Biblio

Modern power systems today are protected and controlled increasingly by embedded systems of computing technologies with a great degree of collaboration enabled by communication. Energy cyber-physical systems such as power systems infrastructures are increasingly vulnerable to cyber-attacks on the protection and control layer. We present a method of securing protective relays from malicious change in protective relay settings via collaboration of devices. Each device checks the proposed setting changes of its neighboring devices for consistency and coordination with its own settings using setting rules based on relay coordination principles. The method is enabled via peer-to-peer communication between IEDs. It is validated in a cyber-physical test bed containing a real time digital simulator and actual relays that communicate via IEC 61850 GOOSE messages. Test results showed improvement in cyber physical security by using domain based rules to block malicious changes in protection settings caused by simulated cyber-attacks. The method promotes the use of defense systems that are aware of the physical systems which they are designed to secure.

Spam emails have been a chronic issue in computer security. They are very costly economically and extremely dangerous for computers and networks. Despite of the emergence of social networks and other Internet based information exchange venues, dependence on email communication has increased over the years and this dependence has resulted in an urgent need to improve spam filters. Although many spam filters have been created to help prevent these spam emails from entering a user's inbox, there is a lack or research focusing on text modifications. Currently, Naive Bayes is one of the most popular methods of spam classification because of its simplicity and efficiency. Naive Bayes is also very accurate; however, it is unable to correctly classify emails when they contain leetspeak or diacritics. Thus, in this proposes, we implemented a novel algorithm for enhancing the accuracy of the Naive Bayes Spam Filter so that it can detect text modifications and correctly classify the email as spam or ham. Our Python algorithm combines semantic based, keyword based, and machine learning algorithms to increase the accuracy of Naive Bayes compared to Spamassassin by over two hundred percent. Additionally, we have discovered a relationship between the length of the email and the spam score, indicating that Bayesian Poisoning, a controversial topic, is actually a real phenomenon and utilized by spammers.

Hybrid attack graphs are a powerful tool when analyzing the cybersecurity of a cyber-physical system. However, it is important to ensure that this tool correctly models reality, particularly when modelling safety-critical applications, such as a nuclear reactor. By automatically verifying that a simulation reaches the state predicted by an attack graph by analyzing the final state of the simulation, this verification procedure can be accomplished. As such, a mechanism to estimate if a simulation reaches the expected state in a hybrid attack graph is proposed here for the nuclear reactor domain.

Traditional firewalls, Intrusion Detection Systems(IDS) and network analytics tools extensively use the `flow' connection concept, consisting of five `tuples' of source and destination IP, ports and protocol type, for classification and management of network activities. By analysing flows, information can be obtained from TCP/IP fields and packet content to give an understanding of what is being transferred within a single connection. As networks have evolved to incorporate more connections and greater bandwidth, particularly from ``always on'' IoT devices and video and data streaming, so too have malicious network threats, whose communication methods have increased in sophistication. As a result, the concept of the 5 tuple flow in isolation is unable to detect such threats and malicious behaviours. This is due to factors such as the length of time and data required to understand the network traffic behaviour, which cannot be accomplished by observing a single connection. To alleviate this issue, this paper proposes the use of additional, two tuple and single tuple flow types to associate multiple 5 tuple communications, with generated metadata used to profile individual connnection behaviour. This proposed approach enables advanced linking of different connections and behaviours, developing a clearer picture as to what network activities have been taking place over a prolonged period of time. To demonstrate the capability of this approach, an expert system rule set has been developed to detect the presence of a multi-peered ZeuS botnet, which communicates by making multiple connections with multiple hosts, thus undetectable to standard IDS systems observing 5 tuple flow types in isolation. Finally, as the solution is rule based, this implementation operates in realtime and does not require post-processing and analytics of other research solutions. This paper aims to demonstrate possible applications for next generation firewalls and methods to acquire additional information from network traffic.

In the smart grid, residents' electricity usage needs to be periodically measured and reported for the purpose of better energy management. At the same time, real-time collection of residents' electricity consumption may unfavorably incur privacy leakage, which has motivated the research on privacy-preserving aggregation of electricity readings. Most previous studies either rely on a trusted third party (TTP) or suffer from expensive computation. In this paper, we first reveal the privacy flaws of a very recent scheme pursing privacy preservation without relying on the TTP. By presenting concrete attacks, we show that this scheme has failed to meet the design goals. Then, for better privacy protection, we construct a new scheme called PMDA, which utilizes Shamir's secret sharing to allow smart meters to negotiate aggregation parameters in the absence of a TTP. Using only lightweight cryptography, PMDA efficiently supports multi-functional aggregation of the electricity readings, and simultaneously preserves residents' privacy. Theoretical analysis is provided with regard to PMDA's security and efficiency. Moreover, experimental data obtained from a prototype indicates that our proposal is efficient and feasible for practical deployment.

Information Centric Networking (ICN) changed the communication model from host-based to content-based to cope with the high volume of traffic due to the rapidly increasing number of users, data objects, devices, and applications. ICN communication model requires new security solutions that will be integrated with ICN architectures. In this paper, we present a security framework to manage ICN traffic by detecting, preventing, and responding to ICN attacks. The framework consists of three components: availability, access control, and privacy. The availability component ensures that contents are available for legitimate users. The access control component allows only legitimate users to get restrictedaccess contents. The privacy component prevents attackers from knowing content popularities or user requests. We also show our specific solutions as examples of the framework components.

Software defined networking (SDN) is a networking paradigm to provide automated network management at run time through network orchestration and virtualization. SDN can also enhance system resilience through recovery from failures and maintaining critical operations during cyber attacks. SDN's self-healing mechanisms can be leveraged to realized autonomous attack containment, which dynamically modifies access control rules based on configurable trust levels. In this paper, we present an approach to aid in selection of security countermeasures dynamically in an SDN enabled Energy Delivery System (EDS) and achieving tradeoff between providing security and QoS. We present the modeling of security cost based on end-to-end packet delay and throughput. We propose a non-dominated sorting based multi-objective optimization framework which can be implemented within an SDN controller to address the joint problem of optimizing between security and QoS parameters by alleviating time complexity at O(M N2), where M is the number of objective functions and N is the number of population for each generation respectively. We present simulation results which illustrate how data availability and data integrity can be achieved while maintaining QoS constraints.

In recent years, there has been progress in applying information technology to industrial control systems (ICS), which is expected to make the development cost of control devices and systems lower. On the other hand, the security threats are becoming important problems. In 2017, a command injection issue on a data logger was reported. In this paper, we focus on the risk assessment in security design for data loggers used in industrial control systems. Our aim is to provide a risk assessment method optimized for control devices and systems in such a way that one can prioritize threats more preciously, that would lead work resource (time and budget) can be assigned for more important threats than others. We discuss problems with application of the automotive-security guideline of JASO TP15002 to ICS risk assessment. Consequently, we propose a three-phase risk assessment method with a novel Risk Scoring Systems (RSS) for quantitative risk assessment, RSS-CWSS. The idea behind this method is to apply CWSS scoring systems to RSS by fixing values for some of CWSS metrics, considering what the designers can evaluate during the concept phase. Our case study with ICS employing a data logger clarifies that RSS-CWSS can offer an interesting property that it has better risk-score dispersion than the TP15002-specified RSS.

Risk Assessment is a foundation of decision-making about a future project behaviour or action. The related decision made might entail further analyzes to perform risk- reduction. The risk is a general phenomenon that takes different depicts and types. Static risk and its circumstances do not significantly change over time while dynamic risk arises out of the changes in interrelated circumstances. A Multi-Agent System (MAS) approach has become a popular tool to tackle different problems that relate to risk. The MAS helps in the decision aid processes and when responding to the consequences of the risk. This paper surveys some of the existing methods and techniques of risk assessment in different application domains. The survey focuses on the employment of MAS approach in risk assessment. The survey outcomes an illustration of the roles and contributions of the MAS in the Dynamic Risk Assessment (DRA) field.

Threat modeling is one of the best practices to secure software development. A primary challenge for using this practice is how to extract threats. Existing threat extraction methods to this purpose are mainly based on penetration tests or vulnerability databases. This imposes a non-automated timeconsuming process, which fully relies on the human knowledge and expertise. In this paper, a method is presented, which can extract the threats to a software system based on the existing description of the software behavior. We elaborately describe software behavior with sequence diagrams enriched by security relevant attributes. To enrich a sequence diagram, some attributes and their associated values are added to the diagram elements and the communication between them. We have also developed a threat knowledge base from reliable sources such as CWE and CAPEC lists. Every threat in the knowledge base is described according to its occurrence conditions in the software. To extract threats of a software system, the enriched sequence diagrams describing the software behavior are matched with the threat rules in our knowledge base using a simple inference process. Results in a set of potential threats for the software system. The proposed method is applied on a software application to extract its threats. Our case study indicates the effectiveness of the proposed method compared to other existing methods.

Smartphones have become ubiquitous in our everyday lives, providing diverse functionalities via millions of applications (apps) that are readily available. To achieve these functionalities, apps need to access and utilize potentially sensitive data, stored in the user's device. This can pose a serious threat to users' security and privacy, when considering malicious or underskilled developers. While application marketplaces, like Google Play store and Apple App store, provide factors like ratings, user reviews, and number of downloads to distinguish benign from risky apps, studies have shown that these metrics are not adequately effective. The security and privacy health of an application should also be considered to generate a more reliable and transparent trustworthiness score. In order to automate the trustworthiness assessment of mobile applications, we introduce the Trust4App framework, which not only considers the publicly available factors mentioned above, but also takes into account the Security and Privacy (S&P) health of an application. Additionally, it considers the S&P posture of a user, and provides an holistic personalized trustworthiness score. While existing automatic trustworthiness frameworks only consider trustworthiness indicators (e.g. permission usage, privacy leaks) individually, Trust4App is, to the best of our knowledge, the first framework to combine these indicators. We also implement a proof-of-concept realization of our framework and demonstrate that Trust4App provides a more comprehensive, intuitive and actionable trustworthiness assessment compared to existing approaches.

In online payment, customers must transmit their personal and financial information through the website to conclude their purchase and pay the services or items selected. They may face possible fears from online transactions raised by their risk perception about financial or privacy loss. They may have concerns over the payment decision with the possible negative behaviors such as shopping cart abandonment. Therefore, customers have three major players that need to be addressed in online payment: the online seller, the payment page, and their own perception. However, few studies have explored these three players in an online purchasing environment. In this paper, we focus on the customer concerns and examine the antecedents of trust, payment security perception as well as their joint effect on two fundamentally important customers' aspects privacy concerns and financial fear perception. A total of 392 individuals participated in an online survey. The results highlight the importance, of the seller website's components (such as ease of use, security signs, and quality information) and their impact on the perceived payment security as well as their impact on customer's trust and financial fear perception. The objective of our study is to design a research model that explains the factors contributing to an online payment decision.

With the recent advances in computing, artificial intelligence (AI) is quickly becoming a key component in the future of advanced applications. In one application in particular, AI has played a major role - that of revolutionizing traditional healthcare assistance. Using embodied interactive agents, or interactive robots, in healthcare scenarios has emerged as an innovative way to interact with patients. As an essential factor for interpersonal interaction, trust plays a crucial role in establishing and maintaining a patient-agent relationship. In this paper, we discuss a study related to healthcare in which we examine aspects of trust between humans and interactive robots during a therapy intervention in which the agent provides corrective feedback. A total of twenty participants were randomly assigned to receive corrective feedback from either a robotic agent or a human agent. Survey results indicate trust in a therapy intervention coupled with a robotic agent is comparable to that of trust in an intervention coupled with a human agent. Results also show a trend that the agent condition has a medium-sized effect on trust. In addition, we found that participants in the robot therapist condition are 3.5 times likely to have trust involved in their decision than the participants in the human therapist condition. These results indicate that the deployment of interactive robot agents in healthcare scenarios has the potential to maintain quality of health for future generations.

A mobile ad hoc network (MANET) is a collection of mobile nodes that do not need to rely on a pre-existing network infrastructure or centralized administration. Securing MANETs is a serious concern as current research on MANETs continues to progress. Each node in a MANET acts as a router, forwarding data packets for other nodes and exchanging routing information between nodes. It is this intrinsic nature that introduces the serious security issues to routing protocols. A black hole attack is one of the well-known security threats for MANETs. A black hole is a security attack in which a malicious node absorbs all data packets by sending fake routing information and drops them without forwarding them. In order to defend against a black hole attack, in this paper we propose a new threshold-based black hole attack prevention method using multiple RREPs. To investigate the performance of the proposed method, we compared it with existing methods. Our simulation results show that the proposed method outperforms existing methods from the standpoints of packet delivery rate, throughput, and routing overhead.

Recently, the armed forces want to bring the Internet of Things technology to improve the effectiveness of military operations in battlefield. So the Internet of Battlefield Things (IoBT) has entered our view. And due to the high processing latency and low reliability of the “combat cloud” network for IoBT in the battlefield environment, in this paper , a novel “combat cloud-fog” network architecture for IoBT is proposed. The novel architecture adds a fog computing layer which consists of edge network equipment close to the users in the “combat-cloud” network to reduce latency and enhance reliability. Meanwhile, since the computing capability of the fog equipment are weak, it is necessary to implement distributed computing in the “combat cloud-fog” architecture. Therefore, the distributed computing load balancing problem of the fog computing layer is researched. Moreover, a distributed generalized diffusion strategy is proposed to decrease latency and enhance the stability and survivability of the “combat cloud-fog” network system. The simulation result indicates that the load balancing strategy based on generalized diffusion algorithm could decrease the task response latency and support the efficient processing of battlefield information effectively, which is suitable for the “combat cloud- fog” network architecture.

Digital microfluidic biochips (DMFBs) have become popular in the healthcare industry recently because of its lowcost, high-throughput, and portability. Users can execute the experiments on biochips with high resolution, and the biochips market therefore grows significantly. However, malicious attackers exploit Intellectual Property (IP) piracy and Trojan attacks to gain illegal profits. The conventional approaches present defense mechanisms that target either IP piracy or Trojan attacks. In practical, DMFBs may suffer from the threat of being attacked by these two attacks at the same time. This paper presents a comprehensive security system to protect DMFBs from IP piracy and Trojan attacks. We propose an authentication mechanism to protect IP and detect errors caused by Trojans with CCD cameras. By our security system, we could generate secret keys for authentication and determine whether the bioassay is under the IP piracy and Trojan attacks. Experimental results demonstrate the efficacy of our security system without overhead of the bioassay completion time.

When robots and human users collaborate, trust is essential for user acceptance and engagement. In this paper, we investigated two factors thought to influence user trust towards a robot: preference elicitation (a combination of user involvement and explanation) and embodiment. We set our experiment in the application domain of a restaurant recommender system, assessing trust via user decision making and perceived source credibility. Previous research in this area uses simulated environments and recommender systems that present the user with the best choice from a pool of options. This experiment builds on past work in two ways: first, we strengthened the ecological validity of our experimental paradigm by incorporating perceived risk during decision making; and second, we used a system that recommends a nonoptimal choice to the user. While no effect of embodiment is found for trust, the inclusion of preference elicitation features significantly increases user trust towards the robot recommender system. These findings have implications for marketing and health promotion in relation to Human-Robot Interaction and call for further investigation into the development and maintenance of trust between robot and user.

The problem of optimal attack path analysis is one of the hotspots in network security. Many methods are available to calculate an optimal attack path, such as Q-learning algorithm, heuristic algorithms, etc. But most of them have shortcomings. Some methods can lead to the problem of path loss, and some methods render the result un-comprehensive. This article proposes an improved Monte Carlo Graph Search algorithm (IMCGS) to calculate optimal attack paths in target network. IMCGS can avoid the problem of path loss and get comprehensive results quickly. IMCGS is divided into two steps: selection and backpropagation, which is used to calculate optimal attack paths. A weight vector containing priority, host connection number, CVSS value is proposed for every host in an attack path. This vector is used to calculate the evaluation value, the total CVSS value and the average CVSS value of a path in the target network. Result for a sample test network is presented to demonstrate the capabilities of the proposed algorithm to generate optimal attack paths in one single run. The results obtained by IMCGS show good performance and are compared with Ant Colony Optimization Algorithm (ACO) and k-zero attack graph.

Control systems for critical infrastructure are becoming increasingly interconnected while cyber threats against critical infrastructure are becoming more sophisticated and difficult to defend against. Historically, cyber security has emphasized building defenses to prevent loss of confidentiality, integrity, and availability in digital information and systems, but in recent years cyber attacks have demonstrated that no system is impenetrable and that control system operation may be detrimentally impacted. Cyber resilience has emerged as a complementary priority that seeks to ensure that digital systems can maintain essential performance levels, even while capabilities are degraded by a cyber attack. This paper examines how cyber security and cyber resilience may be measured and quantified in a control system environment. Load Frequency Control is used as an illustrative example to demonstrate how cyber attacks may be represented within mathematical models of control systems, to demonstrate how these events may be quantitatively measured in terms of cyber security or cyber resilience, and the differences and similarities between the two mindsets. These results demonstrate how various metrics are applied, the extent of their usability, and how it is important to analyze cyber-physical systems in a comprehensive manner that accounts for all the various parts of the system.

We present a novel method for static analysis in which we combine data-flow analysis with machine learning to detect SQL injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities in PHP applications. We assembled a dataset from the National Vulnerability Database and the SAMATE project, containing vulnerable PHP code samples and their patched versions in which the vulnerability is solved. We extracted features from the code samples by applying data-flow analysis techniques, including reaching definitions analysis, taint analysis, and reaching constants analysis. We used these features in machine learning to train various probabilistic classifiers. To demonstrate the effectiveness of our approach, we built a tool called WIRECAML, and compared our tool to other tools for vulnerability detection in PHP code. Our tool performed best for detecting both SQLi and XSS vulnerabilities. We also tried our approach on a number of open-source software applications, and found a previously unknown vulnerability in a photo-sharing web application.

Active Noise Cancellation (ANC) is a classical area where noise in the environment is canceled by producing anti-noise signals near the human ears (e.g., in Bose's noise cancellation headphones). This paper brings IoT to active noise cancellation by combining wireless communication with acoustics. The core idea is to place an IoT device in the environment that listens to ambient sounds and forwards the sound over its wireless radio. Since wireless signals travel much faster than sound, our ear-device receives the sound in advance of its actual arrival. This serves as a glimpse into the future, that we call lookahead, and proves crucial for real-time noise cancellation, especially for unpredictable, wide-band sounds like music and speech. Using custom IoT hardware, as well as lookahead-aware cancellation algorithms, we demonstrate MUTE, a fully functional noise cancellation prototype that outperforms Bose's latest ANC headphone. Importantly, our design does not need to block the ear - the ear canal remains open, making it comfortable (and healthier) for continuous use.

Fog-Cloud framework is being regarded as a more promising technology to provide performance guarantee for IoT applications, which not only have higher requirements on computation resources, but also are delay and/or security sensitive. In this framework, a delay and security-sensitive computation task is usually divided into several sub-tasks, which could be offloaded to either fog or cloud computing servers, referred to as offloading destinations. Sub-tasks may exchange information during their processing and then have requirement on transmission bandwidth. Different destinations produce different completion delays of a sub-task, affecting the corresponding task delay. The existing offloading approaches either considered only a single type of offloading destinations or ignored delay and/or security constraint. This paper studies a computation offloading problem in the fog-cloud scenario where not only computation and security capabilities of offloading destinations may be different, but also bandwidth and delay of links may be different. We first propose a joint offloading approach by formulating the problem as a form of Mixed Integer Programming Multi-Commodity Flow to maximize the fog-cloud provider's revenue without sacrificing performance and security requirements of users. We also propose a greedy algorithm for the problem. Extensive simulation results under various network scales show that the proposed computation offloading mechanism achieves higher revenue than the conventional single-type computation offloading under delay and security constraints.

This paper is to design substitution boxes (S-Boxes) using innovative I-Ching operators (ICOs) that have evolved from ancient Chinese I-Ching philosophy. These three operators-intrication, turnover, and mutual- inherited from I-Ching are specifically designed to generate S-Boxes in cryptography. In order to analyze these three operators, identity, compositionality, and periodicity measures are developed. All three operators are only applied to change the output positions of Boolean functions. Therefore, the bijection property of S-Box is satisfied automatically. It means that our approach can avoid singular values, which is very important to generate S-Boxes. Based on the periodicity property of the ICOs, a new network is constructed, thus to be applied in the algorithm for designing S-Boxes. To examine the efficiency of our proposed approach, some commonly used criteria are adopted, such as nonlinearity, strict avalanche criterion, differential approximation probability, and linear approximation probability. The comparison results show that S-Boxes designed by applying ICOs have a higher security and better performance compared with other schemes. Furthermore, the proposed approach can also be used to other practice problems in a similar way.

The process for determining how to respond to a cyberattack involves evaluating many factors, including some with competing risks. Consequentially, decision makers in the private sector and policymakers in the U.S. government (USG) need a framework in order to make effective response decisions. The authors' research identified two competing risks: 1) the risk of not responding forcefully enough to deter a suspected attacker, and 2) responding in a manner that escalates a situation with an attacker. The authors also identified three primary factors that influence these risks: attribution confidence/time, the scale of the attack, and the relationship with the suspected attacker. This paper provides a framework to help decision makers understand how these factors interact to influence the risks associated with potential response options to cyberattacks. The views expressed do not reflect the official policy or position of the National Intelligence University, the Department of Defense, the U.S. Intelligence Community, or the U.S. Government.

Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan1 protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult, because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model. Ryoan provides a distributed sandbox, leveraging hardware enclaves (e.g., Intel’s software guard extensions (SGX) [40]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user’s input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, health analysis, image processing and machine translation.