Biblio

IEC 61850 is an international standard that is widely used in substation automation systems (SAS) in smart grids. During its development, security was not considered thus leaving SAS vulnerable to attacks from adversaries. IEC 62351 was developed to provide security recommendations for SAS against (distributed) denial-of-service, replay, alteration, spoofing and detection of devices attacks. However, real-time communications, which require protocols such as Generic Object-Oriented Substation Event (GOOSE) to function efficiently, cannot implement these recommendations due to latency constraints. There has been researching that sought to improve the security of GOOSE messages, however, some cannot be practically implemented due to hardware requirements while others are theoretical, even though latency requirements were met. This research investigates the possibility of encrypting GOOSE messages with One- Time Pads (OTP), leveraging the fact that encryption/decryption processes require the random generation of OTPs and modulo addition (XOR), which could be a realistic approach to secure GOOSE while maintaining latency requirements. Results show that GOOSE messages can be encrypted with some future work required.

The healthcare sector is exploring the incorporation of digital solutions in order to improve access, reduce costs, increase quality and enhance their capacity in reaching a higher number of citizens. However, this opens healthcare organisations' systems to external elements used within or beyond their premises, new risks and vulnerabilities in what regards cyber threats and incidents. We propose the creation of a Security Assessment as a Service (SAaaS) crosslayered system that is able to identify vulnerabilities and proactively assess and mitigate threats in an IT healthcare ecosystem exposed to external devices and interfaces, considering that most users are not experts (even technologically illiterate") in cyber security and, thus, unaware of security tactics or policies whatsoever. The SAaaS can be integrated in an IT healthcare environment allowing the monitoring of existing and new devices, the limitation of connectivity and privileges to new devices, assess a device's cybersecurity risk and - based on the device's behaviour - the assignment and revoking of privileges. The SAaaS brings a controlled cyber aware environment that assures security, confidentiality and trust, even in the presence of non-trusted devices and environments.

Computer security has gained more and more attention in a public over the last years, since computer systems are suffering from significant and increasing security threats that cause security breaches by exploiting software vulnerabilities. The most efficient way to ensure the system security is to patch the vulnerable system before a malicious attack occurs. Besides the commonly-used push-type patch management, the pull-type patch management is also adopted. The main issues in the pull-type patch management are two-fold; when to check the vulnerability information and when to apply a patch? This paper considers the security patch management for a virtual machine (VM) based intrusion tolerant system (ITS), where the system undergoes the patch management with a periodic vulnerability checking strategy, and evaluates the system security from the availability aspect. A composite stochastic reward net (SRN) model is applied to capture the attack behavior of adversary and the defense behaviors of system. Two availability measures; interval availability and point-wise availability are formulated to quantify the system security via phase expansion. The proposed approach and metrics not only enable us to quantitatively assess the system security, but also provide insights on the patch management. In numerical experiments, we evaluate effects of the intrusion rate and the number of vulnerability checking on the system security.

The increase of the digitalization taking place in various industrial domains is leading developers towards the design and implementation of more and more complex networked control systems (NCS) supported by Wireless Sensor Networks (WSN). This naturally raises new challenges for the current WSN technology, namely in what concerns improved guarantees of technical aspects such as real-time communications together with safe and secure transmissions. Notably, in what concerns security aspects, several cryptographic protocols have been proposed. Since the design of these protocols is usually error-prone, security breaches can still be exposed and MALICIOUSly exploited unless they are rigorously analyzed and verified. In this paper we formally verify, using ProVerif, three cryptographic protocols used in WSN, regarding the security properties of secrecy and authenticity. The security analysis performed in this paper is more robust than the ones performed in related work. Our contributions involve analyzing protocols that were modeled considering an unbounded number of participants and actions, and also the use of a hierarchical system to classify the authenticity results. Our verification shows that the three analyzed protocols guarantee secrecy, but can only provide authenticity in specific scenarios.

In this paper, we show how practical the little theorem of witness functions is in detecting security flaws in some categories of cryptographic protocols. We convey a formal analysis of the Needham-Schroeder symmetric-key protocol in the theory of witness functions. We show how it helps to warn about a security vulnerability in a given step of this protocol where the value of security of a sensitive ticket in a sent message unexpectedly decreases compared with its value when received. This vulnerability may be exploited by an intruder to mount a replay attack as described by Denning and Sacco.

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.

Blockchain may have a potential to prove its value for the new US FDA regulatory requirements defined in the Drug Supply Chain Security Act (DSCSA) as innovative solutions are needed to support the highly complex pharmaceutical industry supply chain as it seeks to comply. In this paper, we examine how blockchain can be applied to meet with the security compliance requirement for the pharmaceutical supply chain. We explore the online playground of Hyperledger Composer, a set of tools for building blockchain business networks, to model the data and access control rules for the drug supply chain. Our experiment shows that this solution can provide a prototyping opportunity for compliance checking with certain limitations.

With the latest development of hydroelectric power generation system, the industrial control network system of hydroelectric power generation has undergone the transformation from the dedicated network, using proprietary protocols to an increasingly open network, adopting standard protocols, and increasing integration with hydroelectric power generation system. It generally believed that with the improvement of the smart grid, the future hydroelectric power generation system will rely more on the powerful network system. The general application of standardized communication protocol and intelligent electronic equipment in industrial control network provides a technical guarantee for realizing the intellectualization of hydroelectric power generation system but also brings about the network security problems that cannot be ignored. In order to solve the vulnerability of the system, we analyze and quantitatively evaluate the industrial control network of hydropower generation as a whole, and propose a set of attack and defense strategies. The method of vulnerability assessment with high diversity score proposed by us avoids the indifference of different vulnerability score to the greatest extent. At the same time, we propose an optimal attack and defense decision algorithm, which generates the optimal attack and defense strategy. The work of this paper can distinguish the actual hazards of vulnerable points more effectively.

Industrial networks are the cornerstone of modern industrial control systems. Performing security checks of industrial communication processes helps detect unknown risks and vulnerabilities. Fuzz testing is a widely used method for performing security checks that takes advantage of automation. However, there is a big challenge to carry out security checks on industrial network due to the increasing variety and complexity of industrial communication protocols. In this case, existing approaches usually take a long time to model the protocol for generating test cases, which is labor-intensive and time-consuming. This becomes even worse when the target protocol is stateful. To help in addressing this problem, we employed a deep learning model to learn the structures of protocol frames and deal with the temporal features of stateful protocols. We propose a fuzzing framework named SeqFuzzer which automatically learns the protocol frame structures from communication traffic and generates fake but plausible messages as test cases. For proving the usability of our approach, we applied SeqFuzzer to widely-used Ethernet for Control Automation Technology (EtherCAT) devices and successfully detected several security vulnerabilities.

Data outsourcing to cloud has been a common IT practice nowadays due to its significant benefits. Meanwhile, security and privacy concerns are critical obstacles to hinder the further adoption of cloud. Although data encryption can mitigate the problem, it reduces the functionality of query processing, e.g., disabling SQL queries. Several schemes have been proposed to enable one-dimensional query on encrypted data, but multi-dimensional range query has not been well addressed. In this paper, we propose a secure and scalable scheme that can support multi-dimensional range queries over encrypted data. The proposed scheme has three salient features: (1) Privacy: the server cannot learn the contents of queries and data records during query processing. (2) Efficiency: we utilize hierarchical cubes to encode multi-dimensional data records and construct a secure tree index on top of such encoding to achieve sublinear query time. (3) Verifiability: our scheme allows users to verify the correctness and completeness of the query results to address server's malicious behaviors. We perform formal security analysis and comprehensive experimental evaluations. The results on real datasets demonstrate that our scheme achieves practical performance while guaranteeing data privacy and result integrity.

Github Gist is a service provided by Github which is used by developers to share code snippets. While sharing, developers may inadvertently introduce security smells in code snippets as well, such as hard-coded passwords. Security smells are recurrent coding patterns that are indicative of security weaknesses, which could potentially lead to security breaches. The goal of this paper is to help software practitioners avoid insecure coding practices through an empirical study of security smells in publicly-available GitHub Gists. Through static analysis, we found 13 types of security smells with 4,403 occurrences in 5,822 publicly-available Python Gists. 1,817 of those Gists, which is around 31%, have at least one security smell including 689 instances of hard-coded secrets. We also found no significance relation between the presence of these security smells and the reputation of the Gist author. Based on our findings, we advocate for increased awareness and rigorous code review efforts related to software security for Github Gists so that propagation of insecure coding practices are mitigated.

The Internet of Things (IoT) and mobile systems nowadays are required to perform more intensive computation, such as facial detection, image recognition and even remote gaming, etc. Due to the limited computation performance and power budget, it is sometimes impossible to perform these workloads locally. As high-performance GPUs become more common in the cloud, offloading the computation to the cloud becomes a possible choice. However, due to the fact that offloaded workloads from different devices (belonging to different users) are being computed in the same cloud, security concerns arise. Side channel attacks on GPU systems have been widely studied, where the threat model is the attacker and the victim are running on the same operating system. Recently, major GPU vendors have provided hardware and library support to virtualize GPUs for better isolation among users. This work studies the side channel attacks from one virtual machine to another where both share the same physical GPU. We show that it is possible to infer other user's activities in this setup and can further steal others deep learning model.

In recent years, secret key generation based on physical layer security has gradually attracted high attentions. The wireless channel reciprocity and eavesdropping attack are critical problems in secret key generation studies. In this paper, we carry out a simulation and experimental study of channel reciprocity in terms of measuring channel state information (CSI) in both time division duplexing (TDD) and frequency division duplexing (FDD) modes. In simulation study, a close eavesdropping wiretap channel model is introduced to evaluate the security of the CSI by using Pearson correlation coefficient. In experimental study, an indoor wireless CSI measurement system is built with N210 and X310 universal software radio peripheral (USRP) platforms. In TDD mode, theoretical analysis and most of experimental results show that the closer eavesdropping distance, the higher CSI correlation coefficient between eavesdropping channel and legitimate channel. However, in actual environment, when eavesdropping distance is too close (less than 1/4 wavelength), this CSI correlation seriously dropped. In FDD mode, both theoretical analysis and experimental results show that the wireless channel still owns some reciprocity. When frequency interval increases, the FDD channel reciprocity in actual environment is better than that in theoretical analysis.

As the power grid becomes more interconnected the attack surface increases and determining the causes of anomalies becomes more complex. Automated responses are a mechanism which can provide resilience in a power system by responding to anomalies. An automated response system can make intelligent decisions when paired with an automated health assessment system which includes a human in the loop for making critical decisions. Effective responses can be determined by developing a matrix which considers the likely impacts on resilience if a response is taken. A testbed assists to analyze these responses and determine their effects on system resilience.

Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges.

CPS is generally complex to study, analyze, and design, as an important means to ensure the correctness of design and implementation of CPS system, simulation test is difficult to fully test, verify and evaluate the components or subsystems in the CPS system due to the inconsistent development progress of the com-ponents or subsystems in the CPS system. To address this prob-lem, we designed a hybrid P2P based collaborative simulation test framework composed of full physical nodes, hardware in the loop(HIL) nodes and full digital nodes to simulate the compo-nents or subsystems in the CPS system of different development progress, based on the framework, we then proposed collabora-tive simulation control strategy comprising sliding window based clock synchronization, dynamic adaptive time advancement and multi-priority task scheduling with preemptive time threshold. Experiments showed that the hybrid collaborative simulation testing method proposed in this paper can fully test CPS more effectively.

Series-connected IGBTs, when properly controlled, operate similarly to a single device with a much higher voltage capacity. Integrating series IGBTs into a Modular Multilevel Converter (MMC) can reduce its complexity without compromising the voltage capacity. This paper presents the circuit design on the sub-modular level of a MMC in which all the switching devices are series-connected IGBTs. The voltage sharing among the series IGBTs are regulated in a self-balancing manner. Therefore, no central series IGBT controller is needed, which greatly reduces the sensing and communication complexities, increasing the flexibility and expandability. Hardware experiment results demonstrate that the series IGBTs are able to self-regulate the voltage sharing in a fast and accurate manner and the system can operate similarly to a sub-module in a MMC.

SYN flood attack is a very serious cause for disturbing the normal traffic in MANET. SYN flood attack takes advantage of the congestion caused by populating a specific route with unwanted traffic that results in the denial of services. In this paper, we proposed an Adaptive Detection Mechanism using Artificial Intelligence technique named as SYN Flood Attack Detection Based on Bayes Estimator (SFADBE) for Mobile ad hoc Network (MANET). In SFADBE, every node will gather the current information of the available channel and the secure and congested free (Best Path) channel for the traffic is selected. Due to constant congestion, the availability of the data path can be the cause of SYN Flood attack. By using this AI technique, we experienced the SYN Flood detection probability more than the others did. Simulation results show that our proposed SFADBE algorithm is low cost and robust as compared to the other existing approaches.

Current testing for Deep Neural Networks (DNNs) focuses on quantity of test cases but ignores diversity. To the best of our knowledge, DeepXplore is the first white-box framework for Deep Learning testing by triggering differential behaviors between multiple DNNs and increasing neuron coverage to improve diversity. Since it is based on multiple DNNs facing problems that (1) the framework is not friendly to a single DNN, (2) if incorrect predictions made by all DNNs simultaneously, DeepXplore cannot generate test cases. This paper presents Test4Deep, a white-box testing framework based on a single DNN. Test4Deep avoids mistakes of multiple DNNs by inducing inconsistencies between predicted labels of original inputs and that of generated test inputs. Meanwhile, Test4Deep improves neuron coverage to capture more diversity by attempting to activate more inactivated neurons. The proposed method was evaluated on three popular datasets with nine DNNs. Compared to DeepXplore, Test4Deep produced average 4.59% (maximum 10.49%) more test cases that all found errors and faults of DNNs. These test cases got 19.57% more diversity increment and 25.88% increment of neuron coverage. Test4Deep can further be used to improve the accuracy of DNNs by average up to 5.72% (maximum 7.0%).

Blockchain networks which employ Proof-of-Work in their consensus mechanism may face inconsistencies in the form of forks. These forks are usually resolved through the application of block selection rules (such as the Nakamoto consensus). In this paper, we investigate the cause and length of forks for the Bitcoin network. We develop theoretical formulas which model the Bitcoin consensus and network protocols, based on an Erdös-Rényi random graph construction of the overlay network of peers. Our theoretical model addresses the effect of key parameters on the fork occurrence probability, such as block propagation delay, network bandwidth, and block size. We also leverage this model to estimate the weight of fork branches. Our model is implemented using the network simulator OMNET++ and validated by historical Bitcoin data. We show that under current conditions, Bitcoin will not benefit from increasing the number of connections per node.

Single sign-on (SSO) becomes popular as the identity management and authentication infrastructure in the Internet. A user receives an SSO ticket after being authenticated by the identity provider (IdP), and this IdP-issued ticket enables him to sign onto the relying party (RP). However, there are vulnerabilities (e.g., Golden SAML) that allow attackers to arbitrarily issue SSO tickets and then sign onto any RP on behalf of any user. Meanwhile, several incidents of certification authorities (CAs) also indicate that the trusted third party of security services is not so trustworthy as expected, and fraudulent TLS server certificates are signed by compromised or deceived CAs to launch TLS man-in-the-middle attacks. Various approaches are then proposed to tame the absolute authority of (compromised) CAs, to detect or prevent fraudulent TLS server certificates in the TLS handshakes. The trust model of SSO services is similar to that of certificate services. So this paper investigates the defense strategies of these trust-enhancements of certificate services, and attempts to apply these strategies to SSO to derive the trust-enhancements applicable in the SSO services. Our analysis derives (a) some security designs which have been commonly-used in the SSO services or non-SSO authentication services, and (b) two schemes effectively improving the trustworthiness of SSO services, which are not widely discussed or adopted.

The Internet of Things (IoT) is an emerging technology that plays a vital role in interconnecting various objects into a network to provide desired services within its resource constrained characteristics. In IoT, the Routing Protocol for Low power and Lossy network (RPL) is the standardized proactive routing protocol that achieves satisfying resource consumption, but it does not consider the node's routing behavior for forwarding data packets. The malicious intruders exploit these loopholes for launching various forms of routing attacks. Different security mechanisms have been introduced for detecting these attacks singly. However, the launch of multiple attacks such as Rank attack and Sybil attacks simultaneously in the IoT network is one of the devastating and destructive situations. This problem can be solved by establishing secure routing with trustworthy nodes. The trustworthiness of the nodes is determined using trust evaluation methods, where the parameters considered are based on the factors that influence in detecting the attacks. In this work, Providing Routing Security using the Technique of Collective Trust (PROTECT) mechanism is introduced, and it aims to provide a secure RPL routing by simultaneously detecting both Rank and Sybil attacks in the network. The advantage of the proposed scheme is highlighted by comparing its performance with the performance of the Sec-Trust protocol in terms of detection accuracy, energy consumption, and throughput.

In recent years, mobile social networks (MSNs) have developed rapidly and their application fields are becoming more and more widespread. Due to the continuous movement of nodes in mobile social networks, the network topology is very unstable. How to ensure the credibility of network communication is a subject worth studying. In this paper, based on the characteristics of mobile social networks, the definition of trust level is introduced into the DSR routing protocol, and a trusted DSR routing mechanism (TDR) is proposed. The scheme combines the sliding window model to design the calculation method of trust level between nodes and path trust level. The nodes in the network participate in the routing process according to their trust level. When the source node receives multiple routes carried by the response, the appropriate trusted path is selected according to the path trust level. Through simulation analysis, compared with the original DSR protocol, the TDR protocol improves the performance of average delay, route cost and packet delivery fraction, and verifies the reliability and credibility of the TDR protocol.

Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.

This study has built a simulation of a smart home system by the Alibaba ECS. The architecture of hardware was based on edge computing technology. The whole method would design a clear classifier to find the boundary between regular and mutation codes. It could be applied in the detection of the mutation code of network. The project has used the dataset vector to divide them into positive and negative type, and the final result has shown the RBF-function SVM method perform best in this mission. This research has got a good network security detection in the IoT systems and increased the applications of machine learning.