arXiv subjects

Muhammad Ikram

Publications and source records attributed to Muhammad Ikram.

46 records · Page 3Linked to original sources

arXiv2020

Security and Privacy in IoT Using Machine Learning and Blockchain: Threats & Countermeasures

Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.

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arXiv2019

DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modelling

With the number of new mobile malware instances increasing by over 50\% annually since 2012 [24], malware embedding in mobile apps is arguably one of the most serious security issues mobile platforms are exposed to. While obfuscation techniques are successfully used to protect the intellectual property of apps' developers, they are unfortunately also often used by cybercriminals to hide malicious content inside mobile apps and to deceive malware detection tools. As a consequence, most of mobile malware detection approaches fail in differentiating between benign and obfuscated malicious apps. We examine the graph features of mobile apps code by building weighted directed graphs of the API calls, and verify that malicious apps often share structural similarities that can be used to differentiate them from benign apps, even under a heavily "polluted" training set where a large majority of the apps are obfuscated. We present DaDiDroid an Android malware app detection tool that leverages features of the weighted directed graphs of API calls to detect the presence of malware code in (obfuscated) Android apps. We show that DaDiDroid significantly outperforms MaMaDroid [23], a recently proposed malware detection tool that has been proven very efficient in detecting malware in a clean non-obfuscated environment. We evaluate DaDiDroid's accuracy and robustness against several evasion techniques using various datasets for a total of 43,262 benign and 20,431 malware apps. We show that DaDiDroid correctly labels up to 96% of Android malware samples, while achieving an 91% accuracy with an exclusive use of a training set of obfuscated apps.

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arXiv2019

A Longitudinal Analysis of Online Ad-Blocking Blacklists

Websites employ third-party ads and tracking services leveraging cookies and JavaScript code, to deliver ads and track users' behavior, causing privacy concerns. To limit online tracking and block advertisements, several ad-blocking (black) lists have been curated consisting of URLs and domains of well-known ads and tracking services. Using Internet Archive's Wayback Machine in this paper, we collect a retrospective view of the Web to analyze the evolution of ads and tracking services and evaluate the effectiveness of ad-blocking blacklists. We propose metrics to capture the efficacy of ad-blocking blacklists to investigate whether these blacklists have been reactive or proactive in tackling the online ad and tracking services. We introduce a stability metric to measure the temporal changes in ads and tracking domains blocked by ad-blocking blacklists, and a diversity metric to measure the ratio of new ads and tracking domains detected. We observe that ads and tracking domains in websites change over time, and among the ad-blocking blacklists that we investigated, our analysis reveals that some blacklists were more informed with the existence of ads and tracking domains, but their rate of change was slower than other blacklists. Our analysis also shows that Alexa top 5K websites in the US, Canada, and the UK have the most number of ads and tracking domains per website, and have the highest proactive scores. This suggests that ad-blocking blacklists are updated by prioritizing ads and tracking domains reported in the popular websites from these countries.

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arXiv2019

A Decade of Mal-Activity Reporting: A Retrospective Analysis of Internet Malicious Activity Blacklists

This paper focuses on reporting of Internet malicious activity (or mal-activity in short) by public blacklists with the objective of providing a systematic characterization of what has been reported over the years, and more importantly, the evolution of reported activities. Using an initial seed of 22 blacklists, covering the period from January 2007 to June 2017, we collect more than 51 million mal-activity reports involving 662K unique IP addresses worldwide. Leveraging the Wayback Machine, antivirus (AV) tool reports and several additional public datasets (e.g., BGP Route Views and Internet registries) we enrich the data with historical meta-information including geo-locations (countries), autonomous system (AS) numbers and types of mal-activity. Furthermore, we use the initially labelled dataset of approx 1.57 million mal-activities (obtained from public blacklists) to train a machine learning classifier to classify the remaining unlabeled dataset of approx 44 million mal-activities obtained through additional sources. We make our unique collected dataset (and scripts used) publicly available for further research. The main contributions of the paper are a novel means of report collection, with a machine learning approach to classify reported activities, characterization of the dataset and, most importantly, temporal analysis of mal-activity reporting behavior. Inspired by P2P behavior modeling, our analysis shows that some classes of mal-activities (e.g., phishing) and a small number of mal-activity sources are persistent, suggesting that either blacklist-based prevention systems are ineffective or have unreasonably long update periods. Our analysis also indicates that resources can be better utilized by focusing on heavy mal-activity contributors, which constitute the bulk of mal-activities.

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arXiv2019

The Chain of Implicit Trust: An Analysis of the Web Third-party Resources Loading

The Web is a tangled mass of interconnected services, where websites import a range of external resources from various third-party domains. However, the latter can further load resources hosted on other domains. For each website, this creates a dependency chain underpinned by a form of implicit trust between the first-party and transitively connected third-parties. The chain can only be loosely controlled as first-party websites often have little, if any, visibility of where these resources are loaded from. This paper performs a large-scale study of dependency chains in the Web, to find that around 50% of first-party websites render content that they did not directly load. Although the majority (84.91%) of websites have short dependency chains (below 3 levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third-parties are classified as suspicious --- although seemingly small, this limited set of suspicious third-parties have remarkable reach into the wider ecosystem. By running sandboxed experiments, we observe a range of activities with the majority of suspicious JavaScript downloading malware; worryingly, we find this propensity is greater among implicitly trusted JavaScripts.

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arXiv2017

A First Look at Ad Blocking Apps on Google Play

Online advertisers and analytics services (or trackers), are constantly tracking users activities as they access web services either through browsers or a mobile apps. Numerous tools such as browser plugins and specialized mobile apps have been proposed to limit intrusive advertisements and prevent tracking on desktop computing and mobile phones. For desktop computing, browser plugins are heavily studied for their usability and efficiency issues, however, tools that block ads and prevent trackers in mobile platforms, have received the least or no attention. In this paper, we present a first look at 97 Android adblocking apps (or adblockers), extracted from more than 1.5 million apps from Google Play, that promise to block advertisements and analytics services. With our data collection and analysis pipeline of the Android adblockers, we reveal the presences of third-party tracking libraries and sensitive permissions for critical resources on user mobile devices as well as have malware in the source codes. We analyze users' reviews for the in-effectiveness of adblockers in terms of not blocking ads and trackers. We found that a significant fraction of adblockers are not fulfilling their advertised functionality.

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arXiv2017

Measuring, Characterizing, and Detecting Facebook Like Farms

Social networks offer convenient ways to seamlessly reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes, aka like farms, has emerged alongside Facebook's official targeted advertising platform. Nonetheless, there is little work that systematically analyzes Facebook pages' promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics, and find that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms which spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake social activity. We analyze a range of features, grouped into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves precision higher than 99% and 93% recall.

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arXiv2017

Characterizing Key Stakeholders in an Online Black-Hat Marketplace

Over the past few years, many black-hat marketplaces have emerged that facilitate access to reputation manipulation services such as fake Facebook likes, fraudulent search engine optimization (SEO), or bogus Amazon reviews. In order to deploy effective technical and legal countermeasures, it is important to understand how these black-hat marketplaces operate, shedding light on the services they offer, who is selling, who is buying, what are they buying, who is more successful, why are they successful, etc. Toward this goal, in this paper, we present a detailed micro-economic analysis of a popular online black-hat marketplace, namely, SEOClerks.com. As the site provides non-anonymized transaction information, we set to analyze selling and buying behavior of individual users, propose a strategy to identify key users, and study their tactics as compared to other (non-key) users. We find that key users: (1) are mostly located in Asian countries, (2) are focused more on selling black-hat SEO services, (3) tend to list more lower priced services, and (4) sometimes buy services from other sellers and then sell at higher prices. Finally, we discuss the implications of our analysis with respect to devising effective economic and legal intervention strategies against marketplace operators and key users.

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arXiv2016

Combating Fraud in Online Social Networks: Detecting Stealthy Facebook Like Farms

As businesses increasingly rely on social networking sites to engage with their customers, it is crucial to understand and counter reputation manipulation activities, including fraudulently boosting the number of Facebook page likes using like farms. To this end, several fraud detection algorithms have been proposed and some deployed by Facebook that use graph co-clustering to distinguish between genuine likes and those generated by farm-controlled profiles. However, as we show in this paper, these tools do not work well with stealthy farms whose users spread likes over longer timespans and like popular pages, aiming to mimic regular users. We present an empirical analysis of the graph-based detection tools used by Facebook and highlight their shortcomings against more sophisticated farms. Next, we focus on characterizing content generated by social networks accounts on their timelines, as an indicator of genuine versus fake social activity. We analyze a wide range of features extracted from timeline posts, which we group into two main classes: lexical and non-lexical. We postulate and verify that like farm accounts tend to often re-share content, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. We extract relevant lexical and non-lexical features and and use them to build a classifier to detect like farms accounts, achieving significantly higher accuracy, namely, at least 99% precision and 93% recall.

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arXiv2016

Towards Seamless Tracking-Free Web: Improved Detection of Trackers via One-class Learning

Numerous tools have been developed to aggressively block the execution of popular JavaScript programs (JS) in Web browsers. Such blocking also affects functionality of webpages and impairs user experience. As a consequence, many privacy preserving tools (PP-Tools) that have been developed to limit online tracking, often executed via JS, may suffer from poor performance and limited uptake. A mechanism that can isolate JS necessary for proper functioning of the website from tracking JS would thus be useful. Through the use of a manually labelled dataset composed of 2,612 JS, we show how current PP-Tools are ineffective in finding the right balance between blocking tracking JS and allowing functional JS. To the best of our knowledge, this is the first study to assess the performance of current web PP-Tools. To improve this balance, we examine the two classes of JS and hypothesize that tracking JS share structural similarities that can be used to differentiate them from functional JS. The rationale of our approach is that web developers often borrow and customize existing pieces of code in order to embed tracking (resp. functional) JS into their webpages. We then propose one-class machine learning classifiers using syntactic and semantic features extracted from JS. When trained only on samples of tracking JS, our classifiers achieve an accuracy of 99%, where the best of the PP-Tools achieved an accuracy of 78%. We further test our classifiers and several popular PP-Tools on a corpus of 4K websites with 135K JS. The output of our best classifier on this data is between 20 to 64% different from the PP-Tools. We manually analyse a sample of the JS for which our classifier is in disagreement with all other PP-Tools, and show that our approach is not only able to enhance user web experience by correctly classifying more functional JS, but also discovers previously unknown tracking services.

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