With the development of automated software testing technology, software developers can get a large number of crash test cases in a short period of time. However, analyzing these crash test cases and finding their root cause is a time-consuming and labor-intensive task. Techniques based on reverse execution and backward taint analysis are proposed to locate the root cause, but can’t provide context information or explanation of the underlying fault. To address these two limitations, researchers have proposed an automated root cause analysis technique called AURORA. Although this technique provides powerful root cause analysis capabilities, it also have two obvious shortcomings. First, the results of root cause analysis are not accurate enough. Second, the efficiency of root cause analysis is not high enough. In order to improve these two shortcomings, we propose OptRCA, a more efficient and accurate approach for root cause analysis and explanation. Like AURORA's fuzzing strategy, OptRCA is also designed based on AFL's crash mode. The difference between them is mainly reflected in three points. First of all, the goal pursued by OptRCA is different from that of normal fuzzing technology. OptRCA pursues maximum correlation to ensure that as many crash test cases as possible are related to the same root cause. This test case with maximum correlation can greatly improve the accuracy of root cause analysis. Second, OptRCA proposed a more efficient non-crash test case retention strategy, which we named “Hill Climbing Retention”. Using the hill climbing retention method, OptRCA can obtain sufficient root cause information while retaining only a few non-crash test cases. Since the number of test cases is greatly reduced, the efficiency of OptRCA's subsequent root cause analysis process is also greatly improved. In addition, OptRCA also optimizes the analysis formula to obtain more accurate analysis results. In the evaluation experimental results, OptRCA is significantly better than AURORA in terms of accuracy and efficiency. Quantitative analysis shows that OptRCA is 65% more accurate and 61% more efficient than AURORA.

Web services have brought great convenience to our daily lives. Meanwhile, they are vulnerable to Denial-of-Service (DoS) attacks. DoS attacks launched via vulnerabilities in the services can cause great harm. The vulnerabilities in protocol implementations are especially important because they are the keystones of web services. One vulnerable protocol implementation can affect all the web services built on top of it. Compared to the vulnerabilities that cause the target service to crash, resource exhaustion vulnerabilities are equally if not more important. This is because such vulnerabilities can deplete the system resources, leading to the unavailability of not only the vulnerable service but also other services running on the same machine. Despite the significance of this type of vulnerability, there has been limited research in this area. In this paper, we propose Medusa, a dynamic analysis framework to detect memory exhaustion vulnerabilities in protocol implementations, which are the most common type of resource exhaustion vulnerabilities. Medusa works in two phases: exploration phase and verification. In the exploration phase, a protocol property graph (PPG) is constructed to embed the states with relevant properties including memory consumption information. In the verification phase, the PPG is used to simulate DoS attacks to verify the vulnerabilities. We implemented Medusa and evaluated its performance on 21 implementations of five protocols. The results demonstrate that Medusa outperforms the state-of-the-art techniques by discovering overall 127× maximum memory consumption. Lastly, Medusa has discovered six 0-day vulnerabilities in six protocol implementations for three protocols. Particularly, one of the vulnerabilities was found in Eclipse Mosquitto, which can affect thousands of services and it has been assigned with a CVE ID.

The automotive industry is drastically moving towards autonomous. This trend constitutes in a fundamental change of going from mechanical and electrical engineering towards software-driven approaches. Modern vehicles can embed more than hundred electronic control units (ECUs). As autonomous vehicles require more intelligence as well as more computing power, high-performance computers (HPCs) bring the data management capabilities for cloud and IoT services to support the transition to a service-oriented vehicle system architecture. With this growing reliance on software in vehicles, software reliability and trustworthiness are increasingly critical to vehicle security. Measuring security trustworthiness in automotive software is even more valuable as cybersecurity is shifting to the left, i.e. in the early phase of development and design process. In this article, we propose a novel method for evaluating security trustworthiness of automotive software by leveraging a computational trust model. The method consists of selecting different domains contributing to software security, calculating their respective expectation value (trustworthiness score) and combining it using operators from the computational trust model. We evaluate the method using an automotive use case, i.e. over-the-air (OTA) update software. We describe a possible integration of the proposed method into a solution which would be valuable for cybersecurity stakeholders, e.g. cybersecurity managers, cybersecurity architects and software quality managers, aiming to monitor security health of automotive software throughout its development life cycle.

Fuzz drivers are essential for fuzzing library APIs. However, manually composing fuzz drivers is difficult and time-consuming. Therefore, several works have been proposed to generate fuzz drivers automatically. Although these works can learn correct API usage from the consumer programs of the target library, three challenges still hinder the quality of the generated fuzz drivers: 1) How to learn and utilize the control dependencies in API usage; 2) How to handle the noises of the learned API usage, especially for complex real-world consumer programs; 3) How to organize independent sets of API usage inside the fuzz driver to better coordinate with fuzzers. To solve these challenges, we propose RUBICK, an automata-guided control-flow-sensitive fuzz driver generation technique. RUBICK has three key features: 1) it models the API usage (including API data and control dependencies) as a deterministic finite automaton; 2) it leverages active automata learning algorithm to distill the learned API usage; 3) it synthesizes a single automata-guided fuzz driver, which provides scheduling interface for the fuzzer to test independent sets of API usage during fuzzing. During the experiments, the fuzz drivers generated by RUBICK showed a significant performance advantage over the baselines by covering an average of 50.42% more edges than fuzz drivers generated by FUZZGEN and 44.58% more edges than manually written fuzz drivers from OSS-Fuzz or human experts. By learning from large-scale open source projects, RUBICK has generated fuzz drivers for 11 popular Java projects and two of them have been merged into OSS-Fuzz. So far, 199 bugs, including four CVEs, are found using these fuzz drivers, which can affect popular PC and Android software with dozens of millions of downloads.

Flush-based cache attacks seriously threaten the security of automotive system based on ARM Cortex-A9 MPCore. Most of the proposed defense schemes have limited detection capabilities or can’t detect the malicious attacks fast enough. The method of permanently reducing the resolution of all time APIs in the system is not feasible because the high resolution time API is required for normal running of various applications. In this paper, we propose two more secure collaborative APIs—SafeFlush and SafeTime. In addition to the basic function of flushing a cache line, SafeFlush can also detect and handle the suspected flush-based cache attack process. More importantly, SafeFlush collaborates with SafeTime to effectively resist all flush-based cache attacks. That SafeTime adaptively reduces its resolution for a short time based on the signal sent by SafeFlush makes attacks fail. The attack experiment results show that the success rate of Flush+Reload and flush-based Spectre attacks using SafeFlush and SafeTime APIs can be reduced to less than 1%. Performance experiment results show that the access latency of SafeTime based on global timer is 14.5% slower than the original API and 18% slower based on PMCCNTR. The time consumption of SafeFlush is about 25.2% longer than the original cache flush API. Since SafeFlush and SafeTime are far more secure than the original APIs, their performance loss is acceptable.

In this article, we investigate a fundamental question regarding software security: Is the security of SW releases increasing over time? We approach this question with a detailed analysis of the large body of open-source software packaged in the popular Debian GNU/Linux distribution. Contrary to common intuition, we find no clear evidence that the vulnerability rate of widely used software decreases over time: Even in popular and “stable” releases, the fixing of bugs does not seem to reduce the rate of newly identified vulnerabilities. The intuitive conclusion is worrisome: Commonly employed development and validation procedures do not seem to scale with the increase of features and complexity—they are only chopping pieces off the top of an iceberg of vulnerabilities. To the best of our knowledge, this is the first investigation into the problem that studies a complete distribution of software, spanning multiple versions. Although we can not give a definitive answer, we show that several popular beliefs also cannot be confirmed given our dataset. We publish our Debian Vulnerability Analysis Framework (DVAF) , an automated dataset creation and analysis process, to enable reproduction and further analysis of our results. Overall, we hope our contributions provide important insights into the vulnerability discovery process and help in identifying effective techniques for vulnerability analysis and prevention.

In this article, we investigate a fundamental question regarding software security: Is the security of SW releases increasing over time? We approach this question with a detailed analysis of the large body of open-source software packaged in the popular Debian GNU/Linux distribution. Contrary to common intuition, we find no clear evidence that the vulnerability rate of widely used software decreases over time: Even in popular and “stable” releases, the fixing of bugs does not seem to reduce the rate of newly identified vulnerabilities. The intuitive conclusion is worrisome: Commonly employed development and validation procedures do not seem to scale with the increase of features and complexity—they are only chopping pieces off the top of an iceberg of vulnerabilities. To the best of our knowledge, this is the first investigation into the problem that studies a complete distribution of software, spanning multiple versions. Although we can not give a definitive answer, we show that several popular beliefs also cannot be confirmed given our dataset. We publish our Debian Vulnerability Analysis Framework (DVAF), an automated dataset creation and analysis process, to enable reproduction and further analysis of our results. Overall, we hope our contributions provide important insights into the vulnerability discovery process and help in identifying effective techniques for vulnerability analysis and prevention.

In this article, we investigate a fundamental question regarding software security: Is the security of SW releases increasing over time? We approach this question with a detailed analysis of the large body of open-source software packaged in the popular Debian GNU/Linux distribution. Contrary to common intuition, we find no clear evidence that the vulnerability rate of widely used software decreases over time: Even in popular and “stable” releases, the fixing of bugs does not seem to reduce the rate of newly identified vulnerabilities. The intuitive conclusion is worrisome: Commonly employed development and validation procedures do not seem to scale with the increase of features and complexity—they are only chopping pieces off the top of an iceberg of vulnerabilities. To the best of our knowledge, this is the first investigation into the problem that studies a complete distribution of software, spanning multiple versions. Although we can not give a definitive answer, we show that several popular beliefs also cannot be confirmed given our dataset. We publish our Debian Vulnerability Analysis Framework (DVAF), an automated dataset creation and analysis process, to enable reproduction and further analysis of our results. Overall, we hope our contributions provide important insights into the vulnerability discovery process and help in identifying effective techniques for vulnerability analysis and prevention.

In this article, we investigate a fundamental question regarding software security: Is the security of SW releases increasing over time? We approach this question with a detailed analysis of the large body of open-source software packaged in the popular Debian GNU/Linux distribution. Contrary to common intuition, we find no clear evidence that the vulnerability rate of widely used software decreases over time: Even in popular and "stable" releases, the fixing of bugs does not seem to reduce the rate of newly identified vulnerabilities. The intuitive conclusion is worrisome: Commonly employed development and validation procedures do not seem to scale with the increase of features and complexity they are only chopping pieces off the top of an iceberg of vulnerabilities. To the best of our knowledge, this is the first investigation into the problem that studies a complete distribution of software, spanning multiple versions. Although we can not give a definitive answer, we show that several popular beliefs also cannot be confirmed given our dataset. We publish our Debian Vulnerability Analysis Framework (DVAF), an automated dataset creation and analysis process, to enable reproduction and further analysis of our results. Overall, we hope our contributions provide important insights into the vulnerability discovery process and help in identifying effective techniques for vulnerability analysis and prevention.

In this article, we investigate a fundamental question regarding software security: Is the security of SW releases increasing over time? We approach this question with a detailed analysis of the large body of open-source software packaged in the popular Debian GNU/Linux distribution. Contrary to common intuition, we find no clear evidence that the vulnerability rate of widely used software decreases over time: Even in popular and “stable” releases, the fixing of bugs does not seem to reduce the rate of newly identified vulnerabilities. The intuitive conclusion is worrisome: Commonly employed development and validation procedures do not seem to scale with the increase of features and complexity—they are only chopping pieces off the top of an iceberg of vulnerabilities. To the best of our knowledge, this is the first investigation into the problem that studies a complete distribution of software, spanning multiple versions. Although we can not give a definitive answer, we show that several popular beliefs also cannot be confirmed given our dataset. We publish our Debian Vulnerability Analysis Framework (DVAF), an automated dataset creation and analysis process, to enable reproduction and further analysis of our results. Overall, we hope our contributions provide important insights into the vulnerability discovery process and help in identifying effective techniques for vulnerability analysis and prevention.

The devastating effects of cyber-attacks, highlight the need for novel attack detection and prevention techniques. Over the last years, considerable work has been done in the areas of attack detection as well as in collaborative defense. However, an analysis of the state of the art suggests that many challenges exist in prioritizing alert data and in studying the relation between a recently discovered attack and the probability of it occurring again. In this article, we propose a system that is intended for characterizing network entities and the likelihood that they will behave maliciously in the future. Our system, namely Network Entity Reputation Database System (NERDS), takes into account all the available information regarding a network entity (e. g. IP address) to calculate the probability that it will act maliciously. The latter part is achieved via the utilization of machine learning. Our experimental results show that it is indeed possible to precisely estimate the probability of future attacks from each entity using information about its previous malicious behavior and other characteristics. Ranking the entities by this probability has practical applications in alert prioritization, assembly of highly effective blacklists of a limited length and other use cases.

In this work in progress paper, we present a description of a new view of privacy in public, examining how it is possible to ascertain the privacy levels of individuals in context and in groups, and different ways of visualising these Public Privacy levels. We examine how awareness of one’s Public Privacy may have an impact on behaviour and privacy protection in general, and propose future work to examine the concept in more detail.

Despite years of intensive research in the field of software vulnerabilities discovery, exploits are becoming ever more common. Consequently, it is more necessary than ever to choose software configurations that minimize systems' exposure surface to these threats. In order to support users in assessing the security risks induced by their software configurations and in making informed decisions, we introduce M-STAR, a Modular Software Trustworthiness ARchitecture and framework for probabilistically assessing the trustworthiness of software systems, based on evidence, such as their vulnerability history and source code properties. Integral to M-STAR is a software trustworthiness model, consistent with the concept of computational trust. Computational trust models are rooted in Bayesian probability and Dempster-Shafer Belief theory, offering mathematical soundness and expressiveness to our framework. To evaluate our framework, we instantiate M-STAR for Debian Linux packages, and investigate real-world deployment scenarios. In our experiments with real-world data, M-STAR could assess the relative trustworthiness of complete software configurations with an error of less than 10%. Due to its modular design, our proposed framework is agile, as it can incorporate future advances in the field of code analysis and vulnerability prediction. Our results point out that M-STAR can be a valuable tool for system administrators, regular users and developers, helping them assess and manage risks associated with their software configurations.

Authorization, and more generally Trust Management (TM), is an indispensable part of the correct operation of most IT systems. The advent of the Internet of Things (IoT), with its cyber-physical and distributed nature, creates new challenges, that existing TM systems cannot adequately address, such as for example the need for non-interactive exclusive access enforcement. In the meantime, a line of thought in the research community is that Distributed Ledgers (DLs), like the one implemented by the Ethereum blockchain, can provide strong security guarantees for distributed access control. However, this approach has not yet been examined in a scientific, systematic manner, and has many pitfalls, with arguably the most important one being scalability. In this paper, we critically explore the shortcomings of existing solutions for trust management in distributed networks, pinpoint which of these shortcomings can be addressed by utilizing DLs, and offer a conceptual design for a scalable, secure TM system. Our design approaches the problem in three layers, namely a global, an intermediate group or shard layer, and a local layer, corresponding to the set of embedded devices behind an internet access point. We view our design as a novel first step, helping the community to produce more secure and realistic authorization solutions for the IoT, in the near future.

Smartphones have become ubiquitous in our everyday lives, providing diverse functionalities via millions of applications (apps) that are readily available. To achieve these functionalities, apps need to access and utilize potentially sensitive data, stored in the user's device. This can pose a serious threat to users' security and privacy, when considering malicious or underskilled developers. While application marketplaces, like Google Play store and Apple App store, provide factors like ratings, user reviews, and number of downloads to distinguish benign from risky apps, studies have shown that these metrics are not adequately effective. The security and privacy health of an application should also be considered to generate a more reliable and transparent trustworthiness score. In order to automate the trustworthiness assessment of mobile applications, we introduce the Trust4App framework, which not only considers the publicly available factors mentioned above, but also takes into account the Security and Privacy (S&P) health of an application. Additionally, it considers the S&P posture of a user, and provides an holistic personalized trustworthiness score. While existing automatic trustworthiness frameworks only consider trustworthiness indicators (e.g. permission usage, privacy leaks) individually, Trust4App is, to the best of our knowledge, the first framework to combine these indicators. We also implement a proof-of-concept realization of our framework and demonstrate that Trust4App provides a more comprehensive, intuitive and actionable trustworthiness assessment compared to existing approaches.

The destructive effects of cyber-attacks demand more proactive security approaches. One such promising approach is the idea of collaborative intrusion detection systems (CIDS s). These systems combine the knowledge of multiple sensors (e.g., intrusion detection systems, honeypots, or firewalls) to create a holistic picture of a monitored network. Sensors monitor parts of a network and exchange alert data to learn from each other, improve their detection capabilities and ultimately identify sophisticated attacks. Nevertheless, if one or a group of sensors is unreliable (due to incompetence or malice), the system might miss important information needed to detect attacks. In this paper, we propose Sphinx, an evidence-based trust mechanism capable of detecting unreliable sensors within a CIDS. The Sphinx detects, both, single sensors or coalitions of dishonest sensors that lie about the reliability of others to boost or worsen their trust score. Our evaluation shows that, given an honest majority of sensors, dishonesty is punished in a timely manner. Moreover, if several coalitions exist, even when more than 50% of all sensors are dishonest, dishonesty is punished.

Collaborative Intrusion Detection Systems (CIDSs) are an emerging field in cyber-security. In such an approach, multiple sensors collaborate by exchanging alert data with the goal of generating a complete picture of the monitored network. This can provide significant improvements in intrusion detection and especially in the identification of sophisticated attacks. However, the challenge of deciding to which extend a sensor can trust others, has not yet been holistically addressed in related work. In this paper, we firstly propose a set of requirements for reliable trust management in CIDSs. Afterwards, we carefully investigate the most dominant CIDS trust schemes. The main contribution of the paper is mapping the results of the analysis to the aforementioned requirements, along with a comparison of the state of the art. Furthermore, this paper identifies and discusses the research gaps and challenges with regard to trust and CIDSs.

In distributed storage systems, documents are shared among multiple Cloud providers and stored within their respective storage servers. In social secret sharing-based distributed storage systems, shares of the documents are allocated according to the trustworthiness of the storage servers. This paper proposes a trust mechanism using machine learning techniques to compute evidence-based trust values. Our mechanism mitigates the effect of colluding storage servers. More precisely, it becomes possible to detect unreliable evidence and establish countermeasures in order to discourage the collusion of storage servers. Furthermore, this trust mechanism is applied to the social secret sharing protocol AS^3, showing that this new evidence-based trust mechanism enhances the protection of the stored documents.

Trust Management (TM) systems for authentication are vital to the security of online interactions, which are ubiquitous in our everyday lives. Various systems, like the Web PKI (X.509) and PGP's Web of Trust are used to manage trust in this setting. In recent years, blockchain technology has been introduced as a panacea to our security problems, including that of authentication, without sufficient reasoning, as to its merits.In this work, we investigate the merits of using open distributed ledgers (ODLs), such as the one implemented by blockchain technology, for securing TM systems for authentication. We formally model such systems, and explore how blockchain can help mitigate attacks against them. After formal argumentation, we conclude that in the context of Trust Management for authentication, blockchain technology, and ODLs in general, can offer considerable advantages compared to previous approaches. Our analysis is, to the best of our knowledge, the first to formally model and argue about the security of TM systems for authentication, based on blockchain technology. To achieve this result, we first provide an abstract model for TM systems for authentication. Then, we show how this model can be conceptually encoded in a blockchain, by expressing it as a series of state transitions. As a next step, we examine five prevalent attacks on TM systems, and provide evidence that blockchain-based solutions can be beneficial to the security of such systems, by mitigating, or completely negating such attacks.

Distributed storage allows to outsource a document to the cloud such that multiple users can easily access the file. The protection of the document stored relies on secret sharing, which generates and distributes shares of the document to the storage servers. However, the users have to trust that a certain amount of storage servers behaves honestly and do not lose (retrievability) or reveal (confidentiality) the document. To address this so called social secret sharing schemes were developed that allow to adjust the distribution of shares according to the experience made with the involved storage servers. In this work, we provide a framework called AS(3) that allows to build social secret sharing schemes based on dynamic secret sharing. The resulting protocol has more freedom in adjusting the parameters of the shares distribution and therefore leads to more efficient and accurate solutions as well as an optimal storage consumption. Furthermore, we provide measures to detect and to prevent that the document is lost or accidentally revealed to individual storage servers. We also demonstrate how to compute trust values for storage servers, how to initialize trust values for newcomers, and provide a proof of concept implementation.