The rapid growth of the Internet of Things (IoT) devices is paralleled by them being on the front-line of malicious attacks. This has led to an explosion in the number of IoT malware, with continued mutations, evolution, and sophistication. Malware samples are detected using machine learning (ML) algorithms alongside the traditional signature-based methods. Although ML-based detectors improve the detection performance, they are susceptible to malware evolution and sophistication, making them limited to the patterns that they have been trained upon. This continuous trend motivates large body of literature on malware analysis and detection research, with many systems emerging constantly, outperforming their predecessors. In this paper, we systematically examine the state-of-the-art malware detection approaches, that utilize various representation and learning techniques, under a range of adversarial settings. Our analyses highlight the instability of the proposed detectors in learning patterns that distinguish the benign from the malicious software. The results exhibit that software mutations with functionality-preserving operations, such as stripping and padding, significantly deteriorate the accuracy of such detectors. Additionally, our analysis of the industry-standard malware detectors shows their instability to the malware mutations. Through extensive experiments, we highlight the gap between the capabilities of the adversary and that of the existing malware detectors. The evaluations and analyses show that the optimal malware detection system is nowhere near and calls for the community to streamline their efforts towards testing the robustness of malware detectors to different manipulation techniques.

The Linux shell is a command-line interpreter that provides users with a command interface to the operating system, allowing them to perform various functions. Although very useful in building capabilities at the edge, the Linux shell can be exploited, giving adversaries a prime opportunity to use them for malicious activities. With access to Internet of Things (IoT) devices, malware authors can abuse the Linux shell of those devices to propagate infections and launch large-scale attacks, e.g., Distributed Denial of Service. In this work, we provide a first look at the tasks managed by shell commands in Linux-based IoT malware toward detection. We analyze malicious shell commands found in IoT malware and build a neural network-based model, ShellCore, to detect malicious shell commands. Namely, we collected a large data set of shell commands, including malicious commands extracted from 2891 IoT malware samples and benign commands collected from real-world network traffic analysis and volunteered data from Linux users. Using conventional machine and deep learning-based approaches trained with a term- and character-level features, ShellCore is shown to achieve an accuracy of more than 99% in detecting malicious shell commands and files (i.e., binaries).

The acceptance of the Internet of Things (IoT) for both household and industrial applications is accompanied by the rapid growth of IoT malware. With the increase of their attack surface, analyzing, understanding, and detecting IoT malicious behavior are crucial. Traditionally, machine and deep learning-based approaches are used for malware detection and behavioral understanding. However, recent research has shown the susceptibility of those approaches to adversarial attacks by introducing noise to the feature space. In this work, we introduce DL-FHMC, a fine-grained hierarchical learning approach for robust IoT malware detection. DL-FHMC utilizes Control Flow Graph (CFG)-based behavioral patterns for adversarial IoT malicious software detection. In particular, we extract a comprehensive list of behavioral patterns from a large dataset of malicious IoT binaries, represented by the shared execution flows, and use them as a modality for malicious behavior detection. Leveraging machine learning and subgraph isomorphism matching algorithms, DL-FHMC provides state-of-the-art performance in detecting malware samples and adversarial examples (AEs). We first highlight the caveats of CFG-based IoT malware detection systems, showing the adversarial capabilities in generating practical functionality-preserving AEs with reduced overhead using Graph Embedding and Augmentation (GEA) techniques. We then introduce Suspicious Behavior Detector, a component that extracts comprehensive behavioral patterns from three popular IoT malicious families, Gafgyt, Mirai, and Tsunami, for AEs detection with high accuracy. The proposed detector operates as a model-independent standalone module, with no prior assumptions of the adversarial attacks nor their configurations.

The privacy policy statements are the primary mean for service providers to inform Internet users about their data collection and use practices, although they often are long and lack a specific structure. In this work, we introduce TLDR, a pipeline that employs various deep representation techniques for normalizing policies through learning and modeling, and an automated ensemble classifier for privacy policy classification. TLDR advances the state-of-the-art by (i) categorizing policy contents into nine privacy policy categories with high accuracy, (ii) detecting missing information in privacy policies, and (iii) significantly reducing policy reading time and improving understandability by users.

Detection of adversarial examples with high accuracy is critical for the security of deployed deep neural network-based models. We present the first graph-based adversarial detection method that constructs a Latent Neighborhood Graph (LNG) around an input example to determine if the input example is adversarial. Given an input example, selected reference adversarial and benign examples (represented as LNG nodes in Figure 1) are used to capture the local manifold in the vicinity of the input example. The LNG node connectivity parameters are optimized jointly with the parameters of a graph attention network in an end-to-end manner to determine the optimal graph topology for adversarial example detection. The graph attention network is used to determine if the LNG is derived from an adversarial or benign input example. Experimental evaluations on CIFAR-10, STL-10, and ImageNet datasets, using six adversarial attack methods, demonstrate that the proposed method outperforms state-of-the-art adversarial detection methods in white-box and gray-box settings. The proposed method is able to successfully detect adversarial examples crafted with small perturbations using unseen attacks.

Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and interact with sensitive and private information. The security and protection of such personal information are becoming more and more important since mobile devices are vulnerable to unauthorized access or theft. User authentication is a task of paramount importance that grants access to legitimate users at the point of entry and continuously through the usage session. This task is made possible with today's smartphones' embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits. In this article, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods (28 studies), gait-based methods (19 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods (34 studies). The survey provides an overview of the current state-of-the-art approaches for continuous user authentication using behavioral biometrics captured by smartphones' embedded sensors, including insights and open challenges for adoption, usability, and performance.

Deep learning algorithms have been widely used for security applications, including malware detection and classification. Recent results have shown that those algorithms are vulnerable to adversarial examples, whereby a small perturbation in the input sample may result in misclassification. In this paper, we systematically tackle the problem of adversarial examples detection in the control flow graph (CFG) based classifiers for malware detection using Soteria. Unique to Soteria, we use both density-based and level-based labels for CFG labeling to yield a consistent representation, a random walk-based traversal approach for feature extraction, and n-gram based module for feature representation. End-to-end, Soteria's representation ensures a simple yet powerful randomization property of the used classification features, making it difficult even for a powerful adversary to launch a successful attack. Soteria also employs a deep learning approach, consisting of an auto-encoder for detecting adversarial examples, and a CNN architecture for detecting and classifying malware samples. We evaluate the performance of Soteria, using a large dataset consisting of 16,814 IoT samples, and demonstrate its superiority in comparison with state-of-the-art approaches. In particular, Soteria yields an accuracy rate of 97.79% for detecting AEs, and 99.91% overall accuracy for classification malware families.

The Internet has become an essential part of children’s and adolescents’ daily life. Social media platforms are used as educational and entertainment resources on daily bases by young users, leading enormous efforts to ensure their safety when interacting with various social media platforms. In this paper, we investigate the exposure of those users to inappropriate and malicious content in comments posted on YouTube videos targeting this demographic. We collected a large-scale dataset of approximately four million records, and studied the presence of malicious and inappropriate URLs embedded in the comments posted on these videos. Our results show a worrisome number of malicious and inappropriate URLs embedded in comments available for children and young users. In particular, we observe an alarming number of inappropriate and malicious URLs, with a high chance of kids exposure, since the average number of views on videos containing such URLs is 48 million. When using such platforms, children are not only exposed to the material available in the platform, but also to the content of the URLs embedded within the comments. This highlights the importance of monitoring the URLs provided within the comments, limiting the children’s exposure to inappropriate content.

The Onion Router (Tor) is designed to support an anonymous communication through end-to-end encryption. To prevent vulnerability of side channel attacks (e.g. website fingerprinting), dummy packet injection modules have been embedded in Tor to conceal trace patterns that are associated with the individual websites. However, recent study shows that current Website Fingerprinting (WF) defenses still generate patterns that may be captured and recognized by the deep learning technology. In this paper, we conduct in-depth analyses of two state-of-the-art WF defense approaches. Then, based on our new observations and insights, we propose a novel defense mechanism using a per-burst injection technique, called Deep Fingerprinting Defender (DFD), against deep learning-based WF attacks. The DFD has two operation modes, one-way and two-way injection. DFD is designed to break the inherent patterns preserved in Tor user's traces by carefully injecting dummy packets within every burst. We conducted extensive experiments to evaluate the performance of DFD over both closed-world and open-world settings. Our results demonstrate that these two configurations can successfully break the Tor network traffic pattern and achieve a high evasion rate of 86.02% over one-way client-side injection rate of 100%, a promising improvement in comparison with state-of-the-art adversarial trace's evasion rate of 60%. Moreover, DFD outperforms the state-of-the-art alternatives by requiring lower bandwidth overhead; 14.26% using client-side injection.

IoT malware detection using control flow graph (CFG)-based features and deep learning networks are widely explored. The main goal of this study is to investigate the robustness of such models against adversarial learning. We designed two approaches to craft adversarial IoT software: off-the-shelf methods and Graph Embedding and Augmentation (GEA) method. In the off-the-shelf adversarial learning attack methods, we examine eight different adversarial learning methods to force the model to misclassification. The GEA approach aims to preserve the functionality and practicality of the generated adversarial sample through a careful embedding of a benign sample to a malicious one. Intensive experiments are conducted to evaluate the performance of the proposed method, showing that off-the-shelf adversarial attack methods are able to achieve a misclassification rate of 100%. In addition, we observed that the GEA approach is able to misclassify all IoT malware samples as benign. The findings of this work highlight the essential need for more robust detection tools against adversarial learning, including features that are not easy to manipulate, unlike CFG-based features. The implications of the study are quite broad, since the approach challenged in this work is widely used for other applications using graphs.

The main goal of this study is to investigate the robustness of graph-based Deep Learning (DL) models used for Internet of Things (IoT) malware classification against Adversarial Learning (AL). We designed two approaches to craft adversarial IoT software, including Off-the-Shelf Adversarial Attack (OSAA) methods, using six different AL attack approaches, and Graph Embedding and Augmentation (GEA). The GEA approach aims to preserve the functionality and practicality of the generated adversarial sample through a careful embedding of a benign sample to a malicious one. Our evaluations demonstrate that OSAAs are able to achieve a misclassification rate (MR) of 100%. Moreover, we observed that the GEA approach is able to misclassify all IoT malware samples as benign.

The steady growth in the number of deployed Internet of Things (IoT) devices has been paralleled with an equal growth in the number of malicious software (malware) targeting those devices. In this paper, we build a detection mechanism of IoT malware utilizing control flow graphs (CFGs). To motivate for our detection mechanism, we contrast the underlying characteristics of IoT malware to other types of malware—Android malware, which are also Linux-based—across multiple features. The preliminary analyses reveal that the Android malware have high density, strong closeness and betweenness, and a larger number of nodes. We show that IoT malware samples have a large number of edges despite a smaller number of nodes, which demonstrate a richer flow structure and higher complexity. We utilize those various characterizing features as a modality to build a highly effective deep learning-based detection model to detect IoT malware. To test our model, we use CFGs of about 6000 malware and benign IoT disassembled samples, and show a detection accuracy of $\approx 99.66$ %.