Many applications have security vulnerabilities that can be exploited. It is practically impossible to find all of them due to the NP-complete nature of the testing problem. Security solutions provide defenses against these attacks through continuous application testing, fast-patching of vulnerabilities, automatic deployment of patches, and virtual patching detection techniques deployed in network and endpoint security tools. These techniques are limited by the need to find vulnerabilities before the black-hats. We propose an innovative technique to virtually patch vulnerabilities before they are found. We leverage testing techniques for supervised-learning data generation, and show how artificial intelligence techniques can use this data to create predictive deep neural-network models that read an application's input and predict in real time whether it is a potential malicious input. We set up an ahead-of-threat experiment in which we generated data on old versions of an application, and then evaluated the predictive model accuracy on vulnerabilities found years later. Our experiments show ahead-of-threat detection on LibXML2 and LibTIFF vulnerabilities with 91.3% and 93.7% accuracy, respectively. We expect to continue work on this field of research and provide ahead-of-threat virtual patching for more libraries. Success in this research can change the current state of endless racing after application vulnerabilities and put the defenders one step ahead of the attackers
Compared to cryptography, steganography is a less discussed domain. However, there is a recent trend of exploiting various information hiding techniques to empower malware, for instance to bypass security frameworks of mobile devices or to exfiltrate sensitive data. This is mostly due to the need to counteract increasingly sophisticated security mechanisms, such as code analysis, runtime countermeasures, or real-time traffic inspection tools. In this perspective, this paper presents malware exploiting information hiding in a broad sense, i.e., it does not focus on classical covert channels, bu…
Modern databases and data-warehousing systems separate query processing and durable storage. Storage systems have idiosyncratic bugs and security vulnerabilities, thus attacks that compromise only storage are a realistic threat. In this paper, we show that encryption alone is not sufficient to protect databases from compromised storage. Using MongoDB WiredTiger as a concrete example, we demonstrate that sizes of encrypted writes to a durable write-ahead log can reveal sensitive information about the inputs and activities of MongoDB applications. We then design, implement, and evaluate BigFoot,…
Supply chain is emerging as the next frontier of threats in the rapidly evolving IoT ecosystem. It is fundamentally more complex compared to traditional ICT systems. We analyze supply chain risks in IoT systems and their unique aspects, discuss research challenges in supply chain security, and identify future research directions.
Vessels cybersecurity is recently gaining momentum, as a result of a few recent attacks to vessels at sea. These recent attacks have shacked the maritime domain, which was thought to be relatively immune to cyber threats. The cited belief is now over, as proved by recent mandates issued by the International Maritime Organization (IMO). According to these regulations, all vessels should be the subject of a cybersecurity risk analysis, and technical controls should be adopted to mitigate the resulting risks. This initiative is laudable since, despite the recent incidents, the vulnerabilities and…
To avoid costly security patching after software deployment, security-by-design techniques (e.g., STRIDE threat analysis) are adopted in organizations to root out security issues before the system is ever implemented. Despite the global gap in cybersecurity workforce and the high manual effort required for performing threat analysis, organizations are ramping up threat analysis activities. However, past experimental results were inconclusive regarding some performance indicators of threat analysis techniques thus practitioners have little evidence for choosing the technique to adopt. To addres…
Decentralized Finance (DeFi) took shape in 2020. An unprecedented amount of over 14 billion USD moved into DeFi projects offering trading, loans and insurance. But its growth has also drawn the attention of malicious actors. Many projects were exploited as quickly as they launched and millions of USD were lost. While many developers understand integer overflows and reentrancy attacks, security threats to the DeFi ecosystem are more complex and still poorly understood. In this paper we provide the first overview of in-the-wild DeFi security incidents. We observe that many of these exploits are …
Recently, bug-bounty programs have gained popularity and become a significant part of the security culture of many organizations. Bug-bounty programs enable organizations to enhance their security posture by harnessing the diverse expertise of crowds of external security experts (i.e., bug hunters). Nonetheless, quantifying the benefits of bug-bounty programs remains elusive, which presents a significant challenge for managing them. Previous studies focused on measuring their benefits in terms of the number of vulnerabilities reported or based on the properties of the reported vulnerabilities,…
Università degli Studi di Napoli Federico II（那不勒斯费德里科二世大学）；University of Bern（伯尔尼大学）
Proactive approaches to security, such as adversary emulation, leverage information about threat actors and their techniques (Cyber Threat Intelligence, CTI). However, most CTI still comes in unstructured forms (i.e., natural language), such as incident reports and leaked documents. To support proactive security efforts, we present an experimental study on the automatic classification of unstructured CTI into attack techniques using machine learning (ML). We contribute with two new datasets for CTI analysis, and we evaluate several ML models, including both traditional and deep learning-based …
A precise vulnerability discovery model (VDM) will provide a useful insight to assess software security, and could be a good prediction instrument for both software vendors and users to understand security trends and plan ahead patching schedule accordingly. Thus far, several models have been proposed and validated. Yet, no systematically independent validation by somebody other than the author exists. Furthermore, there are a number of issues that might bias previous studies in the field. In this work, we fill in the gap by introducing an empirical methodology that systematically evaluates th…
Technische Universität Wien (TU Wien)（维也纳工业大学（TU Wien））；University of Cyprus（塞浦路斯大学）；ETH Zürich（苏黎世联邦理工学院）；Hanyang University（汉阳大学）
Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful decision-making capabilities. However, they are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy. These threats get aggravated in emerging edge ML devices that have stringent constraints in terms of resources (e.g., compute, memory, power/energy), and that therefore cannot employ costly security and reliability measures. Security, reliability, and vulnerability mitigation t…
A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors
University of Chittagong（吉大港大学）；Northwestern Polytechnical University（西北工业大学）；Noakhali Science and Technology University（诺阿卡利科技大学）；Chittagong University of Engineering & Technology（吉大港工程技术大学）；Edith Cowan University（伊迪斯科文大学）
本文针对网络风险预测问题，提出一种基于 Pertinent Features 随机森林的机器学习模型，结合问卷数据增强和 Apriori 关联规则分析，在20个特征下实现95.95%的准确率，有效识别网络攻击受害者及关键风险因素。
