Biblio

While much of the discussion around supply chain security has focused on the parts, components and gear that make up an organization's physical IT assets, a growing number of experts are making the case that vulnerabilities in the software supply chain may represent the larger cybersecurity threat over the long haul.

Single sign-on (SSO) is becoming more and more popular in the Internet. An SSO ticket issued by the identity provider (IdP) allows an entity to sign onto a relying party (RP) on behalf of the account enclosed in the ticket. To ensure its authenticity, an SSO ticket is digitally signed by the IdP and verified by the RP. However, recent security incidents indicate that a signing system (e.g., certification authority) might be compromised to sign fraudulent messages, even when it is well protected in accredited commercial systems. Compared with certification authorities, the online signing components of IdPs are even more exposed to adversaries and thus more vulnerable to such threats in practice. This paper proposes ticket transparency to provide accountable SSO services with privacy-preserving public logs against potentially fraudulent tickets issued by a compromised IdP. With this scheme, an IdP-signed ticket is accepted by the RP only if it is recorded in the public logs. It enables a user to check all his tickets in the public logs and detect any fraudulent ticket issued without his participation or authorization. We integrate blind signatures, identity-based encryption and Bloom filters in the design, to balance transparency, privacy and efficiency in these security-enhanced SSO services. To the best of our knowledge, this is the first attempt to solve the security problems caused by potentially intruded or compromised IdPs in the SSO services.

In today's world, it's almost impossible to find a sphere of human life in which information technologies would not be used. On the one hand, it simplifies human life - virtually everyone carries a mini-computer in his pocket and it allows to perform many operations, that took a lot of time, in minutes. In addition, IT has simplified and promptly developed areas such as medicine, banking, document circulation, military, and many other infrastructures of the state. Nevertheless, even today, privacy remains a major problem in many information transactions. One of the most important directions for ensuring the information confidentiality in open communication networks has been and remains its protection by cryptographic methods. Although it is known that traditional cryptography methods give reasons to doubt in their reliability, quantum cryptography has proven itself as a more reliable information security technology. As far is it quite new direction there is no sufficiently complete classification of attacks on quantum cryptography methods, in view of this new extended classification of attacks on quantum protocols and quantum cryptosystems is proposed in this work. Classification takes into account the newest attacks (which use devices loopholes) on quantum key distribution equipment. These attacks have been named \textbackslashtextless; \textbackslashtextless; quantum hacking\textbackslashtextgreater\textbackslashtextgreater. Such classification may be useful for choosing commercially available quantum key distribution system. Also abstract model of eavesdropper in quantum systems was created and it allows to determine a set of various nature measures that need to be further implemented to provide reliable security with the help of specific quantum systems.

DDoS attacks are a significant threat to internet service or infrastructure providers. This poster presents an FPGA-accelerated device and DDoS mitigation technique to overcome such attacks. Our work addresses amplification attacks whose goal is to generate enough traffic to saturate the victims links. The main idea of the device is to efficiently filter malicious traffic at high-speeds directly in the backbone infrastructure before it even reaches the victim's network. We implemented our solution for two FPGA platforms using the high-level description in P4, and we report on its performance in terms of throughput and hardware resources.

The article deals with the aspects of IT-security of business processes, using a variety of methodological tools, including Integrated Management Systems. Currently, all IMS consist of at least 2 management systems, including the IT-Security Management System. Typically, these IMS cover biggest part of the company business processes, but in practice, there are examples of different scales, even within a single facility. However, it should be recognized that the total number of such projects both in the Russian Federation and in the World is small. The security of business processes will be considered on the example of the incident of Norsk Hydro. In the article the main conclusions are given to confirm the possibility of security, continuity and recovery of critical business processes on the example of this incident.

Anomaly-based Detection Systems (ADSs) attempt to learn the features of behaviors and events of a system and/or users over a period to build a profile of normal behaviors. There has been a growing interest in ADSs and typically conceived as more powerful systems One of the important factors for ADSs is an ability to distinguish between normal and abnormal behaviors in a given period. However, it is getting complicated due to the dynamic network environment that changes every minute. It is dangerous to distinguish between normal and abnormal behaviors with a fixed threshold in a dynamic environment because it cannot guarantee the threshold is always an indication of normal behaviors. In this paper, we propose an adaptive threshold for a dynamic environment with a trust management scheme for efficiently managing the profiles of normal and abnormal behaviors. Based on the assumption of the statistical analysis-based ADS that normal data instances occur in high probability regions while malicious data instances occur in low probability regions of a stochastic model, we set two adaptive thresholds for normal and abnormal behaviors. The behaviors between the two thresholds are classified as suspicious behaviors, and they are efficiently evaluated with a trust management scheme.

This work describes the self-healing systems and their benefits in the power distribution networks, with the objective of indicating which manual switch should become, as a matter of priority, automatic. The computational tool used is based on graph theory, genetic algorithms and multicriteria evaluation. There are benefits for consumers, that will benefit from a more reliable and stable system, and for the utility, that can reduce costs with team field and financial compensations payed to consumers in case of continuity indexes violation. Data from a real distribution network from the state of Sao Paulo will be used as a case study for the application of the methodology.

The paper describes modification of the ATA (Attack Tree Analysis) technique for assessment of instrumentation and control systems (ICS) dependability (reliability, availability and cyber security) called AvTA (Availability Tree Analysis). The techniques FMEA, FMECA and IMECA applied to carry out preliminary semi-formal and criticality oriented analysis before AvTA based assessment are described. AvTA models combine reliability and cyber security subtrees considering probabilities of ICS recovery in case of hardware (physical) and software (design) failures and attacks on components casing failures. Successful recovery events (SREs) avoid corresponding failures in tree using OR gates if probabilities of SRE for assumed time are more than required. Case for dependability AvTA based assessment (model, availability function and technology of decision-making for choice of component and system parameters) for smart building ICS (Building Automation Systems, BAS) is discussed.

For a long time, the most important approach for detecting malicious applications was the use of static, hash-based signatures. This approach provides a fast response time, has a low performance overhead and is very stable due to its simplicity. However, with the rapid growth in the number of malware, as well as their increased complexity in terms of polymorphism and evasion, the era of reactive security solutions started to fade in favor of new, proactive approaches such as behavior based detection. We propose a novel approach that uses an interpreter virtual machine to run proactive behavior heuristics from bytecode signatures, thus combining the advantages of behavior based detection with those of signatures. Based on our approximation, using this approach we succeeded to reduce by 85% the time required to update a behavior based detection solution to detect new threats, while continuing to benefit from the versatility of behavior heuristics.

This paper presents a study that examines whether social agents can elicit the social emotion shame as humans do. For that, we use job interviews, which are highly evaluative situations per se. We vary the interview style (shame-eliciting vs. neutral) and the job interviewer (human vs. social agent). Our dependent variables include observational data regarding the social signals of shame and shame regulation as well as self-assessment questionnaires regarding the felt uneasiness and discomfort in the situation. Our results indicate that social agents can elicit shame to the same amount as humans. This gives insights about the impact of social agents on users and the emotional connection between them.

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.

This paper presents a computational model for managing an Embodied Conversational Agent's first impressions of warmth and competence towards the user. These impressions are important to manage because they can impact users' perception of the agent and their willingness to continue the interaction with the agent. The model aims at detecting user's impression of the agent and producing appropriate agent's verbal and nonverbal behaviours in order to maintain a positive impression of warmth and competence. User's impressions are recognized using a machine learning approach with facial expressions (action units) which are important indicators of users' affective states and intentions. The agent adapts in real-time its verbal and nonverbal behaviour, with a reinforcement learning algorithm that takes user's impressions as reward to select the most appropriate combination of verbal and non-verbal behaviour to perform. A user study to test the model in a contextualized interaction with users is also presented. Our hypotheses are that users' ratings differs when the agents adapts its behaviour according to our reinforcement learning algorithm, compared to when the agent does not adapt its behaviour to user's reactions (i.e., when it randomly selects its behaviours). The study shows a general tendency for the agent to perform better when using our model than in the random condition. Significant results shows that user's ratings about agent's warmth are influenced by their a-priori about virtual characters, as well as that users' judged the agent as more competent when it adapted its behaviour compared to random condition.

The virtual world does not observe national borders, has no uniform legal system, and does not have a common perception of security and privacy issues. It is however, relatively homogenous in terms of technology.A cyberattack on an energy delivery system can have significant impacts on the availability of a system to perform critical functions as well as the integrity of the system and the confidentiality of sensitive information.

We study a model where a data collector obtains data from users through a payment mechanism to learn the underlying state from the elicited data. The private signal of each user represents her individual knowledge about the state. Through social interactions, each user can also learn noisy versions of her friends' signals, which is called group signals. Based on both her private signal and group signals, each user makes strategic decisions to report a privacy-preserved version of her data to the data collector. We develop a Bayesian game theoretic framework to study the impact of social learning on users' data reporting strategies and devise the payment mechanism for the data collector accordingly. Our findings reveal that, the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy. A generalized majority voting rule is applied by each user to her noisy group signals to determine which strategy to follow. When a user plays the ND strategy, she reports privacy-preserving data completely based on her group signals, independent of her private signal, which indicates that her privacy cost is zero. Both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation at a given payment budget. We derive bounds on the minimum total payment required to achieve a given level of state estimation accuracy.

The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological and sociological theory.

Image forensics is an increasingly relevant problem, as it can potentially address online disinformation campaigns and mitigate problematic aspects of social media. Of particular interest, given its recent successes, is the detection of imagery produced by Generative Adversarial Networks (GANs), e.g. `deepfakes'. Leveraging large training sets and extensive computing resources, recent GANs can be trained to generate synthetic imagery which is (in some ways) indistinguishable from real imagery. We analyze the structure of the generating network of a popular GAN implementation [1], and show that the network's treatment of exposure is markedly different from a real camera. We further show that this cue can be used to distinguish GAN-generated imagery from camera imagery, including effective discrimination between GAN imagery and real camera images used to train the GAN.

Keystroke Dynamics is the study of typing patterns and rhythm for personal identification and traits. Keystrokes may be analysed as fixed text such as passwords or as continuous typed text such as documents. This paper reviews different classification metrics for continuous text, such as the A and R metrics, Canberra, Manhattan and Euclidean and introduces a variant of the Minkowski distance. To test the metrics, we adopted a substantial dataset containing 239 thousand records acquired under real, harsh, and unidealised conditions. We propose a new parameter for the Minkowski metric, and we reinforce another for the A metric, as initially stated by its authors.

Due to globalization of Integrated Circuit (IC) design flow, Intellectual Property (IP) cores have increasingly become susceptible to various hardware threats such as Trojan insertion, piracy, overbuilding etc. An IP core can be secured against these threats using functional obfuscation based security mechanism. This paper presents a functional obfuscation of digital signal processing (DSP) core for consumer electronics systems using a novel IP core locking block (ILB) logic that leverages the structure of flip-flops and combinational circuits. These ILBs perform the locking of the functionality of a DSP design and actuate the correct functionality only on application of a valid key sequence. In existing approaches so far, executing exhaustive trials are sufficient to extract the valid keys from an obfuscated design. However, proposed work is capable of hindering the extraction of valid keys even on exhaustive trials, unless successfully applied in the first attempt only. In other words, the proposed work drastically reduces the probability of obtaining valid key of a functionally obfuscated design in exhaustive trials. Experimental results indicate that the proposed approach achieves higher security and lower design overhead than previous works.

Network lifetime and energy consumption of sensor nodes have an inversely proportional relationship. Thus, it is important to ensure source location privacy (SLP) routing schemes are energy-efficient. This work performs an experimental evaluation of some existing routing schemes and proposes a new angle-based routing algorithm to modify the schemes. The dynamic route creation process of the modified schemes is characterized by processes which include determination of route and banned regions and computation of control angle and lead factor parameters. Results of the analysis show that the modified schemes are effective at obfuscating the adversaries to provide strong SLP protection. Furthermore, the modified schemes consume relatively lower energy and guarantee longer network lifetime.

The H2020 European research project GHOST - Safe-Guarding Home IoT Environments with Personalised Real-time Risk Control - aims to deploy a highly effective security framework for IoT smart home residents through a novel reference architecture for user-centric cyber security in smart homes providing an unobtrusive and user-comprehensible solution. The aforementioned security framework leads to a transparent cyber security environment by increasing the effectiveness of the existing cyber security services and enhancing system's self-defence through disruptive software-enabled network security solutions. In this paper, GHOST security framework for IoT-based smart homes is presented. It is aiming to address the security challenges posed by several types of attacks, such as network, device and software. The effective design of the overall multi-layered architecture is analysed, with particular emphasis given to the integration aspects through dynamic and re-configurable solutions and the features provided by each one of the architectural layers. Additionally, real-life trials and the associated use cases are described showcasing the competences and potential of the proposed framework.

In recent years the use of security gateways (SG) located within the electrical grid distribution network has become pervasive. SGs in substations and renewable distributed energy resource aggregators (DERAs) protect power distribution control devices from cyber and cyber-physical attacks. When encrypted communications within a DER network is used, TCP/IP packet inspection is restricted to packet header behavioral analysis which in most cases only allows the SG to perform anomaly detection of blocks of time-series data (event windows). Packet header anomaly detection calculates the probability of the presence of a threat within an event window, but fails in such cases where the unreadable encrypted payload contains the attack content. The SG system log (syslog) is a time-series record of behavioral patterns of network users and processes accessing and transferring data through the SG network interfaces. Threatening behavioral pattern in the syslog are measurable using both anomaly detection and graph theory. In this paper it will be shown that it is possible to efficiently detect the presence of and classify a potential threat within an SG syslog using light-weight anomaly detection and graph theory.

Safety is one of basic human needs so we need a security system that able to prevent crime happens. Commonly, we use surveillance video to watch environment and human behaviour in a location. However, the surveillance video can only used to record images or videos with no additional information. Therefore we need more advanced camera to get another additional information such as human position and movement. This research were able to extract those information from surveillance video footage by using human detection and tracking algorithm. The human detection framework is based on Deep Learning Convolutional Neural Networks which is a very popular branch of artificial intelligence. For tracking algorithms, channel and spatial correlation filter is used to track detected human. This system will generate and export tracked movement on footage as an additional information. This tracked movement can be analysed furthermore for another research on surveillance video problems.

In this research project, we are interested by finding solutions to the problem of image analysis and processing in the encrypted domain. For security reasons, more and more digital data are transferred or stored in the encrypted domain. However, during the transmission or the archiving of encrypted images, it is often necessary to analyze or process them, without knowing the original content or the secret key used during the encryption phase. We propose to work on this problem, by associating theoretical aspects with numerous applications. Our main contributions concern: data hiding in encrypted images, correction of noisy encrypted images, recompression of crypto-compressed images and secret image sharing.

As the number of Internet users increases, their usage also diversifies, and it is important to prevent Identity on the Internet (Digital Identity) from being violated. Unauthorized authentication is one of the methods to infringe Digital Identity. Multi-factor authentication has been proposed as a method for preventing unauthorized authentication. However, the cryptographic authenticator required for multi-factor authentication is expensive both financially and UX-wise for the user. In this paper, we design, implement and evaluate multi-factor authentication using My Number Card provided by public personal identification service and WebUSB, which is being standardized.

Software security is becoming a high priority for both large companies and start-ups alike due to the increasing potential for harm that vulnerabilities and breaches carry with them. However, attaining robust security assurance while delivering features requires a precarious balancing act in the context of agile development practices. One path forward to help aid development teams in securing their software products is through the design and development of security-focused automation. Ergo, we present a novel approach, called SecureReqNet, for automatically identifying whether issues in software issue tracking systems describe security-related content. Our approach consists of a two-phase neural net architecture that operates purely on the natural language descriptions of issues. The first phase of our approach learns high dimensional word embeddings from hundreds of thousands of vulnerability descriptions listed in the CVE database and issue descriptions extracted from open source projects. The second phase then utilizes the semantic ontology represented by these embeddings to train a convolutional neural network capable of predicting whether a given issue is security-related. We evaluated SecureReqNet by applying it to identify security-related issues from a dataset of thousands of issues mined from popular projects on GitLab and GitHub. In addition, we also applied our approach to identify security-related requirements from a commercial software project developed by a major telecommunication company. Our preliminary results are encouraging, with SecureReqNet achieving an accuracy of 96% on open source issues and 71.6% on industrial requirements.