Ahmed Abusnaina

Ahmed Abusnaina, Yizhen Wang, Sunpreet Arora et al.

Toward robust malware detection, we explore the attack surface of existing malware detection systems. We conduct root-cause analyses of the practical binary-level black-box adversarial malware examples. Additionally, we uncover the sensitivity of volatile features within the detection engines and exhibit their exploitability. Highlighting volatile information channels within the software, we introduce three software pre-processing steps to eliminate the attack surface, namely, padding removal, software stripping, and inter-section information resetting. Further, to counter the emerging section injection attacks, we propose a graph-based section-dependent information extraction scheme for software representation. The proposed scheme leverages aggregated information within various sections in the software to enable robust malware detection and mitigate adversarial settings. Our experimental results show that traditional malware detection models are ineffective against adversarial threats. However, the attack surface can be largely reduced by eliminating the volatile information. Therefore, we propose simple-yet-effective methods to mitigate the impacts of binary manipulation attacks. Overall, our graph-based malware detection scheme can accurately detect malware with an area under the curve score of 88.32\% and a score of 88.19% under a combination of binary manipulation attacks, exhibiting the efficiency of our proposed scheme.

Ahmed Abusnaina, Afsah Anwar, Sultan Alshamrani et al.

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. These malicious software 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 the large body of literature on malware analysis and detection research, with many systems emerging constantly, and outperforming their predecessors. In this work, 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.

Hisham Alasmary, Afsah Anwar, Ahmed Abusnaina et al.

The Linux shell is a command-line interpreter that provides users with a command interface to the operating system, allowing them to perform a variety of 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 IoT devices, malware authors can abuse the Linux shell of those devices to propagate infections and launch large-scale attacks, e.g., DDoS. In this work, we provide a first look at shell commands used in Linux-based IoT malware towards 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 dataset of shell commands, including malicious commands extracted from 2,891 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 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).

Sultan Alshamrani, Ahmed Abusnaina, Mohammed Abuhamad et al.

Social media has become an essential part of the daily routines of children and adolescents. Moreover, enormous efforts have been made to ensure the psychological and emotional well-being of young users as well as their safety when interacting with various social media platforms. In this paper, we investigate the exposure of those users to inappropriate comments posted on YouTube videos targeting this demographic. We collected a large-scale dataset of approximately four million records and studied the presence of five age-inappropriate categories and the amount of exposure to each category. Using natural language processing and machine learning techniques, we constructed ensemble classifiers that achieved high accuracy in detecting inappropriate comments. Our results show a large percentage of worrisome comments with inappropriate content: we found 11% of the comments on children's videos to be toxic, highlighting the importance of monitoring comments, particularly on children's platforms.

Afsah Anwar, Ahmed Abusnaina, Songqing Chen et al.

Vulnerability databases are vital sources of information on emergent software security concerns. Security professionals, from system administrators to developers to researchers, heavily depend on these databases to track vulnerabilities and analyze security trends. How reliable and accurate are these databases though? In this paper, we explore this question with the National Vulnerability Database (NVD), the U.S. government's repository of vulnerability information that arguably serves as the industry standard. Through a systematic investigation, we uncover inconsistent or incomplete data in the NVD that can impact its practical uses, affecting information such as the vulnerability publication dates, names of vendors and products affected, vulnerability severity scores, and vulnerability type categorizations. We explore the extent of these discrepancies and identify methods for automated corrections. Finally, we demonstrate the impact that these data issues can pose by comparing analyses using the original and our rectified versions of the NVD. Ultimately, our investigation of the NVD not only produces an improved source of vulnerability information, but also provides important insights and guidance for the security community on the curation and use of such data sources.

Ahmed Abusnaina, Mohammed Abuhamad, Hisham Alasmary et al.

The wide acceptance of Internet of Things (IoT) for both household and industrial applications is accompanied by several security concerns. A major security concern is their probable abuse by adversaries towards their malicious intent. Understanding and analyzing IoT malicious behaviors is crucial, especially with their rapid growth and adoption in wide-range of applications. However, recent studies have shown that machine learning-based approaches are susceptible to adversarial attacks by adding junk codes to the binaries, for example, with an intention to fool those machine learning or deep learning-based detection systems. Realizing the importance of addressing this challenge, this study proposes a malware detection system that is robust to adversarial attacks. To do so, examine the performance of the state-of-the-art methods against adversarial IoT software crafted using the graph embedding and augmentation techniques. In particular, we study the robustness of such methods against two black-box adversarial methods, GEA and SGEA, to generate Adversarial Examples (AEs) with reduced overhead, and keeping their practicality intact. Our comprehensive experimentation with GEA-based AEs show the relation between misclassification and the graph size of the injected sample. Upon optimization and with small perturbation, by use of SGEA, all the IoT malware samples are misclassified as benign. This highlights the vulnerability of current detection systems under adversarial settings. With the landscape of possible adversarial attacks, we then propose DL-FHMC, a fine-grained hierarchical learning approach for malware detection and classification, that is robust to AEs with a capability to detect 88.52% of the malicious AEs.

Mohammed Abuhamad, Ahmed Abusnaina, DaeHun Nyang et al.

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 paper, 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.

Aminollah Khormali, Ahmed Abusnaina, Songqing Chen et al.

Despite many attempts, the state-of-the-art of adversarial machine learning on malware detection systems generally yield unexecutable samples. In this work, we set out to examine the robustness of visualization-based malware detection system against adversarial examples (AEs) that not only are able to fool the model, but also maintain the executability of the original input. As such, we first investigate the application of existing off-the-shelf adversarial attack approaches on malware detection systems through which we found that those approaches do not necessarily maintain the functionality of the original inputs. Therefore, we proposed an approach to generate adversarial examples, COPYCAT, which is specifically designed for malware detection systems considering two main goals; achieving a high misclassification rate and maintaining the executability and functionality of the original input. We designed two main configurations for COPYCAT, namely AE padding and sample injection. While the first configuration results in untargeted misclassification attacks, the sample injection configuration is able to force the model to generate a targeted output, which is highly desirable in the malware attribution setting. We evaluate the performance of COPYCAT through an extensive set of experiments on two malware datasets, and report that we were able to generate adversarial samples that are misclassified at a rate of 98.9% and 96.5% with Windows and IoT binary datasets, respectively, outperforming the misclassification rates in the literature. Most importantly, we report that those AEs were executable unlike AEs generated by off-the-shelf approaches. Our transferability study demonstrates that the generated AEs through our proposed method can be generalized to other models.

Ahmed Abusnaina, Aminollah Khormali, Hisham Alasmary et al.

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.