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Vastel, A., Rudametkin, W., Rouvoy, R..  2018.  FP -TESTER : Automated Testing of Browser Fingerprint Resilience. 2018 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :103-107.
Despite recent regulations and growing user awareness, undesired browser tracking is increasing. In addition to cookies, browser fingerprinting is a stateless technique that exploits a device's configuration for tracking purposes. In particular, browser fingerprinting builds on attributes made available from Javascript and HTTP headers to create a unique and stable fingerprint. For example, browser plugins have been heavily exploited by state-of-the-art browser fingerprinters as a rich source of entropy. However, as browser vendors abandon plugins in favor of extensions, fingerprinters will adapt. We present FP-TESTER, an approach to automatically test the effectiveness of browser fingerprinting countermeasure extensions. We implement a testing toolkit to be used by developers to reduce browser fingerprintability. While countermeasures aim to hinder tracking by changing or blocking attributes, they may easily introduce subtle side-effects that make browsers more identifiable, rendering the extensions counterproductive. FP-TESTER reports on the side-effects introduced by the countermeasure, as well as how they impact tracking duration from a fingerprinter's point-of-view. To the best of our knowledge, FP-TESTER is the first tool to assist developers in fighting browser fingerprinting and reducing the exposure of end-users to such privacy leaks.
Vatanparvar, Korosh, Al Faruque, Mohammad Abdullah.  2019.  Self-Secured Control with Anomaly Detection and Recovery in Automotive Cyber-Physical Systems. 2019 Design, Automation Test in Europe Conference Exhibition (DATE). :788–793.

Cyber-Physical Systems (CPS) are growing with added complexity and functionality. Multidisciplinary interactions with physical systems are the major keys to CPS. However, sensors, actuators, controllers, and wireless communications are prone to attacks that compromise the system. Machine learning models have been utilized in controllers of automotive to learn, estimate, and provide the required intelligence in the control process. However, their estimation is also vulnerable to the attacks from physical or cyber domains. They have shown unreliable predictions against unknown biases resulted from the modeling. In this paper, we propose a novel control design using conditional generative adversarial networks that will enable a self-secured controller to capture the normal behavior of the control loop and the physical system, detect the anomaly, and recover from them. We experimented our novel control design on a self-secured BMS by driving a Nissan Leaf S on standard driving cycles while under various attacks. The performance of the design has been compared to the state-of-the-art; the self-secured BMS could detect the attacks with 83% accuracy and the recovery estimation error of 21% on average, which have improved by 28% and 8%, respectively.

Vaughn, Jr., Rayford B., Morris, Tommy.  2016.  Addressing Critical Industrial Control System Cyber Security Concerns via High Fidelity Simulation. Proceedings of the 11th Annual Cyber and Information Security Research Conference. :12:1–12:4.

This paper outlines a set of 10 cyber security concerns associated with Industrial Control Systems (ICS). The concerns address software and hardware development, implementation, and maintenance practices, supply chain assurance, the need for cyber forensics in ICS, a lack of awareness and training, and finally, a need for test beds which can be used to address the first 9 cited concerns. The concerns documented in this paper were developed based on the authors' combined experience conducting research in this field for the US Department of Homeland Security, the National Science Foundation, and the Department of Defense. The second half of this paper documents a virtual test bed platform which is offered as a tool to address the concerns listed in the first half of the paper. The paper discusses various types of test beds proposed in literature for ICS research, provides an overview of the virtual test bed platform developed by the authors, and lists future works required to extend the existing test beds to serve as a development platform.

Vavala, B., Neves, N., Steenkiste, P..  2017.  Secure Tera-scale Data Crunching with a Small TCB. 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :169–180.

Outsourcing services to third-party providers comes with a high security cost-to fully trust the providers. Using trusted hardware can help, but current trusted execution environments do not adequately support services that process very large scale datasets. We present LASTGT, a system that bridges this gap by supporting the execution of self-contained services over a large state, with a small and generic trusted computing base (TCB). LASTGT uses widely deployed trusted hardware to guarantee integrity and verifiability of the execution on a remote platform, and it securely supplies data to the service through simple techniques based on virtual memory. As a result, LASTGT is general and applicable to many scenarios such as computational genomics and databases, as we show in our experimental evaluation based on an implementation of LAST-GT on a secure hypervisor. We also describe a possible implementation on Intel SGX.

Vávra, J., Hromada, M..  2017.  Anomaly Detection System Based on Classifier Fusion in ICS Environment. 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT). :32–38.

The detection of cyber-attacks has become a crucial task for highly sophisticated systems like industrial control systems (ICS). These systems are an essential part of critical information infrastructure. Therefore, we can highlight their vital role in contemporary society. The effective and reliable ICS cyber defense is a significant challenge for the cyber security community. Thus, intrusion detection is one of the demanding tasks for the cyber security researchers. In this article, we examine classification problem. The proposed detection system is based on supervised anomaly detection techniques. Moreover, we utilized classifiers algorithms in order to increase intrusion detection capabilities. The fusion of the classifiers is the way how to achieve the predefined goal.

Vaziri, Mandana, Mandel, Louis, Shinnar, Avraham, Siméon, Jérôme, Hirzel, Martin.  2017.  Generating Chat Bots from Web API Specifications. Proceedings of the 2017 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software. :44–57.

Companies want to offer chat bots to their customers and employees which can answer questions, enable self-service, and showcase their products and services. Implementing and maintaining chat bots by hand costs time and money. Companies typically have web APIs for their services, which are often documented with an API specification. This paper presents a compiler that takes a web API specification written in Swagger and automatically generates a chat bot that helps the user make API calls. The generated bot is self-documenting, using descriptions from the API specification to answer help requests. Unfortunately, Swagger specifications are not always good enough to generate high-quality chat bots. This paper addresses this problem via a novel in-dialogue curation approach: the power user can improve the generated chat bot by interacting with it. The result is then saved back as an API specification. This paper reports on the design and implementation of the chat bot compiler, the in-dialogue curation, and working case studies.

Vazirian, Samane, Zahedi, Morteza.  2016.  A modified language modeling method for authorship attribution. :32–37.

This paper presents an approach to a closed-class authorship attribution (AA) problem. It is based on language modeling for classification and called modified language modeling. Modified language modeling aims to offer a solution for AA problem by Combinations of both bigram words weighting and Unigram words weighting. It makes the relation between unseen text and training documents clearer with giving extra reward of training documents; training document including bigram word as well as unigram words. Moreover, IDF value multiplied by related word probability has been used, instead of removing stop words which are provided by Stop words list. we evaluate Experimental results by four approaches; unigram, bigram, trigram and modified language modeling by using two Persian poem corpora as WMPR-AA2016-A Dataset and WMPR-AA2016-B Dataset. Results show that modified language modeling attributes authors better than other approaches. The result on WMPR-AA2016-B, which is bigger dataset, is much better than another dataset for all approaches. This may indicate that if adequate data is provided to train language modeling the modified language modeling can be a good solution to AA problem.

Vazquez Sandoval, Itzel, Lenzini, Gabriele.  2018.  Experience Report: How to Extract Security Protocols' Specifications from C Libraries. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). 02:719—724.

Often, analysts have to face a challenging situation when formally verifying the implementation of a security protocol: they need to build a model of the protocol from only poorly or not documented code, and with little or no help from the developers to better understand it. Security protocols implementations frequently use services provided by libraries coded in the C programming language; automatic tools for codelevel reverse engineering offer good support to comprehend the behavior of code in object-oriented languages but are ineffective to deal with libraries in C. Here we propose a systematic, yet human-dependent approach, which combines the capabilities of state-of-the-art tools in order to help the analyst to retrieve, step by step, the security protocol specifications from a library in C. Those specifications can then be used to create the formal model needed to carry out the analysis.

Vegda, Hiral, Modi, Nimesh.  2018.  Secure and Efficient Approach to Prevent Ad Hoc Network Attacks Using Intrusion Detection System. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). :129-133.

In Ad hoc networks the main purpose is communication without infrastructure and there are many implementations already done on that. There is little effort done for security to prevent threats in ad hoc networks (like MANETs). It is already proven that; there is no any centralized mechanism for defence against threats, such as a firewall, an intrusion detection system, or a proxy in ad hoc networks. Ad hoc networks are very convenient due to its features like self-maintenance, self-organizing and providing wireless communication. In Ad hoc networks there is no fixed infrastructure in which every node works like simply a router which stores and forwards packet to final destination. Due to these dynamic topology features, Ad hoc networks are anywhere, anytime. Therefore, it is necessary to make a secure mechanism for the ad hoc components so that with flexibility they have that security also. This paper shows the secure and flexible implementation about to protect any ad hoc networks. This proposed system design is perfect solution to provide security with flexibility by providing a hybrid system which combines ECC and MAES to detect and prevent Ad hoc network attacks using Intrusion detection system. The complete proposed system designed on NS 2.35 software using Ubuntu (Linux) OS.

Vegh, L., Miclea, L..  2014.  Enhancing security in cyber-physical systems through cryptographic and steganographic techniques. Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on. :1-6.

Information technology is continually changing, discoveries are made every other day. Cyber-physical systems consist of both physical and computational elements and are becoming more and more popular in today's society. They are complex systems, used in complex applications. Therefore, security is a critical and challenging aspect when developing cyber-physical systems. In this paper, we present a solution for ensuring data confidentiality and security by combining some of the most common methods in the area of security - cryptography and steganography. Furthermore, we use hierarchical access to information to ensure confidentiality and also increase the overall security of the cyber-physical system.

Vegh, Laura.  2018.  Cyber-physical systems security through multi-factor authentication and data analytics. 2018 IEEE International Conference on Industrial Technology (ICIT). :1369–1374.
We are living in a society where technology is present everywhere we go. We are striving towards smart homes, smart cities, Internet of Things, Internet of Everything. Not so long ago, a password was all you needed for secure authentication. Nowadays, even the most complicated passwords are not considered enough. Multi-factor authentication is gaining more and more terrain. Complex system may also require more than one solution for real, strong security. The present paper proposes a framework based with MFA as a basis for access control and data analytics. Events within a cyber-physical system are processed and analyzed in an attempt to detect, prevent and mitigate possible attacks.
Velan, Petr, Husák, Martin, Tovarňák, Daniel.  2018.  Rapid prototyping of flow-based detection methods using complex event processing. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. :1—3.
Detection of network attacks is the first step to network security. Many different methods for attack detection were proposed in the past. However, descriptions of these methods are often not complete and it is difficult to verify that the actual implementation matches the description. In this demo paper, we propose to use Complex Event Processing (CEP) for developing detection methods based on network flows. By writing the detection methods in an Event Processing Language (EPL), we can address the above-mentioned problems. The SQL-like syntax of most EPLs is easily readable so the detection method is self-documented. Moreover, it is directly executable in the CEP system, which eliminates inconsistencies between documentation and implementation. The demo will show a running example of a multi-stage HTTP brute force attack detection using Esper and its EPL.
Velaora, M., Roy, R. van, Guéna, F..  2020.  ARtect, an augmented reality educational prototype for architectural design. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :110—115.

ARtect is an Augmented Reality application developed with Unity 3D, which envisions an educational interactive and immersive tool for architects, designers, researchers, and artists. This digital instrument renders the competency to visualize custom-made 3D models and 2D graphics in interior and exterior environments. The user-friendly interface offers an accurate insight before the materialization of any architectural project, enabling evaluation of the design proposal. This practice could be integrated into learning architectural design process, saving resources of printed drawings, and 3D carton models during several stages of spatial conception.

Velásquez, E. P., Correa, J. C..  2017.  Methodology (N2FMEA) for the detection of risks associated with the components of an underwater system. OCEANS 2017 - Anchorage. :1–4.

This paper combines FMEA and n2 approaches in order to create a methodology to determine risks associated with the components of an underwater system. This methodology is based on defining the risk level related to each one of the components and interfaces that belong to a complex underwater system. As far as the authors know, this approach has not been reported before. The resulting information from the mentioned procedures is combined to find the system's critical elements and interfaces that are most affected by each failure mode. Finally, a calculation is performed to determine the severity level of each failure mode based on the system's critical elements.

Vellaithurai, C., Srivastava, A., Zonouz, S., Berthier, R..  2015.  CPIndex: Cyber-Physical Vulnerability Assessment for Power-Grid Infrastructures. Smart Grid, IEEE Transactions on. 6:566-575.

To protect complex power-grid control networks, power operators need efficient security assessment techniques that take into account both cyber side and the power side of the cyber-physical critical infrastructures. In this paper, we present CPINDEX, a security-oriented stochastic risk management technique that calculates cyber-physical security indices to measure the security level of the underlying cyber-physical setting. CPINDEX installs appropriate cyber-side instrumentation probes on individual host systems to dynamically capture and profile low-level system activities such as interprocess communications among operating system assets. CPINDEX uses the generated logs along with the topological information about the power network configuration to build stochastic Bayesian network models of the whole cyber-physical infrastructure and update them dynamically based on the current state of the underlying power system. Finally, CPINDEX implements belief propagation algorithms on the created stochastic models combined with a novel graph-theoretic power system indexing algorithm to calculate the cyber-physical index, i.e., to measure the security-level of the system's current cyber-physical state. The results of our experiments with actual attacks against a real-world power control network shows that CPINDEX, within few seconds, can efficiently compute the numerical indices during the attack that indicate the progressing malicious attack correctly.

Vellingiri, Shanthi, Balakrishnan, Prabhakaran.  2017.  Modeling User Quality of Experience (QoE) through Position Discrepancy in Multi-Sensorial, Immersive, Collaborative Environments. Proceeding MMSys'17 Proceedings of the 8th ACM on Multimedia Systems Conference Pages 296-307 .

Users' QoE (Quality of Experience) in Multi-sensorial, Immersive, Collaborative Environments (MICE) applications is mostly measured by psychometric studies. These studies provide a subjective insight into the performance of such applications. In this paper, we hypothesize that spatial coherence or the lack of it of the embedded virtual objects among users has a correlation to the QoE in MICE. We use Position Discrepancy (PD) to model this lack of spatial coherence in MICE. Based on that, we propose a Hierarchical Position Discrepancy Model (HPDM) that computes PD at multiple levels to derive the application/system-level PD as a measure of performance.; AB@Experimental results on an example task in MICE show that HPDM can objectively quantify the application performance and has a correlation to the psychometric study-based QoE measurements. We envisage HPDM can provide more insight on the MICE application without the need for extensive user study.

Velmovitsky, Pedro Elkind, Viana, Marx, Cirilo, Elder, Milidiu, Ruy Luiz, Pelegrini Morita, Plinio, Lucena, Carlos José Pereira de.  2019.  Promoting Reusability and Extensibility in the Engineering of Domain-Specific Conversational Systems. 2019 8th Brazilian Conference on Intelligent Systems (BRACIS). :473—478.

Conversational systems are computer programs that interact with users using natural language. Considering the complexity and interaction of the different components involved in building intelligent conversational systems that can perform diverse tasks, a promising approach to facilitate their development is by using multiagent systems (MAS). This paper reviews the main concepts and history of conversational systems, and introduces an architecture based on MAS. This architecture was designed to support the development of conversational systems in the domain chosen by the developer while also providing a reusable built-in dialogue control. We present a practical application in the healthcare domain. We observed that it can help developers to create conversational systems in different domains while providing a reusable and centralized dialogue control. We also present derived lessons learned that can be helpful to steer future research on engineering domain-specific conversational systems.

Velmurugan, K.Jayasakthi, Hemavathi, S..  2019.  Video Steganography by Neural Networks Using Hash Function. 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 1:55–58.

Video Steganography is an extension of image steganography where any kind of file in any extension is hidden into a digital video. The video content is dynamic in nature and this makes the detection of hidden data difficult than other steganographic techniques. The main motive of using video steganography is that the videos can store large amount of data in it. This paper focuses on security using the combination of hybrid neural networks and hash function for determining the best bits in the cover video to embed the secret data. For the embedding process, the cover video and the data to be hidden is uploaded. Then the hash algorithm and neural networks are applied to form the stego video. For the extraction process, the reverse process is applied and the secret data is obtained. All experiments are done using MatLab2016a software.

Veloudis, Simeon, Paraskakis, Iraklis, Petsos, Christos.  2017.  An Ontological Framework for Determining the Repercussions of Retirement Actions Targeted at Complex Access Control Policies in Cloud Environments. Companion Proceedings of the10th International Conference on Utility and Cloud Computing. :21–28.
By migrating their data and operations to the cloud, enterprises are able to gain significant benefits in terms of cost savings, increased availability, agility and productivity. Yet, the shared and on-demand nature of the cloud paradigm introduces a new breed of security threats that generally deter stakeholders from relinquishing control of their critical assets to third-party cloud providers. One way to thwart these threats is to instill suitable access control policies into cloud services that protect these assets. Nevertheless, the dynamic nature of cloud environments calls for policies that are able to incorporate a potentially complex body of contextual knowledge. This complexity is further amplified by the interplay that inevitably occurs between the different policies, as well as by the dynamically-evolving nature of an organisation's business and security needs. We argue that one way to tame this complexity is to devise a generic framework that facilitates the governance of policies. This paper presents a particular aspect of such a framework, namely an approach to determining the repercussions that policy retirement actions have on the overall protection of critical assets in the cloud.
Veloudis, Simeon, Paraskakis, Iraklis, Petsos, Christos.  2017.  Ontological Framework for Ensuring Correctness of Security Policies in Cloud Environments. Proceedings of the 8th Balkan Conference in Informatics. :23:1–23:8.

By embracing the cloud computing paradigm enterprises are able to boost their agility and productivity whilst realising significant cost savings. However, many enterprises are reluctant to adopt cloud services for supporting their critical operations due to security and privacy concerns. One way to alleviate these concerns is to devise policies that infuse suitable security controls in cloud services. This work proposes a class of ontologically-expressed rules, namely the so-called axiomatic rules, that aim at ensuring the correctness of these policies by harnessing the various knowledge artefacts that they embody. It also articulates an adequate framework for the expression of policies, one which provides ontological templates for modelling the knowledge artefacts encoded in the policies and which form the basis for the proposed axiomatic rules.

Veloudis, Simeon, Paraskakis, Iraklis, Petsos, Christos.  2017.  Ontological Definition of Governance Framework for Security Policies in Cloud Environments. Proceedings of the 21st Pan-Hellenic Conference on Informatics. :12:1–12:6.

The cloud computing paradigm enables enterprises to realise significant cost savings whilst boosting their agility and productivity. However, security and privacy concerns generally deter enterprises from migrating their critical data to the cloud. One way to alleviate these concerns, hence bolster the adoption of cloud computing, is to devise adequate security policies that control the manner in which these data are stored and accessed in the cloud. Nevertheless, for enterprises to entrust these policies, a framework capable of providing assurances about their correctness is required. This work proposes such a framework. In particular, it proposes an approach that enables enterprises to define their own view of what constitutes a correct policy through the formulation of an appropriate set of well-formedness constraints. These constraints are expressed ontologically thus enabling–-by virtue of semantic inferencing–- automated reasoning about their satisfaction by the policies.

Velthuis, Paul J. E., Schäfer, Marcel, Steinebach, Martin.  2018.  New Authentication Concept Using Certificates for Big Data Analytic Tools. Proceedings of the 13th International Conference on Availability, Reliability and Security. :40:1–40:7.

Companies analyse large amounts of data on clusters of machines, using big data analytic tools such as Apache Spark and Apache Flink to analyse the data. Big data analytic tools are mainly tested regarding speed and reliability. Efforts about Security and thus authentication are spent only at second glance. In such big data analytic tools, authentication is achieved with the help of the Kerberos protocol that is basically built as authentication on top of big data analytic tools. However, Kerberos is vulnerable to attacks, and it lacks providing high availability when users are all over the world. To improve the authentication, this work presents first an analysis of the authentication in Hadoop and the data analytic tools. Second, we propose a concept to deploy Transport Layer Security (TLS) not only for the security of data transportation but as well for authentication within the big data tools. This is done by establishing the connections using certificates with a short lifetime. The proof of concept is realized in Apache Spark, where Kerberos is replaced by the method proposed. We deploy new short living certificates for authentication that are less vulnerable to abuse. With our approach the requirements of the industry regarding multi-factor authentication and scalability are met.

Vemparala, Swapna, Di Troia, Fabio, Corrado, Visaggio Aaron, Austin, Thomas H., Stamo, Mark.  2016.  Malware Detection Using Dynamic Birthmarks. Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics. :41–46.

In this paper, we compare the effectiveness of Hidden Markov Models (HMMs) with that of Profile Hidden Markov Models (PHMMs), where both are trained on sequences of API calls. We compare our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in comparing our two dynamic analysis approaches, we find that using PHMMs consistently outperforms our technique based on HMMs.

Venceslai, Valerio, Marchisio, Alberto, Alouani, Ihsen, Martina, Maurizio, Shafique, Muhammad.  2020.  NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.

Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer attack that threatens the SNNs integrity by exploiting low-level reliability issues through a high-level attack. Particularly, we trigger a fault-injection based sneaky hardware backdoor through a carefully crafted adversarial input noise. Our results on Deep Neural Networks (DNNs) and SNNs show a serious integrity threat to state-of-the art machine-learning techniques.

Venkat, Ashish, Shamasunder, Sriskanda, Shacham, Hovav, Tullsen, Dean M..  2016.  HIPStR: Heterogeneous-ISA Program State Relocation. Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems. :727–741.

Heterogeneous Chip Multiprocessors have been shown to provide significant performance and energy efficiency gains over homogeneous designs. Recent research has expanded the dimensions of heterogeneity to include diverse Instruction Set Architectures, called Heterogeneous-ISA Chip Multiprocessors. This work leverages such an architecture to realize substantial new security benefits, and in particular, to thwart Return-Oriented Programming. This paper proposes a novel security defense called HIPStR – Heterogeneous-ISA Program State Relocation – that performs dynamic randomization of run-time program state, both within and across ISAs. This technique outperforms the state-of-the-art just-in-time code reuse (JIT-ROP) defense by an average of 15.6%, while simultaneously providing greater security guarantees against classic return-into-libc, ROP, JOP, brute force, JIT-ROP, and several evasive variants.