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Showing papers on "Vulnerability (computing) published in 2017"


Journal ArticleDOI
TL;DR: An optimization model is proposed to characterize the behavior of one type of FDI attack that compromises the limited number of state measurements of the power system for electricity theft and achieves high accuracy.
Abstract: Application of computing and communications intelligence effectively improves the quality of monitoring and control of smart grids However, the dependence on information technology also increases vulnerability to malicious attacks False data injection (FDI), that attack on the integrity of data, is emerging as a severe threat to the supervisory control and data acquisition system In this paper, we exploit deep learning techniques to recognize the behavior features of FDI attacks with the historical measurement data and employ the captured features to detect the FDI attacks in real-time By doing so, our proposed detection mechanism effectively relaxes the assumptions on the potential attack scenarios and achieves high accuracy Furthermore, we propose an optimization model to characterize the behavior of one type of FDI attack that compromises the limited number of state measurements of the power system for electricity theft We illustrate the performance of the proposed strategy through the simulation by using IEEE 118-bus test system We also evaluate the scalability of our proposed detection mechanism by using IEEE 300-bus test system

574 citations


Journal ArticleDOI
TL;DR: In this article, a distributed denial-of-service attack demonstrated the high vulnerability of Internet of Things (IoT) systems and devices and addressed this challenge will require scalable security solutions optimized for the IoT ecosystem.
Abstract: Recent distributed denial-of-service attacks demonstrate the high vulnerability of Internet of Things (IoT) systems and devices. Addressing this challenge will require scalable security solutions optimized for the IoT ecosystem.

470 citations


Journal ArticleDOI
TL;DR: This paper describes the various aspects of face presentation attacks, including different types of face artifacts, state-of-the-art PAD algorithms and an overview of the respective research labs working in this domain, vulnerability assessments and performance evaluation metrics, the outcomes of competitions, the availability of public databases for benchmarking new P AD algorithms in a reproducible manner, and a summary of the relevant international standardization in this field.
Abstract: The vulnerability of face recognition systems to presentation attacks (also known as direct attacks or spoof attacks) has received a great deal of interest from the biometric community. The rapid evolution of face recognition systems into real-time applications has raised new concerns about their ability to resist presentation attacks, particularly in unattended application scenarios such as automated border control. The goal of a presentation attack is to subvert the face recognition system by presenting a facial biometric artifact. Popular face biometric artifacts include a printed photo, the electronic display of a facial photo, replaying video using an electronic display, and 3D face masks. These have demonstrated a high security risk for state-of-the-art face recognition systems. However, several presentation attack detection (PAD) algorithms (also known as countermeasures or antispoofing methods) have been proposed that can automatically detect and mitigate such targeted attacks. The goal of this survey is to present a systematic overview of the existing work on face presentation attack detection that has been carried out. This paper describes the various aspects of face presentation attacks, including different types of face artifacts, state-of-the-art PAD algorithms and an overview of the respective research labs working in this domain, vulnerability assessments and performance evaluation metrics, the outcomes of competitions, the availability of public databases for benchmarking new PAD algorithms in a reproducible manner, and finally a summary of the relevant international standardization in this field. Furthermore, we discuss the open challenges and future work that need to be addressed in this evolving field of biometrics.

280 citations


Journal ArticleDOI
TL;DR: It is shown that an attacker can construct an undetectable attack vector against ac state estimation based on a few measurements in the attacking region associated with boundary buses without knowing the full topology and parameter information of the entire power network.
Abstract: Power systems are being exposed to cyber-attacks due to the high integration of information technology and the vulnerability of communication networks. Existing false data attacks research focus on dc state estimation. In this paper, we show that an attacker can construct an undetectable attack vector against ac state estimation based on a few measurements in the attacking region associated with boundary buses without knowing the full topology and parameter information of the entire power network. An iteration approach is adopted to obtain the attack vector. The simulations on the IEEE 14-bus and 118-bus systems are used to demonstrate the correctness and effectiveness of the proposed attack scheme. This paper provides a basis to study the attack behaviors under the ac case, and a theoretical guide to develop protection strategies and detection methods.

206 citations


Posted Content
TL;DR: This paper showed that the revealed internal information helps generate more effective adversarial examples against the black-box model, which can be used for better protection of private content from automatic recognition models using adversarial example.
Abstract: Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -- we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models.

205 citations


Journal ArticleDOI
TL;DR: A Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors is proposed to identify new smart grid vulnerability that can be exploited by attacks on the network topology.
Abstract: Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be expanded to handle the complex risk of cyber-physical attacks. To analyze the transmission grid vulnerability under sequential topology attacks, this paper proposes a Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors. A realistic power flow cascading outage model is used to simulate the system behavior, where attacker can use the Q-learning to improve the damage of sequential topology attack toward system failures with the least attack efforts. Case studies based on three IEEE test systems have demonstrated the learning ability and effectiveness of Q-learning-based vulnerability analysis.

202 citations


Book ChapterDOI
15 Jul 2017
TL;DR: In this article, the transferability of adversarial examples is verified across different DQN models, and a novel class of attacks based on this vulnerability is presented to enable policy manipulation and induction in the learning process of DQNs.
Abstract: Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.

189 citations


Journal ArticleDOI
TL;DR: In this article, the authors explore civil maritime transportation's vulnerability to deceptive GPS signals and develop a detection technique that is compatible with sensors commonly available on modern ships and demonstrate the capability of an attacker to control a maritime surface vessel by broadcasting counterfeit civil GPS signals.
Abstract: An attacker's ability to control a maritime surface vessel by broadcasting counterfeit civil Global Positioning System (GPS) signals is analyzed and demonstrated. The aim of this work is to explore civil maritime transportation's vulnerability to deceptive GPS signals and to develop a detection technique that is compatible with sensors commonly available on modern ships. It is shown that despite access to a variety of high-quality navigation and surveillance sensors, modern maritime navigation depends crucially on satellite navigation and that a deception attack can be disguised as the effects of slowly-changing ocean currents. An innovations-based detection framework that optimally chooses the measurement sampling interval to minimize the probability of a ship exceeding its alert limits without detection is developed and analyzed. A field experiment confirms the vulnerability analysis by demonstrating hostile control of a 65-m yacht in the Mediterranean Sea. Copyright © 2017 Institute of Navigation

176 citations


Proceedings ArticleDOI
20 May 2017
TL;DR: The results indicate that the number of transitive dependencies for JavaScript has grown 60% over the last year, suggesting that developers should look more carefully into their dependencies to understand what exactly is included.
Abstract: Software developers often include available open-source software packages into their projects to minimize redundant effort. However, adding a package to a project can also introduce risks, which can propagate through multiple levels of dependencies. Currently, not much is known about the structure of open-source package ecosystems of popular programming languages and the extent to which transitive bug propagation is possible. This paper analyzes the dependency network structure and evolution of the JavaScript, Ruby, and Rust ecosystems. The reported results reveal significant differences across language ecosystems. The results indicate that the number of transitive dependencies for JavaScript has grown 60% over the last year, suggesting that developers should look more carefully into their dependencies to understand what exactly is included. The study also reveals that vulnerability to a removal of the most popular package is increasing, yet most other packages have a decreasing impact on vulnerability. The findings of this study can inform the development of dependency management tools.

145 citations


Proceedings ArticleDOI
01 Jan 2017
TL;DR: The first comprehensive study of client-side JavaScript library usage and the resulting security implications across the Web demonstrates that not only website administrators, but also the dynamic architecture and developers of third-party services are to blame for the Web's poor state of library management.
Abstract: Web developers routinely rely on third-party Java-Script libraries such as jQuery to enhance the functionality of their sites. However, if not properly maintained, such dependencies can create attack vectors allowing a site to be compromised. In this paper, we conduct the first comprehensive study of client-side JavaScript library usage and the resulting security implications across the Web. Using data from over 133 k websites, we show that 37% of them include at least one library with a known vulnerability; the time lag behind the newest release of a library is measured in the order of years. In order to better understand why websites use so many vulnerable or outdated libraries, we track causal inclusion relationships and quantify different scenarios. We observe sites including libraries in ad hoc and often transitive ways, which can lead to different versions of the same library being loaded into the same document at the same time. Furthermore, we find that libraries included transitively, or via ad and tracking code, are more likely to be vulnerable. This demonstrates that not only website administrators, but also the dynamic architecture and developers of third-party services are to blame for the Web's poor state of library management. The results of our work underline the need for more thorough approaches to dependency management, code maintenance and third-party code inclusion on the Web.

137 citations


Proceedings ArticleDOI
01 Sep 2017
TL;DR: This paper proposes a deep learning approach to predict multi-class severity level of software vulnerability using only vulnerability description, and uses word embeddings and a one-layer shallow Convolutional Neural Network to automatically capture discriminative word and sentence features of vulnerability descriptions for predicting vulnerability severity.
Abstract: Software vulnerabilities pose significant security risks to the host computing system. Faced with continuous disclosure of software vulnerabilities, system administrators must prioritize their efforts, triaging the most critical vulnerabilities to address first. Many vulnerability scoring systems have been proposed, but they all require expert knowledge to determine intricate vulnerability metrics. In this paper, we propose a deep learning approach to predict multi-class severity level of software vulnerability using only vulnerability description. Compared with intricate vulnerability metrics, vulnerability description is the "surface level" information about how a vulnerability works. To exploit vulnerability description for predicting vulnerability severity, discriminative features of vulnerability description have to be defined. This is a challenging task due to the diversity of software vulnerabilities and the richness of vulnerability descriptions. Instead of relying on manual feature engineering, our approach uses word embeddings and a one-layer shallow Convolutional Neural Network (CNN) to automatically capture discriminative word and sentence features of vulnerability descriptions for predicting vulnerability severity. We exploit large amounts of vulnerability data from the Common Vulnerabilities and Exposures (CVE) database to train and test our approach.

Journal ArticleDOI
TL;DR: A cloud architecture reference model that incorporates a wide range of security controls and best practices, and a cloud security assessment model that estimates high level security metrics to quantify the degree of confidentiality and integrity offered by a CCS or cloud service provider (CSP).
Abstract: The vulnerability of cloud computing systems (CCSs) to advanced persistent threats (APTs) is a significant concern to government and industry. We present a cloud architecture reference model that incorporates a wide range of security controls and best practices, and a cloud security assessment model—Cloud-Trust—that estimates high level security metrics to quantify the degree of confidentiality and integrity offered by a CCS or cloud service provider (CSP). Cloud-Trust is used to assess the security level of four multi-tenant IaaS cloud architectures equipped with alternative cloud security controls. Results show the probability of CCS penetration (high value data compromise) is high if a minimal set of security controls are implemented. CCS penetration probability drops substantially if a cloud defense in depth security architecture is adopted that protects virtual machine (VM) images at rest, strengthens CSP and cloud tenant system administrator access controls, and which employs other network security controls to minimize cloud network surveillance and discovery of live VMs.

Journal ArticleDOI
TL;DR: The potential for pairing mode in iOS devices (which allows the establishment of a trusted relationship between an iOS device and a personal computer) to be exploited for covert data exfiltration is highlighted.
Abstract: Increasingly, big data (including sensitive and commercial-in-confidence data) is being accessible and stored on a range of Internet of Things (IoT) devices, such as our mobile devices. Therefore, any vulnerability in IoT devices, operating system or software can be exploited by cybercriminals seeking to exfiltrate our data. In this paper, we use iOS devices as case studies and highlight the potential for pairing mode in iOS devices (which allows the establishment of a trusted relationship between an iOS device and a personal computer) to be exploited for covert data exfiltration. In our three case studies, we demonstrate how an attacker could exfiltrate data from a paired iOS device by abusing a library and a command line tool distributed with iTunes. With the aim of avoiding similar attacks in the future, we present two recommendations.

Journal ArticleDOI
TL;DR: The attack methodology, tactics, techniques and procedures that were successfully deployed in Ukraine could be deployed against infrastructure here and around the world.

Proceedings ArticleDOI
20 May 2017
TL;DR: A scalable binary-level patch analysis framework, named SPAIN, which can automatically identify security patches and summarize patch patterns and their corresponding vulnerability patterns and can be used to search similar patches or vulnerabilities in binary programs.
Abstract: Software vulnerability is one of the major threats to software security. Once discovered, vulnerabilities are often fixed by applying security patches. In that sense, security patches carry valuable information about vulnerabilities, which could be used to discover, understand and fix (similar) vulnerabilities. However, most existing patch analysis approaches work at the source code level, while binary-level patch analysis often heavily relies on a lot of human efforts and expertise. Even worse, some vulnerabilities may be secretly patched without applying CVE numbers, or only the patched binary programs are available while the patches are not publicly released. These practices greatly hinder patch analysis and vulnerability analysis. In this paper, we propose a scalable binary-level patch analysis framework, named SPAIN, which can automatically identify security patches and summarize patch patterns and their corresponding vulnerability patterns. Specifically, given the original and patched versions of a binary program, we locate the patched functions and identify the changed traces (i.e., a sequence of basic blocks) that may contain security or non-security patches. Then we identify security patches through a semantic analysis of these traces and summarize the patterns through a taint analysis on the patched functions. The summarized patterns can be used to search similar patches or vulnerabilities in binary programs. Our experimental results on several real-world projects have shown that: i) SPAIN identified security patches with high accuracy and high scalability, ii) SPAIN summarized 5 patch patterns and their corresponding vulnerability patterns for 5 vulnerability types, and iii) SPAIN discovered security patches that were not documented, and discovered 3 zero-day vulnerabilities.

Journal ArticleDOI
TL;DR: In this survey, both previous and current Somewhat Homomorphic Encryption schemes are reviewed, and the more powerful and recent Fully HomomorphicEncryption (FHE) schemes are comprehensively studied.
Abstract: It is unlikely that a hacker is able to compromise sensitive data that is stored in an encrypted form. However, when data is to be processed, it has to be decrypted, becoming vulnerable to attacks. Homomorphic encryption fixes this vulnerability by allowing one to compute directly on encrypted data. In this survey, both previous and current Somewhat Homomorphic Encryption (SHE) schemes are reviewed, and the more powerful and recent Fully Homomorphic Encryption (FHE) schemes are comprehensively studied. The concepts that support these schemes are presented, and their performance and security are analyzed from an engineering standpoint.

Proceedings ArticleDOI
01 Sep 2017
TL;DR: This paper reveals a security vulnerability in FPGAs that allows a valid configuration to generate severe voltage fluctuations, which crashes the FPGA within a few microseconds, and analyzes its underlying mechanism.
Abstract: Due to the widespread use of FPGAs in many critical application domains, their security is of high concern. In recent systems, such as FPGAs in the Cloud or in Systems-on-Chip (SoCs), users can gain access, even remotely, to the reconfigurable fabric to implement custom accelerators. This access can expose new security vulnerabilities in the entire system through malicious use of the FPGA fabric. In the past, attacks on the power supply level required local access to the hardware. In this paper, we reveal a security vulnerability in FPGAs that allows a valid configuration to generate severe voltage fluctuations, which crashes the FPGA within a few microseconds. Moreover, the extent of this crash is so severe, that manual power-cycling is required to be able to access and use the system again. This vulnerability has been systematically exploited in two different generations of FPGAs, and a SoC containing an FPGA. Because this vulnerability can lead to severe security attacks in systems using FPGA-based accelerators, we also analyze its underlying mechanism, and discuss possibilities for mitigation.

Proceedings ArticleDOI
01 May 2017
TL;DR: Based on the implementation on Samsung Galaxy smartphone and commodity Wi-Fi adapter, it is proved Move2Auth can protect against powerful active attack, i.e., the false-positive rate is consistently lower than 0.5%.
Abstract: Internet of Things (IoT) devices are largely embedded devices which lack a sophisticated user interface, e.g., touch screen, keyboard, etc. As a consequence, traditional Pre-Shared Key (PSK) based authentication for mobile devices becomes difficult to apply. For example, according to our study on home automation devices which leverage smartphone for PSK input, the current process does not protect against active impersonating attack and also leaks the Wi-Fi password to eavesdroppers, i.e., currently these IoT devices can be exploited to enter into critical infrastructures, e.g., home networks. Motivated by this real-world security vulnerability, in this paper we propose a novel proximity-based mechanism for IoT device authentication, called Move2Auth, for the purpose of enhancing IoT device security. In Move2Auth, we require user to hold smartphone and perform one of two hand-gestures (moving towards and away, and rotating) in front of IoT device. By combining (1) large RSS-variation and (2) matching between RSS-trace and smartphone sensor-trace, Move2Auth can reliably detect proximity and authenticate IoT device accordingly. Based on our implementation on Samsung Galaxy smartphone and commodity Wi-Fi adapter, we prove Move2Auth can protect against powerful active attack, i.e., the false-positive rate is consistently lower than 0.5%.

Proceedings ArticleDOI
20 Oct 2017
TL;DR: A flow-based anomaly detection is implemented with machine learning to overcome the limitation of signature-based IDS in the SDN architecture, and results show positive improvement for detection of almost all the possible attacks in SDN environment.
Abstract: Software-Defined Networks (SDN) is an emerging area that promises to change the way we design, build, and operate network architecture. It tends to shift from traditional network architecture of proprietary based to open and programmable network architecture. However, this new innovative and improved technology also brings another security burden into the network architecture, with existing and emerging security threats. The network vulnerability has become more open to intruders: the focus is now shifted to a single point of failure where the central controller is a prime target. Therefore, integration of intrusion detection system (IDS) into the SDN architecture is essential to provide a network with attack countermeasure. The work designed and developed a virtual testbed that simulates the processes of the real network environment, where a star topology is created with hosts and servers connected to the OpenFlow OVS-switch. Signature-based Snort IDS is deployed for traffic monitoring and attack detection, by mirroring the traffic destine to the servers. The vulnerability assessment shows possible attacks threat exist in the network architecture and effectively contain by Snort IDS except for the few which the suggestion is made for possible mitigation. In order to provide scalable threat detection in the architecture, a flow-based IDS model is developed. A flow-based anomaly detection is implemented with machine learning to overcome the limitation of signature-based IDS. The results show positive improvement for detection of almost all the possible attacks in SDN environment with our pattern recognition of neural network for machine learning using our trained model with over 97% accuracy.

Book
01 Jan 2017
TL;DR: Measuring Energy Security Performance in the OECD and Exploring the Contested and Convergent Nature of Energy Security Benjamin K. Sovacool and Tai Wei Lim.
Abstract: Part 1: Definitions and Concepts Introduction: Defining, Measuring, and Exploring Energy Security Benjamin K. Sovacool 1. Energy Security and Climate Change: A Tenuous Link Gal Luft, Ann Korin and Eshita Gupta 2. The Fuzzy Nature of Energy Security Scott Valentine 3. Evaluating the Energy Security Impacts of Energy Policies David von Hippel, Tatsujiro Suzuki, James H. Williams, Timothy Savage and Peter Hayes Part 2: Dimensions 4. The Sustainable Development Dimension of Energy Security Ami Indriyanto, Dwi Ari Fauzi and Alfa Firdaus 5. The Maritime Dimension of Energy Security Caroline Liss 6. The Public Policy Dimension of Energy Security Andreas Goldthau 7. The Diversification Dimension of Energy Security Andy Stirling 8. The Environmental Dimension of Energy Security Michael Dworkin and Marilyn Brown 9. The Energy Poverty Dimension of Energy Security Shonali Pachauri 10. The Social Development Dimension of Energy Security Anthony D'Agostino 11. The Energy Efficiency Dimension of Energy Security Nathalie Trudeau 12. The Energy Services Dimension of Energy Security Jaap Jansen and Adriaan J. Van der Welle 13. The Industrial Dimension of Energy Security Geoffrey Pakiam 14. The Competing Dimensions of Energy Security Martin J. Pasqualetti Part 3: Metrics and Indexing 15. Indicators for Energy Security Bert Kruyt, D.P. van Vuuren, H.J.M. de Vries and H. Groenenberg 16. Measuring Security of Energy Supply with Two Diversity Indexes John Kessels 17. Measuring Energy Security: From Universal Indicators to Contextualized Frameworks Aleh Cherp and Jessica Jewell 18. Applying the Four 'A's of Energy Security as Criteria in an Energy Security Ranking Method Larry Hughes and Darren Shupe 19. Measuring Energy Security Performance in the OECD Benjamin K. Sovacool and Marilyn A. Brown 20. Measuring Energy Security Vulnerability Edgard Gnansounou. Conclusion: Exploring the Contested and Convergent Nature of Energy Security Benjamin K. Sovacool and Tai Wei Lim

Posted Content
TL;DR: In this article, the transferability of adversarial examples is verified across different DQN models, and a novel class of attacks based on this vulnerability is presented to enable policy manipulation and induction in the learning process of DQNs.
Abstract: Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.

Journal ArticleDOI
TL;DR: This paper proposes a heuristic yet effective method to determine a feasible attacking region of a single line, which requires less network information, and uses six IEEE standard systems to validate the proposed attacking strategy.
Abstract: It has been shown that an attacker can stealthily launch false data injection attacks against the state estimation without knowing the full topology and parameter information of the entire power network. In this paper, we propose a heuristic yet effective method to determine a feasible attacking region of a single line, which requires less network information. We use six IEEE standard systems to validate the proposed attacking strategy. This paper can reveal the vulnerability of the real-time topology of a power grid and is very helpful to develop effective protection strategies against topology attacks in smart grids.

Journal ArticleDOI
Guangquan Xu1, Yan Cao1, Yuanyuan Ren1, Xiaohong Li1, Zhiyong Feng1 
TL;DR: Four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability, and network flow, and user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of the proposed ontology model.
Abstract: Internet of Things (IoT) brings the third development wave of the global information industry, which makes users, network, and perception devices cooperate more closely. However, if IoT has security problems, it may cause a variety of damage and even threaten human lives and properties. To improve the abilities of monitoring, providing emergency response, and predicting the development trend of IoT security, a new paradigm called network security situation awareness (NSSA) is proposed. However, it is limited by its ability to mine and evaluate security situation elements from multi-source heterogeneous network security information. To solve this problem, this paper proposes an IoT network security situation awareness model using a situation reasoning method based on semantic ontology and user-defined rules. Ontology technology can provide a unified and formalized description to solve the problem of semantic heterogeneity in the IoT security domain. In this paper, four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability, and network flow. Furthermore, user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of our proposed ontology model. The examples in real IoT scenarios show that the ability of the network security situation awareness that adopts our situation reasoning method is more comprehensive and more powerful reasoning abilities than the traditional NSSA methods.

Journal ArticleDOI
TL;DR: Results, upon comparison with various state-of-the-art techniques, depict that the proposed system is superior in performance and is highly effective in delivering healthcare services during workouts.

Proceedings ArticleDOI
01 Nov 2017
TL;DR: This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.
Abstract: The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.

Journal ArticleDOI
TL;DR: Capacity weighted spectral partitioning is proposed to identify potential flow bottlenecks in the network, without reference to demand information or path assignments, and identifies the network cut with least capacity, taking into account the relative sizes of the sub-networks either side of the cut.
Abstract: Transport networks operating at or near capacity are vulnerable to disruptions, so flow bottlenecks are potent sources of vulnerability. This paper presents an efficient method for finding transport network cuts, which may constitute such bottlenecks. Methods for assessing network vulnerability found in the literature require origin-destination demands and path assignment. However, in transport network planning and design, demand information is often missing, out of date, partial or inaccurate. Capacity weighted spectral partitioning is proposed to identify potential flow bottlenecks in the network, without reference to demand information or path assignments. This method identifies the network cut with least capacity, taking into account the relative sizes of the sub-networks either side of the cut. Spectral analysis has the added advantage of tractability, even for large networks, as shown by numerical examples for a five-node illustrative example, the Sioux Falls road network and the Gifu Prefecture road network.

Journal ArticleDOI
TL;DR: It is revealed that the authentication phase of the scheme does not defend against various known attacks, and a robust authentication scheme is proposed for WSNs, designed to provide security against known active and passive attacks.

Proceedings ArticleDOI
01 Jan 2017
TL;DR: This paper proposes a attack scenario and a countermeasure against replay attack that may occur in the join request transfer process in the LoRaWAN network.
Abstract: LPWAN (Low Power Wide Area Networks) technologies have been attracting attention continuously in IoT (Internet of Things). LoRaWAN is present on the market as a LPWAN technology and it has features such as low power consumption, low transceiver chip cost and wide coverage area. In the LoRaWAN, end devices must perform a join procedure for participating in the network. Attackers could exploit the join procedure because it has vulnerability in terms of security. Replay attack is a method of exploiting the vulnerability in the join procedure. In this paper, we propose a attack scenario and a countermeasure against replay attack that may occur in the join request transfer process.

Journal ArticleDOI
19 Apr 2017
TL;DR: In this paper, a Monte-Carlo simulation-based approach has been developed to analyse disruptions in the gas transmission network, where the vulnerability identification algorithm is used for finding a combination of component failures leading to the most significant security of supply disruptions.
Abstract: Energy supply disruptions highlight the need to study the design of energy infrastructure networks from the security of supply point of view. For this purpose, a Monte-Carlo simulation-based approach has been developed to analyse disruptions in the gas transmission network. The developed simulation model gives realistic results in several problems studied, vulnerability analysis being one of them. The vulnerability identification algorithm is used for finding a combination of component failures leading to the most significant security of supply disruptions. The paper presents a test study case of the European gas transmission network represented by a stochastic flow network in which elements can randomly fail with given failure probabilities. Failure modelling of key gas transmission network components (pipelines, LNG terminals and compressor stations) is presented and discussed. Although the network is quite resistant to any single component failure, the results indicate that a simultaneous failu...

Posted Content
TL;DR: This paper identifies a new vulnerability in all existing logic locking schemes, formalizes a precise notion of security for logic locking, and devise a new logic locking procedure, Meerkat, that guarantees that the locked chip reveals no information about the key or the designer's intended functionality.
Abstract: Chip designers outsource chip fabrication to external foundries, but at the risk of IP theft. Logic locking, a promising solution to mitigate this threat, adds extra logic gates (key gates) and inputs (key bits) to the chip so that it functions correctly only when the correct key, known only to the designer but not the foundry, is applied. In this paper, we identify a new vulnerability in all existing logic locking schemes. Prior attacks on logic locking have assumed that, in addition to the design of the locked chip, the attacker has access to a working copy of the chip. Our attack does not require a working copy and yet we successfully recover a significant fraction of key bits from the design of the locked chip only. Empirically, we demonstrate the success of our attack on eight large benchmark circuits from a benchmark suite that has been tailored specifically for logic synthesis research, for two different logic locking schemes. Then, to address this vulnerability, we initiate the study of provably secure logic locking mechanisms. We formalize, for the first time to our knowledge, a precise notion of security for logic locking. We establish that any locking procedure that is secure under our definition is guaranteed to counter our desynthesis attack, and all other such known attacks. We then devise a new logic locking procedure, Meerkat, that guarantees that the locked chip reveals no information about the key or the designer's intended functionality. A main insight behind Meerkat is that canonical representations of boolean functionality via Reduced Ordered Binary Decision Diagrams (ROBDDs) can be leveraged effectively to provide security. We analyze Meerkat with regards to its security properties and the overhead it incurs. As such, our work is a contribution to both the foundations and practice of securing digital ICs.