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Sachidananda, Vinay, Bhairav, Suhas, Ghosh, Nirnay, Elovici, Yuval.  2019.  PIT: A Probe Into Internet of Things by Comprehensive Security Analysis. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :522–529.
One of the major issues which are hindering widespread and seamless adoption of Internet of Thing (IoT) is security. The IoT devices are vulnerable and susceptible to attacks which became evident from a series of recent large-scale distributed denial-of-service (DDoS) attacks, leading to substantial business and financial losses. Furthermore, in order to find vulnerabilities in IoT, there is a lack of comprehensive security analysis framework. In this paper, we present a modular, adaptable and tunable framework, called PIT, to probe IoT systems at different layers of design and implementation. PIT consists of several security analysis engines, viz., penetration testing, fuzzing, static analysis, and dynamic analysis and an exploitation engine to discover multiple IoT vulnerabilities, respectively. We also develop a novel grey-box fuzzer, called Applica, as a part of the fuzzing engine to overcome the limitations of the present day fuzzers. The proposed framework has been evaluated on a real-world IoT testbed comprising of the state-of-the-art devices. We discovered several network and system-level vulnerabilities such as Buffer Overflow, Denial-of-Service, SQL Injection, etc., and successfully exploited them to demonstrate the presence of security loopholes in the IoT devices.
Ruohonen, Jukka, Hjerppe, Kalle, Rindell, Kalle.  2021.  A Large-Scale Security-Oriented Static Analysis of Python Packages in PyPI. 2021 18th International Conference on Privacy, Security and Trust (PST). :1—10.
Different security issues are a common problem for open source packages archived to and delivered through software ecosystems. These often manifest themselves as software weaknesses that may lead to concrete software vulnerabilities. This paper examines various security issues in Python packages with static analysis. The dataset is based on a snapshot of all packages stored to the Python Package Index (PyPI). In total, over 197 thousand packages and over 749 thousand security issues are covered. Even under the constraints imposed by static analysis, (a) the results indicate prevalence of security issues; at least one issue is present for about 46% of the Python packages. In terms of the issue types, (b) exception handling and different code injections have been the most common issues. The subprocess module stands out in this regard. Reflecting the generally small size of the packages, (c) software size metrics do not predict well the amount of issues revealed through static analysis. With these results and the accompanying discussion, the paper contributes to the field of large-scale empirical studies for better understanding security problems in software ecosystems.
Imtiaz, Sayem Mohammad, Sultana, Kazi Zakia, Varde, Aparna S..  2021.  Mining Learner-friendly Security Patterns from Huge Published Histories of Software Applications for an Intelligent Tutoring System in Secure Coding. 2021 IEEE International Conference on Big Data (Big Data). :4869–4876.

Security patterns are proven solutions to recurring problems in software development. The growing importance of secure software development has introduced diverse research efforts on security patterns that mostly focused on classification schemes, evolution and evaluation of the patterns. Despite a huge mature history of research and popularity among researchers, security patterns have not fully penetrated software development practices. Besides, software security education has not been benefited by these patterns though a commonly stated motivation is the dissemination of expert knowledge and experience. This is because the patterns lack a simple embodiment to help students learn about vulnerable code, and to guide new developers on secure coding. In order to address this problem, we propose to conduct intelligent data mining in the context of software engineering to discover learner-friendly software security patterns. Our proposed model entails knowledge discovery from large scale published real-world vulnerability histories in software applications. We harness association rule mining for frequent pattern discovery to mine easily comprehensible and explainable learner-friendly rules, mainly of the type "flaw implies fix" and "attack type implies flaw", so as to enhance training in secure coding which in turn would augment secure software development. We propose to build a learner-friendly intelligent tutoring system (ITS) based on the newly discovered security patterns and rules explored. We present our proposed model based on association rule mining in secure software development with the goal of building this ITS. Our proposed model and prototype experiments are discussed in this paper along with challenges and ongoing work.

Ahakonye, Love Allen Chijioke, Amaizu, Gabriel Chukwunonso, Nwakanma, Cosmas Ifeanyi, Lee, Jae Min, Kim, Dong-Seong.  2021.  Enhanced Vulnerability Detection in SCADA Systems using Hyper-Parameter-Tuned Ensemble Learning. 2021 International Conference on Information and Communication Technology Convergence (ICTC). :458–461.
The growth of inter-dependency intricacies of Supervisory Control and Data Acquisition (SCADA) systems in industrial operations generates a likelihood of increased vulnerability to malicious threats and machine learning approaches have been extensively utilized in the research for vulnerability detection. Nonetheless, to improve security, an enhanced vulnerability detection using hyper-parameter-tune machine learning is proposed for early detection, classification and mitigation of SCADA communication and transmission networks by classifying benign, or malicious DNS attacks. The proposed scheme, an ensemble optimizer (GentleBoost) upon hyper-parameter tuning, gave a comparative achievement. From the simulation results, the proposed scheme had an outstanding performance within the shortest possible time with an accuracy of 99.49%, 99.23% for precision, and a recall rate of 99.75%. Also, the model was compared to other contemporary algorithms and outperformed all the other algorithms proving to be an approach to keep abreast of the SCADA network vulnerabilities and attacks.
Hassell, Suzanne, Beraud, Paul, Cruz, Alen, Ganga, Gangadhar, Martin, Steve, Toennies, Justin, Vazquez, Pablo, Wright, Gary, Gomez, Daniel, Pietryka, Frank et al..  2012.  Evaluating network cyber resiliency methods using cyber threat, Vulnerability and Defense Modeling and Simulation. MILCOM 2012 - 2012 IEEE Military Communications Conference. :1—6.
This paper describes a Cyber Threat, Vulnerability and Defense Modeling and Simulation tool kit used for evaluation of systems and networks to improve cyber resiliency. This capability is used to help increase the resiliency of networks at various stages of their lifecycle, from initial design and architecture through the operation of deployed systems and networks. Resiliency of computer systems and networks to cyber threats is facilitated by the modeling of agile and resilient defenses versus threats and running multiple simulations evaluated against resiliency metrics. This helps network designers, cyber analysts and Security Operations Center personnel to perform trades using what-if scenarios to select resiliency capabilities and optimally design and configure cyber resiliency capabilities for their systems and networks.
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.
Nair, Kishor Krishnan, Nair, Harikrishnan Damodaran.  2021.  Security Considerations in the Internet of Things Protocol Stack. 2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). :1–6.
Internet of Things (IoT) wireless devices has the capability to interconnect small footprint devices and its key purpose is to have seamless connection without operational barriers. It is built upon a three-layer (Perception, Transportation and Application) protocol stack architecture. A multitude of security principles must be imposed at each layer for the proper and efficient working of various IoT applications. In the forthcoming years, it is anticipated that IoT devices will be omnipresent, bringing several benefits. The intrinsic security issues in conjunction with the resource constraints in IoT devices enables the proliferation of security vulnerabilities. The absence of specifically designed IoT frameworks, specifications, and interoperability issues further exacerbate the challenges in the IoT arena. This paper conducts an investigation in IoT wireless security with a focus on the major security challenges and considerations from an IoT protocol stack perspective. The vulnerabilities in the IoT protocol stack are laid out along with a gap analysis, evaluation, and the discussion on countermeasures. At the end of this work, critical issues are highlighted with the aim of pointing towards future research directions and drawing conclusions out of it.
Zhou, Andy, Sultana, Kazi Zakia, Samanthula, Bharath K..  2021.  Investigating the Changes in Software Metrics after Vulnerability Is Fixed. 2021 IEEE International Conference on Big Data (Big Data). :5658–5663.
Preventing software vulnerabilities while writing code is one of the most effective ways for avoiding cyber attacks on any developed system. Although developers follow some standard guiding principles for ensuring secure code, the code can still have security bottlenecks and be compromised by an attacker. Therefore, assessing software security while developing code can help developers in writing vulnerability free code. Researchers have already focused on metrics-based and text mining based software vulnerability prediction models. The metrics based models showed higher precision in predicting vulnerabilities although the recall rate is low. In addition, current research did not investigate the impact of individual software metric on the occurrences of vulnerabilities. The main objective of this paper is to track the changes in every software metric after the developer fixes a particular vulnerability. The results of our research will potentially motivate further research on building more accurate vulnerability prediction models based on the appropriate software metrics. In particular, we have compared a total of 250 files from Apache Tomcat and Apache CXF. These files were extracted from the Apache database and were chosen because Apache released these files as vulnerable in their publicly available security advisories. Using a static analysis tool, metrics of the targeted vulnerable files and relevant fixed files (files where vulnerable code is removed by the developers) were extracted and compared. We show that eight of the 40 metrics have an average increase of 2% from vulnerable to fixed files. These metrics include CountDeclClass, CountDeclClassMethod, CountDeclClassVariable, CountDeclInstanceVariable, CountDeclMethodDefault, CountLineCode, MaxCyclomaticStrict, MaxNesting. This study will help developers to assess software security through utilizing software metrics in secure coding practices.
Squarcina, Marco, Calzavara, Stefano, Maffei, Matteo.  2021.  The Remote on the Local: Exacerbating Web Attacks Via Service Workers Caches. 2021 IEEE Security and Privacy Workshops (SPW). :432—443.
Service workers boost the user experience of modern web applications by taking advantage of the Cache API to improve responsiveness and support offline usage. In this paper, we present the first security analysis of the threats posed by this programming practice, identifying an attack with major security implications. In particular, we show how a traditional XSS attack can abuse the Cache API to escalate into a personin-the-middle attack against cached content, thus compromising its confidentiality and integrity. Remarkably, this attack enables new threats which are beyond the scope of traditional XSS. After defining the attack, we study its prevalence in the wild, finding that the large majority of the sites which register service workers using the Cache API are vulnerable as long as a single webpage in the same origin of the service worker is affected by an XSS. Finally, we propose a browser-side countermeasure against this attack, and we analyze its effectiveness and practicality in terms of security benefits and backward compatibility with existing web applications.
Squarcina, Marco, Calzavara, Stefano, Maffei, Matteo.  2021.  The Remote on the Local: Exacerbating Web Attacks Via Service Workers Caches. 2021 IEEE Security and Privacy Workshops (SPW). :432—443.
Service workers boost the user experience of modern web applications by taking advantage of the Cache API to improve responsiveness and support offline usage. In this paper, we present the first security analysis of the threats posed by this programming practice, identifying an attack with major security implications. In particular, we show how a traditional XSS attack can abuse the Cache API to escalate into a personin-the-middle attack against cached content, thus compromising its confidentiality and integrity. Remarkably, this attack enables new threats which are beyond the scope of traditional XSS. After defining the attack, we study its prevalence in the wild, finding that the large majority of the sites which register service workers using the Cache API are vulnerable as long as a single webpage in the same origin of the service worker is affected by an XSS. Finally, we propose a browser-side countermeasure against this attack, and we analyze its effectiveness and practicality in terms of security benefits and backward compatibility with existing web applications.
Tewari, Naveen, Datt, Gopal.  2021.  A Study On The Systematic Review Of Security Vulnerabilities Of Popular Web Browsers. 2021 International Conference on Technological Advancements and Innovations (ICTAI). :314—318.
Internet browser is the most normally utilized customer application and speed and proficiency of our online work rely upon program generally. As the market is immersed with new programs there is a ton of disarray in everybody’s psyche regarding which is the best program. Our task intends to respond to this inquiry. We have done a relative investigation of the most well-known internet browsers specifically Google Chrome, Mozilla Firefox, Internet Explorer, Microsoft Edge, Opera, etc. In the main period of our task different correlation boundaries are chosen which can be comprehensively classified into - General Features, Security highlights, and program extensibility highlights. Utilizing the chose benchmarking instruments every program is tried. The main objective of this study is to identify the security vulnerabilities of popular web browsers. We have also discussed and analyzed each potential security vulnerability found in the web browsers. The study also tries to recommend viable measures to slow down the security breach in web browsers.
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.
Sultana, Kazi Zakia, Codabux, Zadia, Williams, Byron.  2020.  Examining the Relationship of Code and Architectural Smells with Software Vulnerabilities. 2020 27th Asia-Pacific Software Engineering Conference (APSEC). :31–40.
Context: Security is vital to software developed for commercial or personal use. Although more organizations are realizing the importance of applying secure coding practices, in many of them, security concerns are not known or addressed until a security failure occurs. The root cause of security failures is vulnerable code. While metrics have been used to predict software vulnerabilities, we explore the relationship between code and architectural smells with security weaknesses. As smells are surface indicators of a deeper problem in software, determining the relationship between smells and software vulnerabilities can play a significant role in vulnerability prediction models. Objective: This study explores the relationship between smells and software vulnerabilities to identify the smells. Method: We extracted the class, method, file, and package level smells for three systems: Apache Tomcat, Apache CXF, and Android. We then compared their occurrences in the vulnerable classes which were reported to contain vulnerable code and in the neutral classes (non-vulnerable classes where no vulnerability had yet been reported). Results: We found that a vulnerable class is more likely to have certain smells compared to a non-vulnerable class. God Class, Complex Class, Large Class, Data Class, Feature Envy, Brain Class have a statistically significant relationship with software vulnerabilities. We found no significant relationship between architectural smells and software vulnerabilities. Conclusion: We can conclude that for all the systems examined, there is a statistically significant correlation between software vulnerabilities and some smells.
Ackley, Darryl, Yang, Hengzhao.  2020.  Exploration of Smart Grid Device Cybersecurity Vulnerability Using Shodan. 2020 IEEE Power Energy Society General Meeting (PESGM). :1–5.
The generation, transmission, distribution, and storage of electric power is becoming increasingly decentralized. Advances in Distributed Energy Resources (DERs) are rapidly changing the nature of the power grid. Moreover, the accommodation of these new technologies by the legacy grid requires that an increasing number of devices be Internet connected so as to allow for sensor and actuator information to be collected, transmitted, and processed. With the wide adoption of the Internet of Things (IoT), the cybersecurity vulnerabilities of smart grid devices that can potentially affect the stability, reliability, and resilience of the power grid need to be carefully examined and addressed. This is especially true in situations in which smart grid devices are deployed with default configurations or without reasonable protections against malicious activities. While much work has been done to characterize the vulnerabilities associated with Supervisory Control and Data Acquisition (SCADA) and Industrial Control System (ICS) devices, this paper demonstrates that similar vulnerabilities associated with the newer class of IoT smart grid devices are becoming a concern. Specifically, this paper first performs an evaluation of such devices using the Shodan platform and text processing techniques to analyze a potential vulnerability involving the lack of password protection. This work further explores several Shodan search terms that can be used to identify additional smart grid components that can be evaluated in terms of cybersecurity vulnerabilities. Finally, this paper presents recommendations for the more secure deployment of such smart grid devices.
Liu, Xiaoyang, Zhu, Ziyuan.  2020.  pcSVF: An Evaluation of Side-Channel Vulnerability of Port Contention. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1813–1819.
The threats from side-channel attacks to modern processors has become a serious problem, especially under the enhancement of the microarchitecture characteristics with multicore and resource sharing. Therefore, the research and measurement of the vulnerability of the side-channel attack of the system is of great significance for computer designers. Most of the current evaluation methods proposed by researchers are only for typical cache side-channel attacks. In this paper, we propose a method to measure systems' vulnerability to side-channel attacks caused by port contention called pcSVF. We collected the traces of the victim and attacker and computed the correlation coefficient between them, thus we can measure the vulnerability of the system against side-channel attack. Then we analyzed the effectiveness of the method through the results under different system defense schemes.
Dmitry, Morozov, Elena, Ponomareva.  2020.  Linux Privilege Increase Threat Analysis. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :0579—0581.
Today, Linux is one of the main operating systems (OS) used both on desktop computers and various mobile devices. This OS is also widely applied in state and municipal structures, including law enforcement agencies and automated control systems used in the Armed Forces of the Russian Federation. It's worth noting that the process of replacing the Linux OS with domestic protected OSs that use the Linux kernel has now begun. In this regard, the analysis of threats to information security of the Linux OS is highly relevant. In this article, the authors discuss the security problems of Linux OS associated with unauthorized user privileges increase, as a result of which an attacker can gain full control over the OS. The approaches to differentiating user privileges in Linux are analyzed and their advantages and disadvantages are considered. As an example, the causes of the vulnerability CVE-2018-14665 were identified and measures to eliminate it were proposed.
Abdalla, Peshraw Ahmed, Varol, Cihan.  2020.  Testing IoT Security: The Case Study of an IP Camera. 2020 8th International Symposium on Digital Forensics and Security (ISDFS). :1—5.
While the Internet of Things (IoT) applications and devices expanded rapidly, security and privacy of the IoT devices emerged as a major problem. Current studies reveal that there are significant weaknesses detected in several types of IoT devices moreover in several situations there are no security mechanisms to protect these devices. The IoT devices' users utilize the internet for the purpose of control and connect their machines. IoT application utilization has risen exponentially over time and our sensitive data is captured by IoT devices continuously, unknowingly or knowingly. The motivation behind this paper was the vulnerabilities that exist at the IP cameras. In this study, we undertake a more extensive investigation of IP cameras' vulnerabilities and demonstrate their effect on users' security and privacy through the use of the Kali Linux penetration testing platform and its tools. For this purpose, the paper performs a hands-on test on an IP camera with the name (“Intelligent Onvif YY HD”) to analyzes the security elements of this device. The results of this paper show that IP cameras have several security lacks and weaknesses which these flaws have multiple security impacts on users.
Beyza, Jesus, Bravo, Victor M., Garcia-Paricio, Eduardo, Yusta, Jose M., Artal-Sevil, Jesus S..  2020.  Vulnerability and Resilience Assessment of Power Systems: From Deterioration to Recovery via a Topological Model based on Graph Theory. 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). 4:1–6.
Traditionally, vulnerability is the level of degradation caused by failures or disturbances, and resilience is the ability to recover after a high-impact event. This paper presents a topological procedure based on graph theory to evaluate the vulnerability and resilience of power grids. A cascading failures model is developed by eliminating lines both deliberately and randomly, and four restoration strategies inspired by the network approach are proposed. In the two cases, the degradation and recovery of the electrical infrastructure are quantified through four centrality measures. Here, an index called flow-capacity is proposed to measure the level of network overload during the iterative processes. The developed sequential framework was tested on a graph of 600 nodes and 1196 edges built from the 400 kV high-voltage power system in Spain. The conclusions obtained show that the statistical graph indices measure different topological aspects of the network, so it is essential to combine the results to obtain a broader view of the structural behaviour of the infrastructure.
Dmitrievich, Asyaev Grigorii, Nikolaevich, Sokolov Aleksandr.  2020.  Automated Process Control Anomaly Detection Using Machine Learning Methods. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :0536–0538.
The paper discusses the features of the automated process control system, defines the algorithm for installing critical updates. The main problems in the administration of a critical system have been identified. The paper presents a model for recognizing anomalies in the network traffic of an industrial information system using machine learning methods. The article considers the network intrusion dataset (raw TCP / IP dump data was collected, where the network was subjected to multiple attacks). The main parameters that affect the recognition of abnormal behavior in the system are determined. The basic mathematical models of classification are analyzed, their basic parameters are reviewed and tuned. The mathematical model was trained on the considered (randomly mixed) sample using cross-validation and the response was predicted on the control (test) sample, where the model should determine the anomalous behavior of the system or normal as the output. The main criteria for choosing a mathematical model for the problem to be solved were the number of correctly recognized (accuracy) anomalies, precision and recall of the answers. Based on the study, the optimal algorithm for recognizing anomalies was selected, as well as signs by which this anomaly can be recognized.
Chen, Sen, Fan, Lingling, Meng, Guozhu, Su, Ting, Xue, Minhui, Xue, Yinxing, Liu, Yang, Xu, Lihua.  2020.  An Empirical Assessment of Security Risks of Global Android Banking Apps. 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). :1310—1322.
Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial loss caused by vulnerabilities, existing research lacks a comprehensive empirical study on the security risks of global banking apps to provide useful insights and improve the security of banking apps. Since data-related weaknesses in banking apps are critical and may directly cause serious financial loss, this paper first revisits the state-of-the-art available tools and finds that they have limited capability in identifying data-related security weaknesses of banking apps. To complement the capability of existing tools in data-related weakness detection, we propose a three-phase automated security risk assessment system, named Ausera, which leverages static program analysis techniques and sensitive keyword identification. By leveraging Ausera, we collect 2,157 weaknesses in 693 real-world banking apps across 83 countries, which we use as a basis to conduct a comprehensive empirical study from different aspects, such as global distribution and weakness evolution during version updates. We find that apps owned by subsidiary banks are always less secure than or equivalent to those owned by parent banks. In addition, we also track the patching of weaknesses and receive much positive feedback from banking entities so as to improve the security of banking apps in practice. We further find that weaknesses derived from outdated versions of banking apps or third-party libraries are highly prone to being exploited by attackers. To date, we highlight that 21 banks have confirmed the weaknesses we reported (including 126 weaknesses in total). We also exchange insights with 7 banks, such as HSBC in UK and OCBC in Singapore, via in-person or online meetings to help them improve their apps. We hope that the insights developed in this paper will inform the communities about the gaps among multiple stakeholders, including banks, academic researchers, and third-party security companies.
Zhu, Fangzhou, Liu, Liang, Meng, Weizhi, Lv, Ting, Hu, Simin, Ye, Renjun.  2020.  SCAFFISD: A Scalable Framework for Fine-Grained Identification and Security Detection of Wireless Routers. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1194–1199.

The security of wireless network devices has received widespread attention, but most existing schemes cannot achieve fine-grained device identification. In practice, the security vulnerabilities of a device are heavily depending on its model and firmware version. Motivated by this issue, we propose a universal, extensible and device-independent framework called SCAFFISD, which can provide fine-grained identification of wireless routers. It can generate access rules to extract effective information from the router admin page automatically and perform quick scans for known device vulnerabilities. Meanwhile, SCAFFISD can identify rogue access points (APs) in combination with existing detection methods, with the purpose of performing a comprehensive security assessment of wireless networks. We implement the prototype of SCAFFISD and verify its effectiveness through security scans of actual products.

Obaidat, M., Brown, J., Hayajneh, A. A..  2020.  Web Browser Extension User-Script XSS Vulnerabilities. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :316—321.

Browser extensions have by and large become a normal and accepted omnipresent feature within modern browsers. However, since their inception, browser extensions have remained under scrutiny for opening vulnerabilities for users. While a large amount of effort has been dedicated to patching such issues as they arise, including the implementation of extension sandboxes and explicit permissions, issues remain within the browser extension ecosystem through user-scripts. User-scripts, or micro-script extensions hosted by a top-level extension, are largely unregulated but inherit the permissions of the top-level application manager, which popularly includes extensions such as Greasemonkey, Tampermonkey, or xStyle. While most user-scripts are docile and serve a specific beneficial functionality, due to their inherently open nature and the unregulated ecosystem, they are easy for malicious parties to exploit. Common attacks through this method involve hijacking of DOM elements to execute malicious javascript and/or XSS attacks, although other more advanced attacks can be deployed as well. User-scripts have not received much attention, and this vulnerability has persisted despite attempts to make browser extensions more secure. This ongoing vulnerability remains an unknown threat to many users who employ user-scripts, and circumvents security mechanisms otherwise put in place by browsers. This paper discusses this extension derivative vulnerability as it pertains to current browser security paradigms.

Marchisio, A., Nanfa, G., Khalid, F., Hanif, M. A., Martina, M., Shafique, M..  2020.  Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs). Recent works have shown that DNNs are vulnerable to adversarial attacks, i.e., small perturbations added to the input data can lead to targeted or random misclassifications. In this paper, we aim at investigating the key research question: "Are SNNs secure?" Towards this, we perform a comparative study of the security vulnerabilities in SNNs and DNNs w.r.t. the adversarial noise. Afterwards, we propose a novel black-box attack methodology, i.e., without the knowledge of the internal structure of the SNN, which employs a greedy heuristic to automatically generate imperceptible and robust adversarial examples (i.e., attack images) for the given SNN. We perform an in-depth evaluation for a Spiking Deep Belief Network (SDBN) and a DNN having the same number of layers and neurons (to obtain a fair comparison), in order to study the efficiency of our methodology and to understand the differences between SNNs and DNNs w.r.t. the adversarial examples. Our work opens new avenues of research towards the robustness of the SNNs, considering their similarities to the human brain's functionality.
DiMase, D., Collier, Z. A., Chandy, J., Cohen, B. S., D'Anna, G., Dunlap, H., Hallman, J., Mandelbaum, J., Ritchie, J., Vessels, L..  2020.  A Holistic Approach to Cyber Physical Systems Security and Resilience. 2020 IEEE Systems Security Symposium (SSS). :1—8.

A critical need exists for collaboration and action by government, industry, and academia to address cyber weaknesses or vulnerabilities inherent to embedded or cyber physical systems (CPS). These vulnerabilities are introduced as we leverage technologies, methods, products, and services from the global supply chain throughout a system's lifecycle. As adversaries are exploiting these weaknesses as access points for malicious purposes, solutions for system security and resilience become a priority call for action. The SAE G-32 Cyber Physical Systems Security Committee has been convened to address this complex challenge. The SAE G-32 will take a holistic systems engineering approach to integrate system security considerations to develop a Cyber Physical System Security Framework. This framework is intended to bring together multiple industries and develop a method and common language which will enable us to more effectively, efficiently, and consistently communicate a risk, cost, and performance trade space. The standard will allow System Integrators to make decisions utilizing a common framework and language to develop affordable, trustworthy, resilient, and secure systems.