Danger is My Middle Name: Experimenting with SSL Vulnerabilities in Android Apps

This paper presents a measurement study of information leakage and SSL vulnerabilities in popular Android apps. We perform static and dynamic analysis on 100 apps, downloaded at least 10M times, that request full network access. Our experiments show that, although prior work has drawn a lot of attention to SSL implementations on mobile platforms, several popular apps (32/100) accept all certificates and all hostnames, and four actually transmit sensitive data unencrypted. We set up an experimental testbed simulating man-in-the-middle attacks and find that many apps (up to 91% when the adversary has a certificate installed on the victim's device) are vulnerable, allowing the attacker to access sensitive information, including credentials, files, personal details, and credit card numbers. Finally, we provide a few recommendations to app developers and highlight several open research problems.Comment: A preliminary version of this paper appears in the Proceedings of ACM WiSec 2015. This is the full versio

Measuring and Mitigating Security and Privacy Issues on Android Applications

Over time, the increasing popularity of the Android operating system (OS) has resulted in its user-base surging past 1 billion unique devices. As a result, cybercriminals and other non-criminal actors are attracted to the OS due to the amount of user information they can access. Aiming to investigate security and privacy issues on the Android ecosystem, previous work has shown that it is possible for malevolent actors to steal users' sensitive personal information over the network, via malicious applications, or vulnerability exploits etc., presenting proof of concepts or evidences of exploits. Due to the ever-changing nature of the Android ecosystem and the arms race involved in detecting and mitigating malicious applications, it is important to continuously examine the ecosystem for security and privacy issues. This thesis presents research contributions in this space, and it is divided into two parts. The first part focuses on measuring and mitigating vulnerabilities in applications due to poor implementation of security and privacy protocols. In particular, we investigate the implementation of the SSL/TLS protocol validation logic, and properties such as ephemerality, anonymity, and end-to-end encryption. We show that, despite increased awareness of vulnerabilities in SSL/TLS implementation by application developers, these vulnerabilities are still present in popular applications, allowing malicious actors steal users' information. To help developers mitigate them, we provide useful recommendations such as enabling SSL/TLS pinning and using the same certificate validation logic in their test and development environments. The second part of this thesis focuses on the detection of malicious applications that compromise users' security and privacy, the detection performance of the different program analysis approach, and the influence of different input generators during dynamic analysis on detection performance. We present a novel method for detecting malicious applications, which is less susceptible to the evolution of the Android ecosystem (i.e., changes in the Android framework as a result of the addition/removal of API calls in new releases) and malware (i.e., changes in techniques to evade detection) compared to previous methods. Overall, this thesis contributes to knowledge around Android apps with respect to, vulnerability discovery that leads to loss of users' security and privacy, and the design of robust Android malware detection tools. It highlights the need for continual evaluation of apps as the ecosystem changes to detect and prevent vulnerabilities and malware that results in a compromise of users' security and privacy

A family of droids -- Android malware detection via behavioral modeling: static vs dynamic analysis

Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach. To this end, we build on MaMaDroid, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AuntieDroid and instantiate it by using both automated (Monkey) and user-generated inputs. We find that combining both static and dynamic analysis yields the best performance, with F-measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and, finally, investigate the reasons for inconsistent misclassifications across methods.Accepted manuscrip

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.Comment: To appear in ACM Transactions on Privacy and Security (TOPS

A Family of Droids -- Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis

Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach. To this end, we build on MaMaDroid, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AuntieDroid and instantiate it by using both automated (Monkey) and user-generated inputs. We find that combining both static and dynamic analysis yields the best performance, with F-measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and, finally, investigate the reasons for inconsistent misclassifications across methods.Comment: A preliminary version of this paper appears in the Proceedings of 16th Annual Conference on Privacy, Security and Trust (PST 2018). This is the full versio

MaMaDroid: Detecting Android malware by building Markov chains of behavioral models (extended version)

As Android has become increasingly popular, so has malware targeting it, thus motivating the research community to propose different detection techniques. However, the constant evolution of the Android ecosystem, and of malware itself, makes it hard to design robust tools that can operate for long periods of time without the need for modifications or costly re-training. Aiming to address this issue, we set to detect malware from a behavioral point of view, modeled as the sequence of abstracted API calls. We introduce MaMaDroid, a static-analysis-based system that abstracts app's API calls to their class, package, or family, and builds a model from their sequences obtained from the call graph of an app as Markov chains. This ensures that the model is more resilient to API changes and the features set is of manageable size. We evaluate MaMaDroid using a dataset of 8.5K benign and 35.5K malicious apps collected over a period of 6 years, showing that it effectively detects malware (with up to 0.99 F-measure) and keeps its detection capabilities for long periods of time (up to 0.87 F-measure 2 years after training). We also show that MaMaDroid remarkably overperforms DroidAPIMiner, a state-of-the-art detection system that relies on the frequency of (raw) API calls. Aiming to assess whether MaMaDroid's effectiveness mainly stems from the API abstraction or from the sequencing modeling, we also evaluate a variant of it that uses frequency (instead of sequences), of abstracted API calls. We find that it is not as accurate, failing to capture maliciousness when trained on malware samples that include API calls that are equally or more frequently used by benign apps

BOTection: bot detection by building Markov Chain models of bots network behavior

This paper was presented at the 15th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2020), 5-9 October 2020, Taipei, Taiwan. This is the accepted manuscript version of the paper. The final version is available online from the Association for Computing Machinery at: https://doi.org/10.1145/3320269.3372202.Botnets continue to be a threat to organizations, thus various machine learning-based botnet detectors have been proposed. However, the capability of such systems in detecting new or unseen botnets is crucial to ensure its robustness against the rapid evolution of botnets. Moreover, it prolongs the effectiveness of the system in detecting bots, avoiding frequent and time-consuming classifier re-training. We present BOTection, a privacy-preserving bot detection system that models the bot network flow behavior as a Markov Chain. The Markov Chain state transitions capture the bots' network behavior using high-level flow features as states, producing content-agnostic and encryption resilient behavioral features. These features are used to train a classifier to first detect flows produced by bots, and then identify their bot families. We evaluate our system on a dataset of over 7M malicious flows from 12 botnet families, showing its capability of detecting bots' network traffic with 99.78% F-measure and classifying it to a malware family with a 99.09% F-measure. Notably, due to the modeling of general bot network behavior by the Markov Chains, BOTection can detect traffic belonging to unseen bot families with an F-measure of 93.03% making it robust against malware evolution.Accepted manuscrip

Emperical Validation of Online Features in the User Acceptance of Internet Banking Websites

Internet banking was introduced in the developed countries around the 1980s whereas in developing countries like Nigeria, it only started around the year 2000. This study focuses on investigating the online features of Internet banking websites of Nigerian banks and the impact of their relationships with the acceptance behaviors of users from an information systems (IS) view. The empirical results obtained from a sample survey of 55 Internet banking users indicate that technology acceptance model (TAM) is an appropriate model for use in forecasting the acceptance behaviors of Internet banking customers. It also indicates that online features have positive effect on customer’s perceived usefulness, attitude and behavioral intension to adopt Internet banking websites