripts from benign and other malicious JavaScript samples with 96.4% accuracy. For dynamic analysis, we analyze the effect of cryptojacking on critical system resources, such as CPU and battery usage. Additionally, we perform web browser fingerprinting to analyze the information exchange between the victim node and the dropzone cryptojacking server. We also build an analytical model to empirically evaluate the feasibility of cryptojacking as an alternative to online advertisement. Our results show a large negative profit and loss gap, indicating that the model is economically impractical. Finally, by leveraging insights from our analyses, we build countermeasures for in-browser cryptojacking that improve upon the existing remedies.

Cybercrimes and cyber criminals widely use dark web and illegal functionalities of the dark web towards the world crisis. More than half of the criminal activities and the terror activities conducted through the dark web such as, cryptocurrency, selling human organs, red rooms, child pornography, arm deals, drug deals, hire assassins and hackers, hacking software and malware programs, etc. The law enforcement agencies such as FBI, NSA, Interpol, Mossad, FSB etc, are always conducting surveillance programs through the dark web to trace down the mass criminals and terrorists while stopping the crimes and the terror activities. This paper is about the dark web marketing and surveillance programs. In the deep end research will discuss the dark web access with securely and how the law enforcement agencies exponentially tracking down the users with terror behaviours and activities. Moreover, the paper discusses dark web sites which users can grab the dark web jihadist services and anonymous markets including safety precautions.

Website fingerprinting attack is a kind of traffic analysis attack that aims to identify the URL of visited websites using the Tor browser. Previous website fingerprinting attacks were based on batch learning methods which assumed that the traffic traces of each website are independent and generated from the stationary probability distribution. But, in realistic scenarios, the websites' concepts can change over time (dynamic websites) that is known as concept drift. To deal with data whose distribution change over time, the classifier model must update its model permanently and be adaptive to concept drift. Streaming algorithms are dynamic models that have these features and lead us to make a comparison of various representative data stream classification algorithms for website fingerprinting. Given to our experiments and results, by considering streaming algorithms along with statistical flow-based network traffic features, the accuracy grows significantly.

Millions of news are being exchanged daily among people. With the appearance of the Internet, the way of broadcasting news has changed and become faster, however it caused many problems. For instance, the increase in the speed of broadcasting news leads to an increase in the speed of fake news creation. Fake news can have a huge impression on societies. Additionally, the existence of a central entity, such as news agencies, could lead to fraud in the news broadcasting process, e.g. generating fake news and publishing them for their benefits. Since Blockchain technology provides a reliable decentralized network, it can be used to publish news. In addition, Blockchain with the help of decentralized applications and smart contracts can provide a platform in which fake news can be detected through public participation. In this paper, we proposed a new method for sharing and analyzing news to detect fake news using Blockchain, called SANUB. SANUB provides features such as publishing news anonymously, news evaluation, reporter validation, fake news detection and proof of news ownership. The results of our analysis show that SANUB outperformed the existing methods.

Content Delivery Networks(CDN) is a standout amongst the most encouraging innovations that upgrade performance for its clients' websites by diverting web demands from browsers to topographically dispersed CDN surrogate nodes. However, due to the variable nature of CDN, it suffers from various security and resource allocation issues. The most common attack which is used to bring down a whole network as well as CDN without even finding a loophole in the security is DDoS. In this proposal, we proposed a distributed virtual honeypot model for diminishing DDoS attacks and prevent intrusion in securing CDN. Honeypots are specially utilized to imitate the primary server with the goal that the attack is alleviated to the fake rather than the main server. Our proposed layer based model utilizes honeypot to be more effective reducing the cost of the system as well as maintaining the smooth delivery in geographically dispersed servers without performance degradation.

In the era of the ever-growing number of smart devices, fraudulent practices through Phishing Websites have become an increasingly severe threat to modern computers and internet security. These websites are designed to steal the personal information from the user and spread over the internet without the knowledge of the user using the system. These websites give a false impression of genuinity to the user by mirroring the real trusted web pages which then leads to the loss of important credentials of the user. So, Detection of such fraudulent websites is an essence and the need of the hour. In this paper, various classifiers have been considered and were found that ensemble classifiers predict to utmost efficiency. The idea behind was whether a combined classifier model performs better than a single classifier model leading to a better efficiency and accuracy. In this paper, for experimentation, three Meta Classifiers, namely, AdaBoostM1, Stacking, and Bagging have been taken into consideration for performance comparison. It is found that Meta Classifier built by combining of simple classifier(s) outperform the simple classifier's performance.

The use of information systems is a solutions to support the operations of the institution. In order to access information systems in accordance with their access rights, usually the user will enter a username and password as the authentication process. However, this has a weakness if the other side is cheating by sniffing or tapping user passwords. This makes the password unsafe to use for access information systems. If the username and password if it is stolen, abuse will occur for the crime or theft of the owner's identity accounts like name, email, telephone number, biological mother's name, account number and others. One solution is to apply two factor authentication method which is Time-Based One Time Password (TOTP) and Secure Algorithm Hash Algorithm 1 (SHA1). With this method, the system Authentication of a website or site does not only depend on the username and password to enter the account user but the user will get a token or code which is used to log in to the user's account. After testing hundred times, the authentication process who use Two Factor Authentication can tackle possible attacks on abuse o user access rights. Time Based Application One Time Password and Secure Hash Algorithm 1 Generate code that can't be the same because of the code it can only be used once with a time limit certain so it is difficult to guess. SHA1 with long input different strings will produce output with a fixed length string of 160 bits. Test results are obtained the results that 30 seconds is enough to prevent hackers log in and take over the account without permission and also prove that two-factor authentication can increase the security of the authentication process well. The time above is the result of testing the process user authentication until the hacker sniffing against tokens to try to take over the account.

Automatic extractive text summarization is the process of condensing textual information while preserving the important concepts. The proposed method after performing pre-processing on input Persian news articles generates a feature vector of salient sentences from a combination of statistical, semantic and heuristic methods and that are scored and concatenated accordingly. The scoring of the salient features is based on the article's title, proper nouns, pronouns, sentence length, keywords, topic words, sentence position, English words, and quotations. Experimental results on measurements including recall, F-measure, ROUGE-N are presented and compared to other Persian summarizers and shown to provide higher performance.

Most Web sites rely on resources hosted by third parties such as CDNs. Third parties may be compromised or coerced into misbehaving, e.g. delivering a malicious script or stylesheet. Unexpected changes to resources hosted by third parties can be detected with the Subresource Integrity (SRI) mechanism. The focus of SRI is on scripts and stylesheets. Web fonts cannot be secured with that mechanism under all circumstances. The first contribution of this paper is to evaluates the potential for attacks using malicious fonts. With an instrumented browser we find that (1) more than 95% of the top 50,000 Web sites of the Tranco top list rely on resources hosted by third parties and that (2) only a small fraction employs SRI. Moreover, we find that more than 60% of the sites in our sample use fonts hosted by third parties, most of which are being served by Google. The second contribution of the paper is a proof of concept of a malicious font as well as a tool for automatically generating such a font, which targets security-conscious users who are used to verifying cryptographic fingerprints. Software vendors publish such fingerprints along with their software packages to allow users to verify their integrity. Due to incomplete SRI support for Web fonts, a third party could force a browser to load our malicious font. The font targets a particular cryptographic fingerprint and renders it as a desired different fingerprint. This allows attackers to fool users into believing that they download a genuine software package although they are actually downloading a maliciously modified version. Finally, we propose countermeasures that could be deployed to protect the integrity of Web fonts.

Modern Browsers have become sophisticated applications, providing a portal to the web. Browsers host a complex mix of interpreters such as HTML and JavaScript, allowing not only useful functionality but also malicious activities, known as browser-hijacking. These attacks can be particularly difficult to detect, as they usually operate within the scope of normal browser behaviour. CryptoJacking is a form of browser-hijacking that has emerged as a result of the increased popularity and profitability of cryptocurrencies, and the introduction of new cryptocurrencies that promote CPU-based mining. This paper proposes MANiC (Multi-step AssessmeNt for Crypto-miners), a system to detect CryptoJacking websites. It uses regular expressions that are compiled in accordance with the API structure of different miner families. This allows the detection of crypto-mining scripts and the extraction of parameters that could be used to detect suspicious behaviour associated with CryptoJacking. When MANiC was used to analyse the Alexa top 1m websites, it detected 887 malicious URLs containing miners from 11 different families and demonstrated favourable results when compared to related CryptoJacking research. We demonstrate that MANiC can be used to provide insights into this new threat, to identify new potential features of interest and to establish a ground-truth dataset, assisting future research.

Certificate Transparency (CT) is an emerging system for enabling the rapid discovery of malicious or misissued certificates. Initially standardized in 2013, CT is now finally beginning to see widespread support. Although CT provides desirable security benefits, web browsers cannot begin requiring all websites to support CT at once, due to the risk of breaking large numbers of websites. We discuss challenges for deployment, analyze the adoption of CT on the web, and measure the error rates experienced by users of the Google Chrome web browser. We find that CT has so far been widely adopted with minimal breakage and warnings. Security researchers often struggle with the tradeoff between security and user frustration: rolling out new security requirements often causes breakage. We view CT as a case study for deploying ecosystem-wide change while trying to minimize end user impact. We discuss the design properties of CT that made its success possible, as well as draw lessons from its risks and pitfalls that could be avoided in future large-scale security deployments.

Phishing attacks have reached record volumes in recent years. Simultaneously, modern phishing websites are growing in sophistication by employing diverse cloaking techniques to avoid detection by security infrastructure. In this paper, we present PhishFarm: a scalable framework for methodically testing the resilience of anti-phishing entities and browser blacklists to attackers' evasion efforts. We use PhishFarm to deploy 2,380 live phishing sites (on new, unique, and previously-unseen .com domains) each using one of six different HTTP request filters based on real phishing kits. We reported subsets of these sites to 10 distinct anti-phishing entities and measured both the occurrence and timeliness of native blacklisting in major web browsers to gauge the effectiveness of protection ultimately extended to victim users and organizations. Our experiments revealed shortcomings in current infrastructure, which allows some phishing sites to go unnoticed by the security community while remaining accessible to victims. We found that simple cloaking techniques representative of real-world attacks- including those based on geolocation, device type, or JavaScript- were effective in reducing the likelihood of blacklisting by over 55% on average. We also discovered that blacklisting did not function as intended in popular mobile browsers (Chrome, Safari, and Firefox), which left users of these browsers particularly vulnerable to phishing attacks. Following disclosure of our findings, anti-phishing entities are now better able to detect and mitigate several cloaking techniques (including those that target mobile users), and blacklisting has also become more consistent between desktop and mobile platforms- but work remains to be done by anti-phishing entities to ensure users are adequately protected. Our PhishFarm framework is designed for continuous monitoring of the ecosystem and can be extended to test future state-of-the-art evasion techniques used by malicious websites.

The aim of this work is to create a new style web browser. The other web browsers can have safety issues and have many ads and popups. The other web browsers can fill up cache with the logging of big history of visited web pages. This app is a light-weight web browser which is both secure and private with no ads and no popups, just the plain Internet shown in full screen. The app does not store all user data, so the navigation of webpages is done in incognito mode. The app was made to open any new HTML5 web page in a secure and private mode with big focus on loading speed of the web pages.

The popularity of Web-based computing has given increase to browser-based cyberattacks. These cyberattacks use websites that exploit various web browser vulnerabilities. To help regular users avoid exploit websites and engage in safe online activities, we propose a methodology of building a machine learning-powered predictive analytical model that will measure the risk of attacks and privacy breaches associated with visiting different websites and performing online activities using web browsers. The model will learn risk levels from historical data and metadata scraped from web browsers.

Phishing is typically deployed as an attack vector in the initial stages of a hacking endeavour. Due to it low-risk rightreward nature it has seen a widespread adoption, and detecting it has become a challenge in recent times. This paper proposes a novel means of detecting phishing websites using a Generative Adversarial Network. Taking into account the internal structure and external metadata of a website, the proposed approach uses a generator network which generates both legitimate as well as synthetic phishing features to train a discriminator network. The latter then determines if the features are either normal or phishing websites, before improving its detection accuracy based on the classification error. The proposed approach is evaluated using two different phishing datasets and is found to achieve a detection accuracy of up to 94%.

As a cyber attack which leverages social engineering and other sophisticated techniques to steal sensitive information from users, phishing attack has been a critical threat to cyber security for a long time. Although researchers have proposed lots of countermeasures, phishing criminals figure out circumventions eventually since such countermeasures require substantial manual feature engineering and can not detect newly emerging phishing attacks well enough, which makes developing an efficient and effective phishing detection method an urgent need. In this work, we propose a novel phishing website detection approach by detecting the Uniform Resource Locator (URL) of a website, which is proved to be an effective and efficient detection approach. To be specific, our novel capsule-based neural network mainly includes several parallel branches wherein one convolutional layer extracts shallow features from URLs and the subsequent two capsule layers generate accurate feature representations of URLs from the shallow features and discriminate the legitimacy of URLs. The final output of our approach is obtained by averaging the outputs of all branches. Extensive experiments on a validated dataset collected from the Internet demonstrate that our approach can achieve competitive performance against other state-of-the-art detection methods while maintaining a tolerable time overhead.

Nowadays, phishing is one of the most usual web threats with regards to the significant growth of the World Wide Web in volume over time. Phishing attackers always use new (zero-day) and sophisticated techniques to deceive online customers. Hence, it is necessary that the anti-phishing system be real-time and fast and also leverages from an intelligent phishing detection solution. Here, we develop a reliable detection system which can adaptively match the changing environment and phishing websites. Our method is an online and feature-rich machine learning technique to discriminate the phishing and legitimate websites. Since the proposed approach extracts different types of discriminative features from URLs and webpages source code, it is an entirely client-side solution and does not require any service from the third-party. The experimental results highlight the robustness and competitiveness of our anti-phishing system to distinguish the phishing and legitimate websites.

In this work, we applied deep semantic analysis, and machine learning and deep learning techniques, to capture inherent characteristics of email text, and classify emails as phishing or non -phishing.

Phishing is the major problem of the internet era. In this era of internet the security of our data in web is gaining an increasing importance. Phishing is one of the most harmful ways to unknowingly access the credential information like username, password or account number from the users. Users are not aware of this type of attack and later they will also become a part of the phishing attacks. It may be the losses of financial found, personal information, reputation of brand name or trust of brand. So the detection of phishing site is necessary. In this paper we design a framework of phishing detection using URL.

Phishing is a criminal offense which involves theft of user's sensitive data. The phishing websites target individuals, organizations, the cloud storage hosting sites and government websites. Currently, hardware based approaches for anti-phishing is widely used but due to the cost and operational factors software based approaches are preferred. The existing phishing detection approaches fails to provide solution to problem like zero-day phishing website attacks. To overcome these issues and precisely detect phishing occurrence a three phase attack detection named as Web Crawler based Phishing Attack Detector(WC-PAD) has been proposed. It takes the web traffics, web content and Uniform Resource Locator(URL) as input features, based on these features classification of phishing and non phishing websites are done. The experimental analysis of the proposed WC-PAD is done with datasets collected from real phishing cases. From the experimental results, it is found that the proposed WC-PAD gives 98.9% accuracy in both phishing and zero-day phishing attack detection.

The Web ecosystem has been evolving over the past years and new Internet protocols, namely HTTP/2 over TLS/TCP and QUIC/UDP, are now used to deliver Web contents. Similarly, CDNs (Content Delivery Network) are deployed worldwide, caching contents close to end-users to optimize web browsing quality. We present in this paper an analysis of the influence of the Internet protocols and CDN on the Top 10,000 Alexa websites, based on a 12-month measurement campaign (from April 2018 to April 2019) performed via our tool Web View [1]. Part of our measurements are made public, represented on a monitoring website1, showing the results for the Top 50 Alexa Websites plus few specific websites and 8 french websites, suggested by the French Agency in charge of regulating telecommunications. Our analysis of this long-term measurement campaign allows to better analyze the delivery of public websites. For instance, it shows that even if some argue that QUIC optimizes the quality, it is not observed in the real-life since QUIC is not largely deployed. Our method for analyzing CDN delivery in the Web browsing allows us to evaluate its influence, which is important since their usage can decrease the web pages' loading time, on average 43.1% with HTTP/2 and 38.5% with QUIC, when requesting a second time the same home page.

In recent years, websites that incorporate user reviews, such as Amazon, IMDB and YELP, have become exceedingly popular. As an important factor affecting users purchasing behavior, review information has been becoming increasingly important, and accordingly, the reliability of review information becomes an important issue. This paper proposes a method to more accurately detect the appearance period of spam reviews and to identify the spam reviews by verifying the consistency of review information among multiple review sites. Evaluation experiments were conducted to show the accuracy of the detection results, and compared the newly proposed method with our previously proposed method.

Comment spam is one of the great challenges faced by forum administrators. Detecting and blocking comment spam can relieve the load on servers, improve user experience and purify the network conditions. This paper focuses on the detection of comment spam. The behaviors of spammer and the content of spam were analyzed. According to analysis results, two types of effective features are extracted which can make a better description of spammer characteristics. Additionally, a gradient boosting tree algorithm was used to construct the comment spam detector based on the extracted features. Our proposed method is examined on a blog spam dataset which was published by previous research, and the result illustrates that our method performs better than the previous method on detection accuracy. Moreover, the CPU time is recorded to demonstrate that the time spent on both training and testing maintains a small value.

Many websites induce the browser to send network traffic in response to user input events. This includes websites with autocomplete, a popular feature on search engines that anticipates the user's query while they are typing. Websites with this functionality require HTTP requests to be made as the query input field changes, such as when the user presses a key. The browser responds to input events by generating network traffic to retrieve the search predictions. The traffic emitted by the client can expose the timings of keyboard input events which may lead to a keylogging side channel attack whereby the query is revealed through packet inter-arrival times. We investigate the feasibility of such an attack on several popular search engines by characterizing the behavior of each website and measuring information leakage at the network level. Three out of the five search engines we measure preserve the mutual information between keystrokes and timings to within 1% of what it is on the host. We describe the ways in which two search engines mitigate this vulnerability with minimal effects on usability.