This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants. Using a dataset containing hundreds of variants for several major malware families, our method achieves 98.6 classification accuracy using the signatures generated by the DBN. The presented method is completely agnostic to the type of malware behavior that is logged (e.g., API calls and their parameters, registry entries, websites and ports accessed, etc.), and can use any raw input from a sandbox to successfully train the deep neural network which is used to generate malware signatures.
The majority of anti-virus programs reply on analyzing the executable file to determine whether it is a malware. As Filiol and Josse @cite_9 establish, most current anti-virus programs do not detects variant of malware. They propose a method for automatic signature generation by analyzing the executable's code and substrings, and measure statistical distribution of code across variants of malware. Their experiments were performed on short (small sized) malware such as Nimda, Code Red Code Red II, MS Blaster, Sober, Netsky and Beagle. This method is less accurate when applied to larger malware. Most real world malware are large, containing many modules and sub-modules, and so a statistical analysis would not be sufficient to accurately classify them. @cite_14 generates a list of signatures for a malware by splitting its executable to segments of equal sizes. For each segment a signature is generated, and the list of signatures is subsequently ranked. This method is more resilient to small modifications in the executable, but a malware can evade this method by encrypting the executable (which is a simple and popular method for many malware programs), and thus evading any method which inspects the executable file for signature comparison.
@cite_9 @cite_14
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This research proposes a novel automatic method (termed Auto-Sign) for extracting unique signatures of malware executables to be used by high-speed malware filtering devices based on deep-packet inspection and operating in real-time. Contrary to extant string and token-based signature generation methods, we implemented Auto-Sign an automatic signature generation method that can be used on large-size malware by disregarding signature candidates which appear in benign executables. Results from experimental evaluation of the proposed method suggest that picking a collection of executables which closely represents commonly used code, plays a key role in achieving highly specific signatures which yield low false positives. In order to effectively evade anti-malware solutions, Android malware authors are progressively resorting to automatic obfuscation strategies. Recent works have shown, on small-scale experiments, the possibility of evading anti-malware engines by applying simple obfuscation transformations on previously detected malware samples. In this paper, we provide a large-scale experiment in which the detection performances of a high number of anti-malware solutions are tested against two different sets of malware samples that have been obfuscated according to different strategies. Moreover, we show that anti-malware engines search for possible malicious content inside assets and entry-point classes. We also provide a temporal analysis of the detection performances of anti-malware engines to verify if their resilience has improved since 2013. Finally, we show how, by manipulating the area of the Android executable that contains the strings used by the application, it is possible to deceive anti-malware engines so that they will identify legitimate samples as malware. On one hand, the attained results show that anti-malware systems have improved their resilience against trivial obfuscation techniques. On the other hand, more complex changes to the application executable have proved to be still effective against detection. Thus, we claim that a deeper static (or dynamic) analysis of the application is needed to improve the robustness of such systems. Android Smartphone popularity has increased malware threats forcing security researchers and AntiVirus (AV) industry to carve out smart methods to defend Smartphone against malicious apps. Robust signature based solutions to mitigate threats become necessary to protect the Smartphone and confidential user data. In this paper we present AndroSimilar, a robust approach which generates signature by extracting statistically improbable features, to detect malicious Android apps. Proposed method is effective against code obfuscation and repackaging, widely used techniques to evade AV signature and to propagate unseen variants of known malware. AndroSimilar is a syntactic foot-printing mechanism that finds regions of statistical similarity with known malware to detect those unknown, zero day samples. Syntactic file similarity of whole file is considered instead of just opcodes for faster detection compared to known fuzzy hashing approaches. Results demonstrate robust detection of variants of known malware families. Proposed approach can be refined to deploy as Smartphone AV. Because of the explosive growth of Android malware and due to the severity of its damages, the detection of Android malware has become an increasing important topic in cyber security. Currently, the major defense against Android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls for new defensive techniques that are harder to evade. In this paper, resting on the analysis of Application Programming Interface (API) calls extracted from the smali files, we further categorize the API calls which belong to the some method in the smali code into a block. Based on the generated code blocks, we then apply a deep learning framework (i.e., Deep Belief Network) for newly unknown Android malware detection. Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches. Promising experimental results demonstrate that DroidDelver which integrates our proposed method outperform other alternative Android malware detection techniques. Malicious code is an increasingly important problem that threatens the security of computer systems. The traditional line of defense against malware is composed of malware detectors such as virus and spyware scanners. Unfortunately, both researchers and malware authors have demonstrated that these scanners, which use pattern matching to identify malware, can be easily evaded by simple code transformations. To address this shortcoming, more powerful malware detectors have been proposed. These tools rely on semantic signatures and employ static analysis techniques such as model checking and theorem proving to perform detection. While it has been shown that these systems are highly effective in identifying current malware, it is less clear how successful they would be against adversaries that take into account the novel detection mechanisms. The goal of this paper is to explore the limits of static analysis for the detection of malicious code. To this end, we present a binary obfuscation scheme that relies on the idea of opaque constants, which are primitives that allow us to load a constant into a register such that an analysis tool cannot determine its value. Based on opaque constants, we build obfuscation transformations that obscure program control flow, disguise access to local and global variables, and interrupt tracking of values held in processor registers. Using our proposed obfuscation approach, we were able to show that advanced semantics-based malware detectors can be evaded. Moreover, our opaque constant primitive can be applied in a way such that is provably hard to analyze for any static code analyzer. This demonstrates that static analysis techniques alone might no longer be sufficient to identify malware. Malware includes several protections to complicate their analysis: the longer it takes to analyze a new malware sample, the longer the sample survives and the larger number of systems it compromises. Nowadays, new malware samples are analyzed dynamically using virtual environments (e.g., emulators, virtual machines, or debuggers). Therefore, malware incorporate a variety of tests to detect whether they are executed through such environments and obfuscate their behavior if they suspect their execution is being monitored. Several simple tests, we indistinctly call red-pills, have already been proposed in literature to detect whether the execution of a program is performed in a real or in a virtual environment. In this paper we propose an automatic and systematic technique to generate red-pills, specific for detecting if a program is executed through a CPU emulator. Using this technique we generated thousands of new red-pills, involving hundreds of different opcodes, for two publicly available emulators, which are widely used for analyzing malware. This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants. Using a dataset containing hundreds of variants for several major malware families, our method achieves 98.6 classification accuracy using the signatures generated by the DBN. The presented method is completely agnostic to the type of malware behavior that is logged (e.g., API calls and their parameters, registry entries, websites and ports accessed, etc.), and can use any raw input from a sandbox to successfully train the deep neural network which is used to generate malware signatures. The drastic increase of Android malware has led to a strong interest in developing methods to automate the malware analysis process. Existing automated Android malware detection and classification methods fall into two general categories: 1) signature-based and 2) machine learning-based. Signature-based approaches can be easily evaded by bytecode-level transformation attacks. Prior learning-based works extract features from application syntax, rather than program semantics, and are also subject to evasion. In this paper, we propose a novel semantic-based approach that classifies Android malware via dependency graphs. To battle transformation attacks, we extract a weighted contextual API dependency graph as program semantics to construct feature sets. To fight against malware variants and zero-day malware, we introduce graph similarity metrics to uncover homogeneous application behaviors while tolerating minor implementation differences. We implement a prototype system, DroidSIFT, in 23 thousand lines of Java code. We evaluate our system using 2200 malware samples and 13500 benign samples. Experiments show that our signature detection can correctly label 93 of malware instances; our anomaly detector is capable of detecting zero-day malware with a low false negative rate (2 ) and an acceptable false positive rate (5.15 ) for a vetting purpose. We present a novel network-level behavioral malware clustering system. We focus on analyzing the structural similarities among malicious HTTP traffic traces generated by executing HTTP-based malware. Our work is motivated by the need to provide quality input to algorithms that automatically generate network signatures. Accordingly, we define similarity metrics among HTTP traces and develop our system so that the resulting clusters can yield high-quality malware signatures. We implemented a proof-of-concept version of our network-level malware clustering system and performed experiments with more than 25,000 distinct malware samples. Results from our evaluation, which includes real-world deployment, confirm the effectiveness of the proposed clustering system and show that our approach can aid the process of automatically extracting network signatures for detecting HTTP traffic generated by malware-compromised machines. Malware programs that incorporate trigger-based behavior initiate malicious activities based on conditions satisfied only by specific inputs. State-of-the-art malware analyzers discover code guarded by triggers via multiple path exploration, symbolic execution, or forced conditional execution, all without knowing the trigger inputs. We present a malware obfuscation technique that automatically conceals specific trigger-based behavior from these malware analyzers. Our technique automatically transforms a program by encrypting code that is conditionally dependent on an input value with a key derived from the input and then removing the key from the program. We have implemented a compiler-level tool that takes a malware source program and automatically generates an obfuscated binary. Experiments on various existing malware samples show that our tool can hide a significant portion of trigger based code. We provide insight into the strengths, weaknesses, and possible ways to strengthen current analysis approaches in order to defeat this malware obfuscation technique. Malware remains an important security threat, as miscreants continue to deliver a variety of malicious programs to hosts around the world. At the heart of all the malware delivery techniques are executable files (known as downloader trojans or droppers) that download other malware. Because the act of downloading software components from the Internet is not inherently malicious, benign and malicious downloaders are difficult to distinguish based only on their content and behavior. In this paper, we introduce the downloader-graph abstraction, which captures the download activity on end hosts, and we explore the growth patterns of benign and malicious graphs. Downloader graphs have the potential of exposing large parts of the malware download activity, which may otherwise remain undetected. By combining telemetry from anti-virus and intrusion-prevention systems, we reconstruct and analyze 19 million downloader graphs from 5 million real hosts. We identify several strong indicators of malicious activity, such as the growth rate, the diameter, and the Internet access patterns of downloader graphs. Building on these insights, we implement and evaluate a machine learning system for malware detection. Our system achieves a 96.0 true-positive rate, with a 1.0 false-positive rate, and detects malware an average of 9.24 days earlier than existing anti-virus products. We also perform an external validation by examining a sample of unlabeled files that our system detects as malicious, and we find that 41.41 are blocked by anti-virus products. We propose a simple yet effective method for visualizing and classifying malware using image processing techniques. Malware binaries are visualized as gray-scale images, with the observation that for many malware families, the images belonging to the same family appear very similar in layout and texture. Motivated by this visual similarity, a classification method using standard image features is proposed. Neither disassembly nor code execution is required for classification. Preliminary experimental results are quite promising with 98 classification accuracy on a malware database of 9,458 samples with 25 different malware families. Our technique also exhibits interesting resilience to popular obfuscation techniques such as section encryption. System call analysis is a behavioral malware detection technique that is popular due to its promising detection results and ease of implementation. This study describes a system that uses system call analysis to detect malware that evade traditional defenses. The system monitors executing processes to identify compromised hosts in production environments. Experimental results compare the effectiveness of multiple feature extraction strategies and detectors based on their detection accuracy at low false positive rates. Logistic regression and support vector machines consistently outperform log-likelihood ratio and signature detectors as processing and detection methods. A feature selection study indicates that a relatively small set of system call 3-grams provide detection accuracy comparable to that of more complex models. A case study indicates that the detection system performs well against a variety of malware samples, benign workloads, and host configurations. Recently researchers have proposed using deep learning-based systems for malware detection. Unfortunately, all deep learning classification systems are vulnerable to adversarial attacks. Previous work has studied adversarial attacks against static analysis-based malware classifiers which only classify the content of the unknown file without execution. However, since the majority of malware is either packed or encrypted, malware classification based on static analysis often fails to detect these types of files. To overcome this limitation, anti-malware companies typically perform dynamic analysis by emulating each file in the anti-malware engine or performing in-depth scanning in a virtual machine. These strategies allow the analysis of the malware after unpacking or decryption. In this work, we study different strategies of crafting adversarial samples for dynamic analysis. These strategies operate on sparse, binary inputs in contrast to continuous inputs such as pixels in images. We then study the effects of two, previously proposed defensive mechanisms against crafted adversarial samples including the distillation and ensemble defenses. We also propose and evaluate the weight decay defense. Experiments show that with these three defensive strategies, the number of successfully crafted adversarial samples is reduced compared to a standard baseline system without any defenses. In particular, the ensemble defense is the most resilient to adversarial attacks. Importantly, none of the defenses significantly reduce the classification accuracy for detecting malware. Finally, we demonstrate that while adding additional hidden layers to neural models does not significantly improve the malware classification accuracy, it does significantly increase the classifier's robustness to adversarial attacks. The continuing fight against intentionally malicious software has, to date, favoured the proliferators of malware. Signature detection methods are growingly impotent against rapidly evolving obfuscation techniques. Research has recently focussed on the low-level opcode analysis of disassembled executable programs, both statically and dynamically. While able to detect malware, static analysis often still cannot unravel obfuscated code; dynamic approaches allow investigators to reveal the run-time code. Old and inadequately sampled datasets have limited the extrapolation potential of much of the body of research. This work presents a dynamic opcode analysis approach to malware detection, applying machine learning techniques to the largest dataset of its kind, both in terms of breadth (610–100k features) and depth (48k samples). N-gram analysis of opcode sequences from n = 1. . 3 was applied as a means of enhancing the feature set. Feature selection was then investigated to tackle the feature explosion which resulted in more than 100,000 features in some cases. As the earliest detection of malware is the most favourable, run-length, i.e. the number of recorded opcodes in a trace, was examined to find the optimal capture size. This research found that dynamic opcode analysis can detect malware from benignware with a 99.01 accuracy rate, using a sequence of only 32k opcodes and 50 features. This demonstrates that a dynamic opcode analysis approach can compare with static analysis in terms of speed. Furthermore, it has a very real potential application to the unending fight against malware, which is, by definition, continuously on the back foot. Smartphone sales have recently experienced explosive growth. Their popularity also encourages malware authors to penetrate various mobile marketplaces with malicious applications (or apps). These malicious apps hide in the sheer number of other normal apps, which makes their detection challenging. Existing mobile anti-virus software are inadequate in their reactive nature by relying on known malware samples for signature extraction. In this paper, we propose a proactive scheme to spot zero-day Android malware. Without relying on malware samples and their signatures, our scheme is motivated to assess potential security risks posed by these untrusted apps. Specifically, we have developed an automated system called RiskRanker to scalably analyze whether a particular app exhibits dangerous behavior (e.g., launching a root exploit or sending background SMS messages). The output is then used to produce a prioritized list of reduced apps that merit further investigation. When applied to examine 118,318 total apps collected from various Android markets over September and October 2011, our system takes less than four days to process all of them and effectively reports 3281 risky apps. Among these reported apps, we successfully uncovered 718 malware samples (in 29 families) and 322 of them are zero-day (in 11 families). These results demonstrate the efficacy and scalability of RiskRanker to police Android markets of all stripes. Malware is constantly adapting in order to avoid detection. Model-based malware detectors, such as SVM and neural networks, are vulnerable to so-called adversarial examples which are modest changes to detectable malware that allows the resulting malware to evade detection. Continuous-valued methods that are robust to adversarial examples of images have been developed using saddle-point optimization formulations. We are inspired by them to develop similar methods for the discrete, e.g. binary, domain which characterizes the features of malware. A specific extra challenge of malware is that the adversarial examples must be generated in a way that preserves their malicious functionality. We introduce methods capable of generating functionally preserved adversarial malware examples in the binary domain. Using the saddle-point formulation, we incorporate the adversarial examples into the training of models that are robust to them. We evaluate the effectiveness of the methods and others in the literature on a set of Portable Execution (PE) files. Comparison prompts our introduction of an online measure computed during training to assess general expectation of robustness. Android is targeted the most by malware coders as the number of Android users is increasing. Although there are many Android anti-malware solutions available in the market, almost all of them are based on malware signatures, and more advanced solutions based on machine learning techniques are not deemed to be practical for the limited computational resources of mobile devices. In this paper we aim to show not only that the computational resources of consumer mobile devices allow deploying an efficient anti-malware solution based on machine learning techniques, but also that such a tool provides an effective defense against novel malware, for which signatures are not yet available. To this end, we first propose the extraction of a set of lightweight yet effective features from Android applications. Then, we embed these features in a vector space, and use a pre-trained machine learning model on the device for detecting malicious applications. We show that without resorting to any signatures, and relying only on a training phase involving a reasonable set of samples, the proposed system outperforms many commercial anti-malware products, as well as providing slightly better performances than the most effective commercial products. Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior. Android being the most popular open source mobile operating system, attracts a plethora of app developers. Millions of applications are developed for Android platform with a great extent of behavioral diversities and are available on Play Store as well as on many third party app stores. Due to its open nature, in the past Android Platform has been targeted by many malware writers. The conventional way of signature-based detection methods for detecting malware on a device are no longer promising due to an exponential increase in the number of variants of the same application with different signatures. Moreover, they lack in dynamic analysis too. In this paper, we propose DRACO, which employs a two-phase detection technique that blends the synergy of both static and dynamic analysis. It has two modules, client module that is in the form an Android app and gets installed on mobile devices and a server module that runs on a server. DRACO also explains user about the features contributing to the maliciousness of analyzed app and generates scoring for that maliciousness. It does not require any root or super-user privileges. In an evaluation of 18,000 benign applications and 10,000 malware samples, DRACO performs better than several related existing approaches and detects 98.4 of the malware with few false alerts. On ten popular smartphones, the method requires an average of 6 seconds for on device analysis and 90 seconds on server analysis.