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Ami, Amit Seal, Kafle, Kaushal, Nadkarni, Adwait, Poshyvanyk, Denys, Moran, Kevin.  2021.  µSE: Mutation-Based Evaluation of Security-Focused Static Analysis Tools for Android. 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). :53—56.
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.
Nguyen, Phuong T., Di Sipio, Claudio, Di Rocco, Juri, Di Penta, Massimiliano, Di Ruscio, Davide.  2021.  Adversarial Attacks to API Recommender Systems: Time to Wake Up and Smell the Coffee? 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). :253—265.
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.
Yamamoto, Moeka, Kakei, Shohei, Saito, Shoichi.  2021.  FirmPot: A Framework for Intelligent-Interaction Honeypots Using Firmware of IoT Devices. 2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW). :405–411.
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.
Li, Qiang, Song, Jinke, Tan, Dawei, Wang, Haining, Liu, Jiqiang.  2021.  PDGraph: A Large-Scale Empirical Study on Project Dependency of Security Vulnerabilities. 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :161–173.
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.
Li, Yuezun, Zhang, Cong, Sun, Pu, Ke, Lipeng, Ju, Yan, Qi, Honggang, Lyu, Siwei.  2021.  DeepFake-o-meter: An Open Platform for DeepFake Detection. 2021 IEEE Security and Privacy Workshops (SPW). :277–281.
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.
Zukran, Busra, Siraj, Maheyzah Md.  2021.  Performance Comparison on SQL Injection and XSS Detection using Open Source Vulnerability Scanners. 2021 International Conference on Data Science and Its Applications (ICoDSA). :61–65.

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.

Paul, Rajshakhar, Turzo, Asif Kamal, Bosu, Amiangshu.  2021.  Why Security Defects Go Unnoticed During Code Reviews? A Case-Control Study of the Chromium OS Project 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). :1373–1385.
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.
Ranade, Priyanka, Piplai, Aritran, Mittal, Sudip, Joshi, Anupam, Finin, Tim.  2021.  Generating Fake Cyber Threat Intelligence Using Transformer-Based Models. 2021 International Joint Conference on Neural Networks (IJCNN). :1–9.
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.
Haney, Oliver, ElAarag, Hala.  2021.  Secure Suite: An Open-Source Service for Internet Security. SoutheastCon 2021. :1—7.
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.
Velez, Miguel, Jamshidi, Pooyan, Siegmund, Norbert, Apel, Sven, Kästner, Christian.  2021.  White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems. 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). :1072–1084.

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.

Bhuiyan, Farzana Ahamed, Murphy, Justin, Morrison, Patrick, Rahman, Akond.  2021.  Practitioner Perception of Vulnerability Discovery Strategies. 2021 IEEE/ACM 2nd International Workshop on Engineering and Cybersecurity of Critical Systems (EnCyCriS). :41—44.
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.
Diamond, Benjamin E..  2021.  Many-out-of-Many Proofs and Applications to Anonymous Zether. 2021 IEEE Symposium on Security and Privacy (SP). :1800–1817.
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.
Twardokus, Geoff, Rahbari, Hanif.  2021.  Evaluating V2V Security on an SDR Testbed. IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :1–3.
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.
Songala, Komal Kumar, Ammana, Supraja Reddy, Ramachandruni, Hari Chandana, Achanta, Dattatreya Sarma.  2020.  Simplistic Spoofing of GPS Enabled Smartphone. 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). :460–463.
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.
Hough, Katherine, Welearegai, Gebrehiwet, Hammer, Christian, Bell, Jonathan.  2020.  Revealing Injection Vulnerabilities by Leveraging Existing Tests. 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). :284–296.
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.
Sapountzis, Nikolaos, Sun, Ruimin, Wei, Xuetao, Jin, Yier, Crandall, Jedidiah, Oliveira, Daniela.  2020.  MITOS: Optimal Decisioning for the Indirect Flow Propagation Dilemma in Dynamic Information Flow Tracking Systems. 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS). :1090–1100.
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.
Wang, Zhuoyao, Guo, Changguo, Fu, Zhipeng, Yang, Shazhou.  2020.  Identifying the Development Trend of ARM-based Server Ecosystem Using Linux Kernels. 2020 IEEE International Conference on Progress in Informatics and Computing (PIC). :284—288.
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.
Pereira, José D’Abruzzo.  2020.  Techniques and Tools for Advanced Software Vulnerability Detection. 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). :123—126.
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.
Na, Yoonjong, Joo, Yejin, Lee, Heejo, Zhao, Xiangchen, Sajan, Kurian Karyakulam, Ramachandran, Gowri, Krishnamachari, Bhaskar.  2020.  Enhancing the Reliability of IoT Data Marketplaces through Security Validation of IoT Devices. 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS). :265—272.
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.
Chandavarkar, B. R., Gadagkar, Akhilraj V..  2020.  Mitigating Localization and Neighbour Spoofing Attacks in Underwater Sensor Networks. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—5.
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.
Pashchenko, Ivan, Scandariato, Riccardo, Sabetta, Antonino, Massacci, Fabio.  2021.  Secure Software Development in the Era of Fluid Multi-party Open Software and Services. 2021 IEEE/ACM 43rd International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER). :91—95.
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.
Angermeir, Florian, Voggenreiter, Markus, Moyón, Fabiola, Mendez, Daniel.  2021.  Enterprise-Driven Open Source Software: A Case Study on Security Automation. 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). :278—287.
Agile and DevOps are widely adopted by the industry. Hence, integrating security activities with industrial practices, such as continuous integration (CI) pipelines, is necessary to detect security flaws and adhere to regulators’ demands early. In this paper, we analyze automated security activities in CI pipelines of enterprise-driven open source software (OSS). This shall allow us, in the long-run, to better understand the extent to which security activities are (or should be) part of automated pipelines. In particular, we mine publicly available OSS repositories and survey a sample of project maintainers to better understand the role that security activities and their related tools play in their CI pipelines. To increase transparency and allow other researchers to replicate our study (and to take different perspectives), we further disclose our research artefacts.Our results indicate that security activities in enterprise-driven OSS projects are scarce and protection coverage is rather low. Only 6.83% of the analyzed 8,243 projects apply security automation in their CI pipelines, even though maintainers consider security to be rather important. This alerts industry to keep the focus on vulnerabilities of 3rd Party software and it opens space for other improvements of practice which we outline in this manuscript.
Zeng, Jingxiang, Nie, Xiaofan, Chen, Liwei, Li, Jinfeng, Du, Gewangzi, Shi, Gang.  2020.  An Efficient Vulnerability Extrapolation Using Similarity of Graph Kernel of PDGs. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1664–1671.
Discovering the potential vulnerabilities in software plays a crucial role in ensuring the security of computer system. This paper proposes a method that can assist security auditors with the analysis of source code. When security auditors identify new vulnerabilities, our method can be adopted to make a list of recommendations that may have the same vulnerabilities for the security auditors. Our method relies on graph representation to automatically extract the mode of PDG(program dependence graph, a structure composed of control dependence and data dependence). Besides, it can be applied to the vulnerability extrapolation scenario, thus reducing the amount of audit code. We worked on an open-source vulnerability test set called Juliet. According to the evaluation results, the clustering effect produced is satisfactory, so that the feature vectors extracted by the Graph2Vec model are applied to labeling and supervised learning indicators are adopted to assess the model for its ability to extract features. On a total of 12,000 small data sets, the training score of the model can reach up to 99.2%, and the test score can reach a maximum of 85.2%. Finally, the recommendation effect of our work is verified as satisfactory.
Đuranec, A., Gruičić, S., Žagar, M..  2020.  Forensic analysis of Windows 10 Sandbox. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :1224—1229.

With each Windows operating system Microsoft introduces new features to its users. Newly added features present a challenge to digital forensics examiners as they are not analyzed or tested enough. One of the latest features, introduced in Windows 10 version 1909 is Windows Sandbox; a lightweight, temporary, environment for running untrusted applications. Because of the temporary nature of the Sandbox and insufficient documentation, digital forensic examiners are facing new challenges when examining this newly added feature which can be used to hide different illegal activities. Throughout this paper, the focus will be on analyzing different Windows artifacts and event logs, with various tools, left behind as a result of the user interaction with the Sandbox feature on a clear virtual environment. Additionally, the setup of testing environment will be explained, the results of testing and interpretation of the findings will be presented, as well as open-source tools used for the analysis.

Herrera, Adrian.  2020.  Optimizing Away JavaScript Obfuscation. 2020 IEEE 20th International Working Conference on Source Code Analysis and Manipulation (SCAM). :215—220.

JavaScript is a popular attack vector for releasing malicious payloads on unsuspecting Internet users. Authors of this malicious JavaScript often employ numerous obfuscation techniques in order to prevent the automatic detection by antivirus and hinder manual analysis by professional malware analysts. Consequently, this paper presents SAFE-DEOBS, a JavaScript deobfuscation tool that we have built. The aim of SAFE-DEOBS is to automatically deobfuscate JavaScript malware such that an analyst can more rapidly determine the malicious script's intent. This is achieved through a number of static analyses, inspired by techniques from compiler theory. We demonstrate the utility of SAFE-DEOBS through a case study on real-world JavaScript malware, and show that it is a useful addition to a malware analyst's toolset.