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Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems. Given the limited resources in practice, SV assessment and prioritization help practitioners devise optimal SV…
Software, while beneficial, poses potential cybersecurity risks due to inherent vulnerabilities. Detecting these vulnerabilities is crucial, and deep learning has shown promise as an effective tool for this task due to its ability to…
Insider Attack Detection in commercial networks is a critical problem that does not have any good solutions at this current time. The problem is challenging due to the lack of visibility into live networks and a lack of a standard feature…
In order to ensure the quality of software and prevent attacks from hackers on critical systems, static analysis tools are frequently utilized to detect vulnerabilities in the early development phase. However, these tools often report a…
Fuzzing has proven to be very effective for discovering certain classes of software flaws, but less effective in helping developers process these discoveries. Conventional crash-based fuzzers lack enough information about failures to…
Artificial Intelligence-enabled systems are increasingly being deployed in real-world safety-critical settings involving human participants. It is vital to ensure the safety of such systems and stop the evolution of the system with error…
We present the first systematic approach to static and dynamic taint analysis for Graph APIs focusing on broken access control. The approach comprises the following. We taint nodes of the Graph API if they represent data requiring specific…
Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more.…
With the expansion of the power grid and the increase of the proportion of new energy sources, the uncertainty and random factors of the power grid increase, endangering the safe operation of the system. It is particularly important to find…
Many IoT devices are vulnerable to attacks due to flawed security designs and lacking mechanisms for firmware updates or patches to eliminate the security vulnerabilities. Device-type identification combined with data from vulnerability…
This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks. The study was performed on a SecureWater Treatment testbed…
Intrusion detection into computer networks has become one of the most important issues in cybersecurity. Attackers keep on researching and coding to discover new vulnerabilities to penetrate information security system. In consequence…
Off-the-shelf software for Command and Control is often used by attackers and legitimate pentesters looking for discretion. Among other functionalities, these tools facilitate the customization of their network traffic so it can mimic…
Identifying the tasks a given piece of malware was designed to perform (e.g. logging keystrokes, recording video, establishing remote access, etc.) is a difficult and time-consuming operation that is largely human-driven in practice. In…
Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation…
In this paper, we present a novel information processing architecture for safe deep learning-based visual navigation of autonomous systems. The proposed information processing architecture is used to support a perceptual attention-based…
Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades. Traditional code analysis methods have been proposed, but are often ineffective and inefficient. In this work, we model…
The utilization of third-party open-source libraries is widespread in modern software development. Due to the dependency relationships, vulnerabilities within open-source libraries pose significant security threats to downstream software.…
We develop a learning-based control algorithm for unknown dynamical systems under very severe data limitations. Specifically, the algorithm has access to streaming and noisy data only from a single and ongoing trial. It accomplishes such…
As bug-finding methods improve, bug-fixing capabilities are exceeded, resulting in an accumulation of potential vulnerabilities. There is thus a need for efficient and precise bug prioritization based on exploitability. In this work, we…