Biblio

The growing maturity of orchestration languages is contributing to the elaboration of cloud composite services, whose resources may be deployed over different distributed infrastructures. These composite services are subject to changes over time, that are typically required to support cloud properties, such as scalability and rapid elasticity. In particular, the migration of their elementary resources may be triggered by performance constraints. However, changes induced by this migration may introduce vulnerabilities that may compromise the resources, or even the whole cloud service. In that context, we propose an automated SMT1-based security framework for supporting the migration of resources in cloud composite services, and preventing the occurrence of new configuration vulnerabilities. We formalize the underlying security automation based on SMT solving, in order to assess the migrated resources and select adequate counter-measures, considering both endogenous and exogenous security mechanisms. We then evaluate its benefits and limits through large series of experiments based on a proof-of-concept prototype implemented over the CVC4 commonly-used open-source solver. These experiments show a minimal overhead with regular operating systems deployed in cloud environments.

Modern software development frequently uses third-party packages, raising the concern of supply chain security attacks. Many attackers target popular package managers, like npm, and their users with supply chain attacks. In 2021 there was a 650% year-on-year growth in security attacks by exploiting Open Source Software's supply chain. Proactive approaches are needed to predict package vulnerability to high-risk supply chain attacks. The goal of this work is to help software developers and security specialists in measuring npm supply chain weak link signals to prevent future supply chain attacks by empirically studying npm package metadata.

In this paper, we analyzed the metadata of 1.63 million JavaScript npm packages. We propose six signals of security weaknesses in a software supply chain, such as the presence of install scripts, maintainer accounts associated with an expired email domain, and inactive packages with inactive maintainers. One of our case studies identified 11 malicious packages from the install scripts signal. We also found 2,818 maintainer email addresses associated with expired domains, allowing an attacker to hijack 8,494 packages by taking over the npm accounts. We obtained feedback on our weak link signals through a survey responded to by 470 npm package developers. The majority of the developers supported three out of our six proposed weak link signals. The developers also indicated that they would want to be notified about weak links signals before using third-party packages. Additionally, we discussed eight new signals suggested by package developers.

Operating systems have various components that produce artifacts. These artifacts are the outcome of a user’s interaction with an application or program and the operating system’s logging capabilities. Thus, these artifacts have great importance in digital forensics investigations. For example, these artifacts can be utilized in a court of law to prove the existence of compromising computer system behaviors. One such component of the Microsoft Windows operating system is Shellbag, which is an enticing source of digital evidence of high forensics interest. The presence of a Shellbag entry means a specific user has visited a particular folder and done some customizations such as accessing, sorting, resizing the window, etc. In this work, we forensically analyze Shellbag as we talk about its purpose, types, and specificity with the latest version of the Windows 11 operating system and uncover the registry hives that contain Shellbag customization information. We also conduct in-depth forensics examinations on Shellbag entries using three tools of three different types, i.e., open-source, freeware, and proprietary tools. Lastly, we compared the capabilities of tools utilized in Shellbag forensics investigations.

Recently, the Distributed Denial of Service (DDOS) attacks has been used for different aspects to denial the number of services for the end-users. Therefore, there is an urgent need to design an effective detection method against this type of attack. A fuzzy inference system offers the results in a more readable and understandable form. This paper introduces an anomaly-based Intrusion Detection (IDS) system using fuzzy logic. The fuzzy logic inference system implemented as a detection method for Distributed Denial of Service (DDOS) attacks. The suggested method was applied to an open-source DDOS dataset. Experimental results show that the anomaly-based Intrusion Detection system using fuzzy logic obtained the best result by utilizing the InfoGain features selection method besides the fuzzy inference system, the results were 91.1% for the true-positive rate and 0.006% for the false-positive rate.

This demo paper presents the technical details and usage scenarios of μSE: a mutation-based tool for evaluating security-focused static analysis tools for Android. Mutation testing is generally used by software practitioners to assess the robustness of a given test-suite. However, we leverage this technique to systematically evaluate static analysis tools and uncover and document soundness issues.μSE's analysis has found 25 previously undocumented flaws in static data leak detection tools for Android.μSE offers four mutation schemes, namely Reachability, Complex-reachability, TaintSink, and ScopeSink, which determine the locations of seeded mutants. Furthermore, the user can extend μSE by customizing the API calls targeted by the mutation analysis.μSE is also practical, as it makes use of filtering techniques based on compilation and execution criteria that reduces the number of ineffective mutations.

Recommender systems in software engineering provide developers with a wide range of valuable items to help them complete their tasks. Among others, API recommender systems have gained momentum in recent years as they became more successful at suggesting API calls or code snippets. While these systems have proven to be effective in terms of prediction accuracy, there has been less attention for what concerns such recommenders’ resilience against adversarial attempts. In fact, by crafting the recommenders’ learning material, e.g., data from large open-source software (OSS) repositories, hostile users may succeed in injecting malicious data, putting at risk the software clients adopting API recommender systems. In this paper, we present an empirical investigation of adversarial machine learning techniques and their possible influence on recommender systems. The evaluation performed on three state-of-the-art API recommender systems reveals a worrying outcome: all of them are not immune to malicious data. The obtained result triggers the need for effective countermeasures to protect recommender systems against hostile attacks disguised in training data.

IoT honeypots that mimic the behavior of IoT devices for threat analysis are becoming increasingly important. Existing honeypot systems use devices with a specific version of firmware installed to monitor cyber attacks. However, honeypots frequently receive requests targeting devices and firmware that are different from themselves. When honeypots return an error response to such a request, the attack is terminated, and the monitoring fails.To solve this problem, we introduce FirmPot, a framework that automatically generates intelligent-interaction honeypots using firmware. This framework has a firmware emulator optimized for honeypot generation and learns the behavior of embedded applications by using machine learning. The generated honeypots continue to interact with attackers by a mechanism that returns the best from the emulated responses to the attack request instead of an error response.We experimented on embedded web applications of wireless routers based on the open-source OpenWrt. As a result, our framework generated honeypots that mimicked the embedded web applications of eight vendors and ten different CPU architectures. Furthermore, our approach to the interaction improved the session length with attackers compared to existing ones.

The reuse of libraries in software development has become prevalent for improving development efficiency and software quality. However, security vulnerabilities of reused libraries propagated through software project dependency pose a severe security threat, but they have not yet been well studied. In this paper, we present the first large-scale empirical study of project dependencies with respect to security vulnerabilities. We developed PDGraph, an innovative approach for analyzing publicly known security vulnerabilities among numerous project dependencies, which provides a new perspective for assessing security risks in the wild. As a large-scale software collection in dependency, we find 337,415 projects and 1,385,338 dependency relations. In particular, PDGraph generates a project dependency graph, where each node is a project, and each edge indicates a dependency relationship. We conducted experiments to validate the efficacy of PDGraph and characterized its features for security analysis. We revealed that 1,014 projects have publicly disclosed vulnerabilities, and more than 67,806 projects are directly dependent on them. Among these, 42,441 projects still manifest 67,581 insecure dependency relationships, indicating that they are built on vulnerable versions of reused libraries even though their vulnerabilities are publicly known. During our eight-month observation period, only 1,266 insecure edges were fixed, and corresponding vulnerable libraries were updated to secure versions. Furthermore, we uncovered four underlying dependency risks that can significantly reduce the difficulty of compromising systems. We conducted a quantitative analysis of dependency risks on the PDGraph.

In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes. The availability of open-source tools to create DeepFakes poses as a threat to the trustworthiness of the online media. In this work, we develop an open-source online platform, known as DeepFake-o-meter, that integrates state-of-the-art DeepFake detection methods and provide a convenient interface for the users. We describe the design and function of DeepFake-o-meter in this work.

Web technologies are typically built with time constraints and security vulnerabilities. Automatic software vulnerability scanners are common tools for detecting such vulnerabilities among software developers. It helps to illustrate the program for the attacker by creating a great deal of engagement within the program. SQL Injection and Cross-Site Scripting (XSS) are two of the most commonly spread and dangerous vulnerabilities in web apps that cause to the user. It is very important to trust the findings of the site vulnerability scanning software. Without a clear idea of the accuracy and the coverage of the open-source tools, it is difficult to analyze the result from the automatic vulnerability scanner that provides. The important to do a comparison on the key figure on the automated vulnerability scanners because there are many kinds of a scanner on the market and this comparison can be useful to decide which scanner has better performance in term of SQL Injection and Cross-Site Scripting (XSS) vulnerabilities. In this paper, a method by Jose Fonseca et al, is used to compare open-source automated vulnerability scanners based on detection coverage and a method by Yuki Makino and Vitaly Klyuev for precision rate. The criteria vulnerabilities will be injected into the web applications which then be scanned by the scanners. The results then are compared by analyzing the precision rate and detection coverage of vulnerability detection. Two leading open source automated vulnerability scanners will be evaluated. In this paper, the scanner that being utilizes is OW ASP ZAP and Skipfish for comparison. The results show that from precision rate and detection rate scope, OW ASP ZAP has better performance than Skipfish by two times for precision rate and have almost the same result for detection coverage where OW ASP ZAP has a higher number in high vulnerabilities.

Peer code review has been found to be effective in identifying security vulnerabilities. However, despite practicing mandatory code reviews, many Open Source Software (OSS) projects still encounter a large number of post-release security vulnerabilities, as some security defects escape those. Therefore, a project manager may wonder if there was any weakness or inconsistency during a code review that missed a security vulnerability. Answers to this question may help a manager pinpointing areas of concern and taking measures to improve the effectiveness of his/her project's code reviews in identifying security defects. Therefore, this study aims to identify the factors that differentiate code reviews that successfully identified security defects from those that missed such defects. With this goal, we conduct a case-control study of Chromium OS project. Using multi-stage semi-automated approaches, we build a dataset of 516 code reviews that successfully identified security defects and 374 code reviews where security defects escaped. The results of our empirical study suggest that the are significant differences between the categories of security defects that are identified and that are missed during code reviews. A logistic regression model fitted on our dataset achieved an AUC score of 0.91 and has identified nine code review attributes that influence identifications of security defects. While time to complete a review, the number of mutual reviews between two developers, and if the review is for a bug fix have positive impacts on vulnerability identification, opposite effects are observed from the number of directories under review, the number of total reviews by a developer, and the total number of prior commits for the file under review.

Cyber-defense systems are being developed to automatically ingest Cyber Threat Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge graphs. A potential risk is that fake CTI can be generated and spread through Open-Source Intelligence (OSINT) communities or on the Web to effect a data poisoning attack on these systems. Adversaries can use fake CTI examples as training input to subvert cyber defense systems, forcing their models to learn incorrect inputs to serve the attackers' malicious needs. In this paper, we show how to automatically generate fake CTI text descriptions using transformers. Given an initial prompt sentence, a public language model like GPT-2 with fine-tuning can generate plausible CTI text that can mislead cyber-defense systems. We use the generated fake CTI text to perform a data poisoning attack on a Cybersecurity Knowledge Graph (CKG) and a cybersecurity corpus. The attack introduced adverse impacts such as returning incorrect reasoning outputs, representation poisoning, and corruption of other dependent AI-based cyber defense systems. We evaluate with traditional approaches and conduct a human evaluation study with cyber-security professionals and threat hunters. Based on the study, professional threat hunters were equally likely to consider our fake generated CTI and authentic CTI as true.

Internet security is constantly at risk as a result of the fast developing and highly sophisticated exploitation methods. These attacks use numerous media to take advantage of the most vulnerable of Internet users. Phishing, spam calling, unsecure content and other means of intrusion threaten Internet users every day. In order to maintain the security and privacy of sensitive user data, the user must pay for services that include the storage and generation of secure passwords, monitoring internet traffic to discourage navigation to malicious websites, among other services. Some people do not have the money to purchase privacy protection services and others find convoluted euphemisms baked into privacy policies quite confusing. In response to this problem, we developed an Internet security software package, Secure Suite, which we provide as open source and hence free of charge. Users can easily deploy and manage Secure Suite. It is composed of a password manager, a malicious URL detection service, dubbed MalURLNet, a URL extender, data visualization tools, a browser extension to interact with the web app, and utility tools to maintain data integrity. MalURLNet is one of the main components of Secure Suite. It utilizes deep learning and other open-source software to mitigate security threats by identifying malicious URLs. We exhaustively tested our proposed MalURLNet service. Our studies show that MalURLNet outperforms four other well-known URL classifiers in terms of accuracy, loss, precision, recall, and F1-Score.

Performance-influence models can help stakeholders understand how and where configuration options and their interactions influence the performance of a system. With this understanding, stakeholders can debug performance behavior and make deliberate configuration decisions. Current black-box techniques to build such models combine various sampling and learning strategies, resulting in tradeoffs between measurement effort, accuracy, and interpretability. We present Comprex, a white-box approach to build performance-influence models for configurable systems, combining insights of local measurements, dynamic taint analysis to track options in the implementation, compositionality, and compression of the configuration space, without relying on machine learning to extrapolate incomplete samples. Our evaluation on 4 widely-used, open-source projects demonstrates that Comprex builds similarly accurate performance-influence models to the most accurate and expensive black-box approach, but at a reduced cost and with additional benefits from interpretable and local models.

The fourth industrial revolution envisions industry manufacturing systems to be software driven where mundane manufacturing tasks can be automated. As software is perceived as an integral part of this vision, discovering vulnerabilities is of paramount of importance so that manufacturing systems are secure. A categorization of vulnerability discovery strategies can inform practitioners on how to identify undiscovered vulnerabilities in software. Recently researchers have investigated and identified vulnerability discovery strategies used in open source software (OSS) projects. The efficacy of the derived strategy needs to be validated by obtaining feedback from practitioners. Such feedback can be helpful to assess if identified strategies are useful for practitioners and possible directions the derived vulnerability discovery strategies can be improvised. We survey 51 practitioners to assess if four vulnerability discovery strategies: diagnostics, malicious payload construction, misconfiguration, and pernicious execution can be used to identify undiscovered vulnerabilities. Practitioners perceive the strategies to be useful: for example, we observe 88% of the surveyed practitioners to agree that diagnostics could be used to discover vulnerabilities. Our work provides evidence of usefulness for the identified strategies.

Anonymous Zether, proposed by Bünz, Agrawal, Zamani, and Boneh (FC'20), is a private payment design whose wallets demand little bandwidth and need not remain online; this unique property makes it a compelling choice for resource-constrained devices. In this work, we describe an efficient construction of Anonymous Zether. Our protocol features proofs which grow only logarithmically in the size of the "anonymity sets" used, improving upon the linear growth attained by prior efforts. It also features competitive transaction sizes in practice (on the order of 3 kilobytes).Our central tool is a new family of extensions to Groth and Kohlweiss's one-out-of-many proofs (Eurocrypt 2015), which efficiently prove statements about many messages among a list of commitments. These extensions prove knowledge of a secret subset of a public list, and assert that the commitments in the subset satisfy certain properties (expressed as linear equations). Remarkably, our communication remains logarithmic; our computation increases only by a logarithmic multiplicative factor. This technique is likely to be of independent interest.We present an open-source, Ethereum-based implementation of our Anonymous Zether construction.

We showcase the capabilities of V2Verifier, a new open-source software-defined radio (SDR) testbed for vehicle-to-vehicle (V2V) communications security, to expose the strengths and vulnerabilities of current V2V security systems based on the IEEE 1609.2 standard. V2Verifier supports both major V2V technologies and facilitates a broad range of experimentation with upper- and lower-layer attacks using a combination of SDRs and commercial V2V on-board units (OBUs). We demonstrate two separate attacks (jamming and replay) against Dedicated Short Range Communication (DSRC) and Cellular Vehicle-to-Everything (C-V2X) technologies, experimentally quantifying the threat posed by these types of attacks. We also use V2Verifier's open-source implementation to show how the 1609.2 standard can effectively mitigate certain types of attacks (e.g., message replay), facilitating further research into the security of V2V.

Intentional interference such as spoofing is an emerging threat to GPS receivers used in both civilian and defense applications. With the majority of smartphones relying on GPS for positioning and navigation, the vulnerability of these phones to spoofing attacks is an issue of security concern. In this paper, it is demonstrated that is easy to successfully spoof a smartphone using a simplistic spoofing technique. A spoofing signal is generated using open-source signal simulator and transmitted using a low-cost SDR. In view of the tremendously increasing usage of GPS enabled smartphones, it is necessary to develop suitable countermeasures for spoofing. This work carries significance as it would help in understanding the effects of spoofing at various levels of signal processing in the receiver and develop advanced spoofing detection and mitigation techniques.

Code injection attacks, like the one used in the high-profile 2017 Equifax breach, have become increasingly common, now ranking \#1 on OWASP's list of critical web application vulnerabilities. Static analyses for detecting these vulnerabilities can overwhelm developers with false positive reports. Meanwhile, most dynamic analyses rely on detecting vulnerabilities as they occur in the field, which can introduce a high performance overhead in production code. This paper describes a new approach for detecting injection vulnerabilities in applications by harnessing the combined power of human developers' test suites and automated dynamic analysis. Our new approach, Rivulet, monitors the execution of developer-written functional tests in order to detect information flows that may be vulnerable to attack. Then, Rivulet uses a white-box test generation technique to repurpose those functional tests to check if any vulnerable flow could be exploited. When applied to the version of Apache Struts exploited in the 2017 Equifax attack, Rivulet quickly identifies the vulnerability, leveraging only the tests that existed in Struts at that time. We compared Rivulet to the state-of-the-art static vulnerability detector Julia on benchmarks, finding that Rivulet outperformed Julia in both false positives and false negatives. We also used Rivulet to detect new vulnerabilities.

Dynamic Information Flow Tracking (DIFT), also called Dynamic Taint Analysis (DTA), is a technique for tracking the information as it flows through a program's execution. Specifically, some inputs or data get tainted and then these taint marks (tags) propagate usually at the instruction-level. While DIFT has been a fundamental concept in computer and network security for the past decade, it still faces open challenges that impede its widespread application in practice; one of them being the indirect flow propagation dilemma: should the tags involved in an indirect flow, e.g., in a control or address dependency, be propagated? Propagating all these tags, as is done for direct flows, leads to overtainting (all taintable objects become tainted), while not propagating them leads to undertainting (information flow becomes incomplete). In this paper, we analytically model that decisioning problem for indirect flows, by considering various tradeoffs including undertainting versus overtainting, importance of heterogeneous code semantics and context. Towards tackling this problem, we design MITOS, a distributed-optimization algorithm, that: decides about the propagation of indirect flows by properly weighting all these tradeoffs, is of low-complexity, is scalable, is able to flexibly adapt to different application scenarios and security needs of large distributed systems. Additionally, MITOS is applicable to most DIFT systems that consider an arbitrary number of tag types, and introduces the key properties of fairness and tag-balancing to the DIFT field. To demonstrate MITOS's applicability in practice, we implement and evaluate MITOS on top of an open-source DIFT, and we shed light on the open problem. We also perform a case-study scenario with a real in-memory only attack and show that MITOS improves simultaneously (i) system's spatiotemporal overhead (up to 40%), and (ii) system's fingerprint on suspected bytes (up to 167%) compared to traditional DIFT, even though these metrics usually conflict.

In the last couple of years ARM-based servers have been gradually adopted by cloud service providers and utilized in the data centers. Such tendency may provide great business opportunities for various companies in the industry. Hence, the ability to timely track the development trend of the ARM-based server ecosystem (ASE) from technical perspective is of great importance. In this paper the level of development of the ASE is quantitatively assessed based on open-source data analysis. In particular, statistical data is extracted from 42 Linux kernels to analyze the development process of the ASE. Furthermore, an estimate of the development trend of the ASE in the next 10 years is made based on the statistical data. The estimated results provide insight on when the ASE may become as mature as today's x86-based server ecosystem.

Software is frequently deployed with vulnerabilities that may allow hackers to gain access to the system or information, leading to money or reputation losses. Although there are many techniques to detect software vulnerabilities, their effectiveness is far from acceptable, especially in large software projects, as shown by several research works. This Ph.D. aims to study the combination of different techniques to improve the effectiveness of vulnerability detection (increasing the detection rate and decreasing the number of false-positives). Static Code Analysis (SCA) has a good detection rate and is the central technique of this work. However, as SCA reports many false-positives, we will study the combination of various SCA tools and the integration with other detection approaches (e.g., software metrics) to improve vulnerability detection capabilities. We will also study the use of such combination to prioritize the reported vulnerabilities and thus guide the development efforts and fixes in resource-constrained projects.

IoT data marketplaces are being developed to help cities and communities create large scale IoT applications. Such data marketplaces let the IoT device owners sell their data to the application developers. Following this application development model, the application developers need not deploy their own IoT devices when developing IoT applications; instead, they can buy data from a data marketplace. In a marketplace-based IoT application, the application developers are making critical business and operation decisions using the data produced by seller's IoT devices. Under these circumstances, it is crucial to verify and validate the security of IoT devices.In this paper, we assess the security of IoT data marketplaces. In particular, we discuss what kind of vulnerabilities exist in IoT data marketplaces using the well-known STRIDE model, and present a security assessment and certification framework for IoT data marketplaces to help the device owners to examine the security vulnerabilities of their devices. Most importantly, our solution certifies the IoT devices when they connect to the data marketplace, which helps the application developers to make an informed decision when buying and consuming data from a data marketplace. To demonstrate the effectiveness of the proposed approach, we have developed a proof-of-concept using I3 (Intelligent IoT Integrator), which is an open-source IoT data marketplace developed at the University of Southern California, and IoTcube, which is a vulnerability detection toolkit developed by researchers at Korea University. Through this work, we show that it is possible to increase the reliability of a IoT data marketplace while not damaging the convenience of the users.

The location information of a node is one of the essential attributes used in most underwater communication routing algorithms to identify a candidate forwarding node by any of the sources. The exact location information of a node exchanged with its neighbours' in plain text and the absence of node authentication results in some of the attacks such as Sybil attack, Blackhole attack, and Wormhole attack. Moreover, the severe consequence of these attacks is Denial of Service (DoS), poor network performance, reduced network lifetime, etc. This paper proposes an anti-Spoof (a-Spoof) algorithm for mitigating localization and neighbour spoofing attacks in UASN. a-Spoof uses three pre-shared symmetric keys to share the location. Additionally, location integrity provided through the hash function. Further, the performance of a-Spoof demonstrated through its implementation in UnetStack with reference to end-to-end packet delay and the number of hops.

Pushed by market forces, software development has become fast-paced. As a consequence, modern development projects are assembled from 3rd-party components. Security & privacy assurance techniques once designed for large, controlled updates over months or years, must now cope with small, continuous changes taking place within a week, and happening in sub-components that are controlled by third-party developers one might not even know they existed. In this paper, we aim to provide an overview of the current software security approaches and evaluate their appropriateness in the face of the changed nature in software development. Software security assurance could benefit by switching from a process-based to an artefact-based approach. Further, security evaluation might need to be more incremental, automated and decentralized. We believe this can be achieved by supporting mechanisms for lightweight and scalable screenings that are applicable to the entire population of software components albeit there might be a price to pay.