Android is one of the leading operating systems for smart phones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts' intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce; (1) a novel technique for opcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we have developed a dataset for end-to-end detection of android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted features extraction, and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47%, precision of 0.976 and F1 score of 0.979.
The Android operating system is the most spread mobile platform in the world. Therefor attackers are producing an incredible number of malware applications for Android. Our aim is to detect Android's malware in order to protect the user. To do so really good results are obtained by dynamic analysis of software, but it requires complex environments. In order to achieve the same level of precision we analyze the machine code and investigate the frequencies of ngrams of opcodes in order to detect singular code blocks. This allow us to construct a database of infected code blocks. Then, because at…
Nowadays, with the booming development of Internet and software industry, more and more malware variants are designed to perform various malicious activities. Traditional signature-based detection methods can not detect variants of malware. In addition, most behavior-based methods require a secure and isolated environment to perform malware detection, which is vulnerable to be contaminated. In this paper, similar to natural language processing, we propose a novel and efficient approach to perform static malware analysis, which can automatically learn the opcode sequence patterns of malware. We…
Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge f…
Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eli…
Centre for Secure Information Technologies (CSIT)（安全信息技术中心（CSIT））；Queen’s University Belfast（贝尔法斯特女王大学）
In this paper we study data augmentation for opcode sequence based Android malware detection. Data augmentation has been successfully used in many areas of deep-learning to significantly improve model performance. Typically, data augmentation simulates realistic variations in data to increase the apparent diversity of the training-set. However, for opcode-based malware analysis it is not immediately clear how to apply data augmentation. Hence we first study the use of fixed transformations, then progress to adaptive methods. We propose a novel data augmentation method -- Self-Embedding Languag…
Malwares are the key means leveraged by threat actors in the cyber space for their attacks. There is a large array of commercial solutions in the market and significant scientific research to tackle the challenge of the detection and defense against malwares. At the same time, attackers also advance their capabilities in creating polymorphic and metamorphic malwares to make it increasingly challenging for existing solutions. To tackle this issue, we propose a methodology to perform malware detection and family attribution. The proposed methodology first performs the extraction of opcodes from …
Centre for Secure Information Technologies (CSIT)（安全信息技术中心（CSIT））；Queen’s University Belfast（贝尔法斯特女王大学）
We propose the Malceiver, a hierarchical Perceiver model for Android malware detection that makes use of multi-modal features. The primary inputs are the opcode sequence and the requested permissions of a given Android APK file. To reach a malware classification decision the model combines hierarchical features extracted from the opcode sequence together with the requested permissions. The model's architecture is based on the Perceiver/PerceiverIO which allows for very long opcode sequences to be processed efficiently. Our proposed model can be easily extended to use multi-modal features. We s…
Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party libraries in Android application development and the widespread use of repackaging in malware development. We design and implement an Android malware clustering system through iterative mining of malicious payload and checking whether malware samples share the same version of malicious payload. Our system utilizes a hierarchical clustering techn…
The number of Android malware variants (clones) are on the rise and, to stop this attack of clones we need to develop new methods and techniques for analysing and detecting them. As a first step, we need to study how these malware clones are generated. This will help us better anticipate and recognize these clones. In this paper we present a new tool named DroidMorph, that provides morphing of Android applications (APKs) at different level of abstractions, and can be used to create Android application (malware/benign) clones. As a case study we perform testing and evaluating resilience of curr…
The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable…
The popularity of Android system, not only in the handset devices but also in IoT devices, makes it a very attractive destination for malware. Indeed, malware is expanding at a similar rate targeting such devices that rely, in most cases, on Internet to work properly. The state-of-the-art malware mitigation solutions mainly focus on the detection of the actual malicious Android apps using dy- namic and static analyses features to distinguish malicious apps from benign ones. However, there is a small coverage for the In- ternet/network dimension of the Android malicious apps. In this paper, we …
With the increasing popularity of Android in the last decade, Android is popular among users as well as attackers. The vast number of android users grabs the attention of attackers on android. Due to the continuous evolution of the variety and attacking techniques of android malware, our detection methods should need an update too. Most of the researcher's works are based on static features, and very few focus on dynamic features. In this paper, we are filling the literature gap by detecting android malware using System calls. We are running the malicious app in a monitored and controlled envi…
The astonishing spread of Android OS, not only in smartphones and tablets but also in IoT devices, makes this operating system a very tempting target for malware threats. Indeed, the latter are expanding at a similar rate. In this respect, malware fingerprints, whether based on cryptographic or fuzzy-hashing, are the first defense line against such attacks. Fuzzy-hashing fingerprints are suitable for capturing malware static features. Moreover, they are more resilient to small changes in the actual static content of malware files. On the other hand, dynamic analysis is another technique for ma…
Android malware detection is a significat problem that affects billions of users using millions of Android applications (apps) in existing markets. This paper proposes PetaDroid, a framework for accurate Android malware detection and family clustering on top of static analyses. PetaDroid automatically adapts to Android malware and benign changes over time with resilience to common binary obfuscation techniques. The framework employs novel techniques elaborated on top of natural language processing (NLP) and machine learning techniques to achieve accurate, adaptive, and resilient Android malwar…
Android is currently the most extensively used smartphone platform in the world. Due to its popularity and open source nature, Android malware has been rapidly growing in recent years, and bringing great risks to users' privacy. The malware applications in a malware family may have common features and similar behaviors, which are beneficial for malware detection and inspection. Thus, classifying Android malware into their corresponding families is an important task in malware analysis. At present, the main problem of existing research works on Android malware family classification lies in that…
Cryptography has been extensively used in Android applications to guarantee secure communications, conceal critical data from reverse engineering, or ensure mobile users' privacy. Various system-based and third-party libraries for Android provide cryptographic functionalities, and previous works mainly explored the misuse of cryptographic API in benign applications. However, the role of cryptographic API has not yet been explored in Android malware. This paper performs a comprehensive, longitudinal analysis of cryptographic API in Android malware. In particular, we analyzed $603\,937$ Android …
The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years. An important task of malware analysis is the classification of malware samples into known families. Static malware analysis is known to fall short against techniques that change static characteristics of the malware (e.g. code obfuscation), while dynamic analysis has proven effective against such techniques. To the best of our knowledge, the most notable work on Android malware family classification purely based on dynamic analysis is DroidScri…
With the popularity of Android growing exponentially, the amount of malware has significantly exploded. It is arguably one of the most viral problems on mobile platforms. Recently, various approaches have been introduced to detect Android malware, the majority of these are either based on the Manifest File features or the structural information, such as control flow graph and API calls. Among those methods, nearly all of them only consider the Java byte-code as the target to detect malicious behaviors. However, Recent research and our own statistics show that native payloads are commonly used …
According to the Symantec and F-Secure threat reports, mobile malware development in 2013 and 2014 has continued to focus almost exclusively ~99% on the Android platform. Malware writers are applying stealthy mutations (obfuscations) to create malware variants, thwarting detection by signature based detectors. In addition, the plethora of more sophisticated detectors making use of static analysis techniques to detect such variants operate only at the bytecode level, meaning that malware embedded in native code goes undetected. A recent study shows that 86% of the most popular Android applicati…
Smartphones and mobile devices are rapidly becoming indispensable devices for many users. Unfortunately, they also become fertile grounds for hackers to deploy malware and to spread virus. There is an urgent need to have a "security analytic & forensic system" which can facilitate analysts to examine, dissect, associate and correlate large number of mobile applications. An effective analytic system needs to address the following questions: How to automatically collect and manage a high volume of mobile malware? How to analyze a zero-day suspicious application, and compare or associate it with …
Android malware is a persistent threat to billions of users around the world. As a countermeasure, Android malware detection systems are occasionally implemented. However, these systems are often vulnerable to \emph{evasion attacks}, in which an adversary manipulates malicious instances so that they are misidentified as benign. In this paper, we launch various innovative evasion attacks against several Android malware detection systems. The vulnerability inherent to all of these systems is that they are part of Androguard~\cite{desnos2011androguard}, a popular open source library used in Andro…
Malware classification is an important and challenging problem in information security. Modern malware classification techniques rely on machine learning models that can be trained on features such as opcode sequences, API calls, and byte $n$-grams, among many others. In this research, we consider opcode features. We implement hybrid machine learning techniques, where we engineer feature vectors by training hidden Markov models -- a technique that we refer to as HMM2Vec -- and Word2Vec embeddings on these opcode sequences. The resulting HMM2Vec and Word2Vec embedding vectors are then used as f…
Repackaging is a technique that has been increasingly adopted by authors of Android malware. The main problem facing the research community working on devising techniques to detect this breed of malware is the lack of ground truth that pinpoints the malicious segments grafted within benign apps. Without this crucial knowledge, it is difficult to train reliable classifiers able to effectively classify novel, out-of-sample repackaged malware. To circumvent this problem, we argue that reliable classifiers can be trained to detect repackaged malware, if they are allowed to request new, more accura…
Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform. Without such view, the researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this paper, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.2K families over a period of eight years (from 2010 to 2017). We aim at understanding how the behavior of Android malwa…
Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional under-ground actors, however previous studies overlooked such information as a feature in detecting and classifying malware, and in attributing malware to creators. Guided by this insight, we propose a method to improve on the performance of Android malware detection by incorporating the creator's information as a feature and classify malicious applications into similar groups. We develope…
Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduc…
The most common malware detection approaches which are based on signature matching and are not sufficient for metamorphic malware detection, since virus kits and metamorphic engines can produce variants with no resemblance to one another. Metamorphism provides an efficient way for eluding malware detection software kits. Code obfuscation methods like dead-code insertion are also widely used in metamorphic malware. In order to address the problem of detecting mutated generations, we propose a method based on Opcode Graph Similarity (OGS). OGS tries to detect metamorphic malware using the simila…
As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users face, the detection of malware on Android devices has become an increasingly important issue in cyber security. Traditional methods like signature-based routines are unable to protect users from the ever-increasing sophistication and rapid behavior changes in new types of Android malware. Therefore, a great deal of effort has been made recently to use machine learning models and methods to characterize and generalize th…
In evaluating detection methods, the malware research community relies on scan results obtained from online platforms such as VirusTotal. Nevertheless, given the lack of standards on how to interpret the obtained data to label apps, researchers hinge on their intuitions and adopt different labeling schemes. The dynamicity of VirusTotal's results along with adoption of different labeling schemes significantly affect the accuracies achieved by any given detection method even on the same dataset, which gives subjective views on the method's performance and hinders the comparison of different malw…
Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts for a long time. In this paper, we propose Droidetec, a deep learning based method for android malware detection and malicious code localization, to model an application program as a natural language sequence. Droidetec adopts a novel feature extraction method to derive behavior sequences from Android applications. Based on that, the bi-directional Long Short Term Memory network is utilized for malware detection. Each …
The Android operating system is the most spread mobile platform in the world. Therefor attackers are producing an incredible number of malware applications for Android. Our aim is to detect Android's malware in order to protect the user. To do so really good results are obtained by dynamic analysis of software, but it requires complex environments. In order to achieve the same level of precision we analyze the machine code and investigate the frequencies of ngrams of opcodes in order to detect singular code blocks. This allow us to construct a database of infected code blocks. Then, because at…
With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches. Traditional methods such as signature-based ones cannot defend users from an increasing number of new types of malware or rapid malware behavior changes. In this paper, we propose a new Android malware detection approach based on deep learning and static analysis. Instead of using Application Programming Interfaces (APIs) only, we further analyze the source code of Android…
The growth in the number of Android and Internet of Things (IoT) devices has witnessed a parallel increase in the number of malicious software (malware), calling for new analysis approaches. We represent binaries using their graph properties of the Control Flow Graph (CFG) structure and conduct an in-depth analysis of malicious graphs extracted from the Android and IoT malware to understand their differences. Using 2,874 and 2,891 malware binaries corresponding to IoT and Android samples, we analyze both general characteristics and graph algorithmic properties. Using the CFG as an abstract str…
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. W…
Machine learning based solutions have been successfully employed for automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen perturbations to the normal inputs. So far, the adversarial examples can only deceive detectors that rely on syntactic features (e.g., requested permissions, API calls, etc), and the perturbations can only be implemented by simply modifying application's manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than m…
As Android malware is growing and evolving, deep learning has been introduced into malware detection, resulting in great effectiveness. Recent work is considering hybrid models and multi-view learning. However, they use only simple features, limiting the accuracy of these approaches in practice. In this paper, we propose DeepCatra, a multi-view learning approach for Android malware detection, whose model consists of a bidirectional LSTM (BiLSTM) and a graph neural network (GNN) as subnets. The two subnets rely on features extracted from statically computed call traces leading to critical APIs …
We present BPFroid -- a novel dynamic analysis framework for Android that uses the eBPF technology of the Linux kernel to continuously monitor events of user applications running on a real device. The monitored events are collected from different components of the Android software stack: internal kernel functions, system calls, native library functions, and the Java API framework. As BPFroid hooks these events in the kernel, a malware is unable to trivially bypass monitoring. Moreover, using eBPF doesn't require any change to the Android system or the monitored applications. We also present an…
The daily amount of Android malicious applications (apps) targeting the app repositories is increasing, and their number is overwhelming the process of fingerprinting. To address this issue, we propose an enhanced Cypider framework, a set of techniques and tools aiming to perform a systematic detection of mobile malware by building a scalable and obfuscation resilient similarity network infrastructure of malicious apps. Our approach is based on our proposed concept, namely malicious community, in which we consider malicious instances that share common features are the most likely part of the s…
Researchers and commercial companies have made a lot of efforts on detecting malware in Android platform. However, a recent malware threat, App collusion, makes malware detection challenging. In App collusion, two or more Apps collaborate to perform malicious actions by communicating with each other, which makes single App analysis insufficient. In this paper, we first introduce Android security mechanism and communication channels used by android Applications. Then we summarize the security vulnerabilities and potential threats introduced by App communication. Finally, we discuss state of art…
Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks. In fact, people have discovered around one million new malware samples per quarter, and it was reported that over 98% of these new malware samples are in fact "derivatives" (or variants) from existing malware families. In this paper, we first show that runtime behaviors of malware's core functionalities are in fact similar within a malware family. Hence, we propose a framework to combine "runtime behavior" with "static structures" to detect malware variants. …
Android is undergoing unprecedented malicious threats daily, but the existing methods for malware detection often fail to cope with evolving camouflage in malware. To address this issue, we present HAWK, a new malware detection framework for evolutionary Android applications. We model Android entities and behavioural relationships as a heterogeneous information network (HIN), exploiting its rich semantic metastructures for specifying implicit higher-order relationships. An incremental learning model is created to handle the applications that manifest dynamically, without the need for re-constr…
With the increasing user base of Android devices and advent of technologies such as Internet Banking, delicate user data is prone to be misused by malware and spyware applications. As the app developer community increases, the quality reassurance could not be justified for every application and a possibility of data leakage arises. In this research, with the aim to ensure the application authenticity, Deep Learning methods and Taint Analysis are deployed on the applications. The detection system named DroidMark looks for possible sinks and sources of data leakage in the application by modellin…
The importance of employing machine learning for malware detection has become explicit to the security community. Several anti-malware vendors have claimed and advertised the application of machine learning in their products in which the inference phase is performed on servers and high-performance machines, but the feasibility of such approaches on mobile devices with limited computational resources has not yet been assessed by the research community, vendors still being skeptical. In this paper, we aim to show the practicality of devising a learning-based anti-malware on Android mobile device…
With the development in the field of smartphones and ever growing base of Internet, various softwares are left prone to many malicious activities like pharming, phishing, ransomware, spam, spoofing, spyware, eavesdropping, etc. These threats have not spared the smartphones which are equally prone to them. In this work, we aim to detect these malwares with accuracy and efficiency. This being essentially a classification problem, we use various machine learning methods for this task. We observe that across models, Attention based Artificial Neural Networks (ANN), or broadly speaking, Deep Learni…
Android is the most widely used smartphone OS with 82.8% market share in 2015. It is therefore the most widely targeted system by malware authors. Researchers rely on dynamic analysis to extract malware behaviors and often use emulators to do so. However, using emulators lead to new issues. Malware may detect emulation and as a result it does not execute the payload to prevent the analysis. Dealing with virtual device evasion is a never-ending war and comes with a non-negligible computation cost. To overcome this state of affairs, we propose a system that does not use virtual devices for analy…
The rise in popularity of the Android platform has resulted in an explosion of malware threats targeting it. As both Android malware and the operating system itself constantly evolve, it is very challenging to design robust malware mitigation techniques that can operate for long periods of time without the need for modifications or costly re-training. In this paper, we present MaMaDroid, an Android malware detection system that relies on app behavior. MaMaDroid builds a behavioral model, in the form of a Markov chain, from the sequence of abstracted API calls performed by an app, and uses it t…
Malware authors reuse the same program segments found in other applications for performing the similar kind of malicious activities such as information stealing, sending SMS and so on. Hence, there may exist several semantically similar malware samples in a family/dataset. Many researchers unaware about these semantically similar apps and use their features in their ML models for evaluation. Hence, the performance measures might be seriously affected by these similar kinds of apps. In this paper, we study the impact of semantically similar applications in the performance measures of ML based A…
Android has become the most popular mobile operating system. Correspondingly, an increasing number of Android malware has been developed and spread to steal users' private information. There exists one type of malware whose benign behaviors are developed to camouflage malicious behaviors. The malicious component occupies a small part of the entire code of the application (app for short), and the malicious part is strongly coupled with the benign part. In this case, the malware may cause false negatives when malware detectors extract features from the entire apps to conduct classification becau…
Widespread growth in Android malwares stimulates security researchers to propose different methods for analyzing and detecting malicious behaviors in applications. Nevertheless, current solutions are ill-suited to extract the fine-grained behavior of Android applications accurately and efficiently. In this paper, we propose ServiceMonitor, a lightweight host-based detection system that dynamically detects malicious applications directly on mobile devices. ServiceMonitor reconstructs the fine-grained behavior of applications based on a novel systematic system service use analysis technique. Usi…
Discrete hidden Markov models (HMM) are often applied to malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture model-HMMs (GMM-HMM), are rarely considered in the field of cybersecurity. In this paper, we use GMM-HMMs for malware classification and we compare our results to those obtained using discrete HMMs. As features, we consider opcode sequences and entropy-based sequences. For our opcode features, GMM-HMMs produce results that are comparable to those obtained using discrete HMMs, whereas for our entropy-based features, GM…
Ransomware is a significant global threat, with easy deployment due to the prevalent ransomware-as-a-service model. Machine learning algorithms incorporating the use of opcode characteristics and Support Vector Machine have been demonstrated to be a successful method for general malware detection. This research focuses on crypto-ransomware and uses static analysis of malicious and benign Portable Executable files to extract 443 opcodes across all samples, representing them as density histograms within the dataset. Using the SMO classifier and PUK kernel in the WEKA machine learning toolset it …
Nowadays, Android is the most dominant operating system in the mobile ecosystem, with billions of people using its apps daily. As expected, this trend did not go unnoticed by miscreants, and Android became the favorite platform for discovering new victims through malicious apps. Moreover, these apps have become so sophisticated that they can bypass anti-malware measures to protect the users. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, sparking the urge to develop an efficient way to detect Android malware. This paper presents hybrid-Flacon, a…
