An official website of the United States government
Abstract In today’s interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have shown great success in fast and accurate detection, classification, and even analysis of such threats. Accordingly, there is a growing body of literature addressing how subfields of AI/ML (e.g., natural language processing (NLP)) are getting leveraged to accurately detect evasive malicious patterns in network traffic. In this paper, we delve into the current advancements in ML-based network traffic classification using image visualization. Through a rigorous experimental methodology, we first explore the process of network traffic to image conversion. Subsequently, we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network traffic. Through the utilization of production-level tools and utilities in realistic experiments, our proposed solution achieves an impressive accuracy rate of 99.48% in detecting fileless malware, which is widely regarded as one of the most elusive classes of malicious software.
more »
« less
Malware Detection and Prevention using Artificial Intelligence Techniques
With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholder’s, particularly, end user’s security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity. We present a detailed review of current malware detection technologies, their shortcomings, and ways to improve efficiency. Our study shows that adopting futuristic approaches for the development of malware detection applications shall provide significant advantages. The comprehension of this synthesis shall help researchers for further research on malware detection and prevention using AI.
more »
« less
- PAR ID:
- 10346931
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Big Data (Big Data)
- Page Range / eLocation ID:
- 5369 to 5377
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)As organizations drastically expand their usage of collaborative systems and multi-user applications during this period of mass remote work, it is crucial to understand and manage the risks that such platforms may introduce. Improperly or carelessly deployed and configured systems hide security threats that can impact not only a single organization, but the whole economy. Cloud-based architecture is used in many collaborative systems, such as audio/video conferencing, collaborative document sharing/editing, distance learning and others. Therefore, it is important to understand that safety risk can be triggered by attacks on remote servers and confidential information might be compromised. In this paper, we present an AI powered application that aims to constantly introspect multiple virtual servers in order to detect malicious activities based on their anomalous behavior. Once the suspicious process(es) detected, the application in real-time notifies system administrator about the potential threat. Developed software is able to detect user space based keyloggers, rootkits, process hiding and other intrusion artifacts via agent-less operation, by operating directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Conducted experiments on more than twenty different types of malicious applications provide evidence of high detection accuracymore » « less
-
Feasible and developmentally appropriate sociotechnical approaches for protecting youth from online risks have become a paramount concern among human-computer interaction research communities. Therefore, we conducted 38 interviews with entrepreneurs, IT professionals, clinicians, educators, and researchers who currently work in the space of youth online safety to understand the different sociotechnical approaches they proposed to keep youth safe online, while overcoming key challenges associated with these approaches. We identified three approaches taken among these stakeholders, which included 1) leveraging artificial intelligence (AI)/machine learning to detect risks, 2) building security/safety tools, and 3) developing new forms of parental control software. The trade-offs between privacy and protection, as well as other tensions among different stakeholders (e.g., tensions toward the big-tech companies) arose as major challenges, followed by the subjective nature of risk, lack of necessary but proprietary data, and costs to develop these technical solutions. To overcome the challenges, solutions such as building centralized and multi-disciplinary collaborations, creating sustainable business plans, prioritizing human-centered approaches, and leveraging state-of-art AI were suggested. Our contribution to the body of literature is providing evidence-based implications for the design of sociotechnical solutions to keep youth safe online.more » « less
-
The unprecedented growth in mobile systems has transformed the way we approach everyday computing. Unfortunately, the emergence of a sophisticated type of malware known as ransomware poses a great threat to consumers of this technology. Traditional research on mobile malware detection has focused on approaches that rely on analyzing bytecode for uncovering malicious apps. However, cybercriminals can bypass such methods by embedding malware directly in native machine code, making traditional methods inadequate. Another challenge that detection solutions face is scalability. The sheer number of malware variants released every year makes it difficult for solutions to efficiently scale their coverage. To address these concerns, this work presents RansomShield, an energy-efficient solution that leverages CNNs to detect ransomware. We evaluate CNN architectures that have been known to perform well on computer vision tasks and examine their suitability for ransomware detection. We show that systematically converting native instructions from Android apps into images using space-filling curve visualization techniques enable CNNs to reliably detect ransomware with high accuracy. We characterize the robustness of this approach across ARM and x86 architectures and demonstrate the effectiveness of this solution across heterogeneous platforms including smartphones and chromebooks. We evaluate the suitability of different models for mobile systems by comparing their energy demands using different platforms. In addition, we present a CNN introspection framework that determines the important features that are needed for ransomware detection. Finally, we evaluate the robustness of this solution against adversarial machine learning (AML) attacks using state-of-the-art Android malware dataset.more » « less
-
Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F -beta, and precision scores.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData52589.2021.9671434
