Resilient and Scalable Android Malware Fingerprinting and Detection

Malicious software (Malware) proliferation reaches hundreds of thousands daily. The manual analysis of such a large volume of malware is daunting and time-consuming. The diversity of targeted systems in terms of architecture and platforms compounds the challenges of Android malware detection and malware in general. This highlights the need to design and implement new scalable and robust methods, techniques, and tools to detect Android malware. In this thesis, we develop a malware fingerprinting framework to cover accurate Android malware detection and family attribution. In this context, we emphasize the following: (i) the scalability over a large malware corpus; (ii) the resiliency to common obfuscation techniques; (iii) the portability over different platforms and architectures. In the context of bulk and offline detection on the laboratory/vendor level: First, we propose an approximate fingerprinting technique for Android packaging that captures the underlying static structure of the Android apps. We also propose a malware clustering framework on top of this fingerprinting technique to perform unsupervised malware detection and grouping by building and partitioning a similarity network of malicious apps. Second, we propose an approximate fingerprinting technique for Android malware's behavior reports generated using dynamic analyses leveraging natural language processing techniques. Based on this fingerprinting technique, we propose a portable malware detection and family threat attribution framework employing supervised machine learning techniques. Third, we design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. We leverage graph analysis techniques to generate relevant, actionable, and granular intelligence that can be used to identify the threat effects induced by malicious Internet activity associated to Android malicious apps. In the context of the single app and online detection on the mobile device level, we further propose the following: Fourth, we design a portable and effective Android malware detection system that is suitable for deployment on mobile and resource constrained devices, using machine learning classification on raw method call sequences. Fifth, we elaborate a framework for Android malware detection that is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. We also evaluate the portability of the proposed techniques and methods beyond Android platform malware, as follows: Sixth, we leverage the previously elaborated techniques to build a framework for cross-platform ransomware fingerprinting relying on raw hybrid features in conjunction with advanced deep learning techniques

DATDroid: Dynamic Analysis Technique in Android Malware Detection

Android system has become a target for malware developers due to its huge market globally in recent years. The emergence of 5G in the market and limited protocols post a great challenge to the security in Android. Hence, various techniques have been taken by researchers to ensure high security in Android devices. There are three types of analysis namely static, dynamic and hybrid analysis used to detect and analyze the malicious application in Android. Due to evolving nature of the malware, it is very challenging for the existing techniques to detect and analyze it efficiently and accurately. This paper proposed a Dynamic Analysis Technique in Android Malware detection called DATDroid. The proposed technique consists of three phases, which includes feature extraction, feature selection and classification phases. A total of five features namely system call, errors and time of system call process, CPU usage, memory and network packets are extracted. During the classification 70% of the dataset was allocated for training phase and 30% for testing phase using machine learning algorithm. Our experimental results achieved an overall accuracy of 91.7% with lower false positive rates as compared to benchmarked method. DATDroid also achieved higher precision and recall rate of 93.1% and 90.0%, respectively. Hence our proposed technique has proven to be able to classify malware more accurately and reduce misclassification of malware application as benign significantly

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

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications

Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead