Biblio

Internet of Things (IoT) evolution calls for stringent communication demands, including low delay and reliability. At the same time, wireless mesh technology is used to extend the communication range of IoT deployments, in a multi-hop manner. However, Wireless Mesh Networks (WMNs) are facing link failures due to unstable topologies, resulting in unsatisfied IoT requirements. Named-Data Networking (NDN) can enhance WMNs to meet such IoT requirements, thanks to the content naming scheme and in-network caching, but necessitates adaptability to the challenging conditions of WMNs.In this work, we argue that Software-Defined Networking (SDN) is an ideal solution to fill this gap and introduce an integrated SDN-NDN deployment over WMNs involving: (i) global view of the network in real-time; (ii) centralized decision making; and (iii) dynamic NDN adaptation to network changes. The proposed system is deployed and evaluated over the wiLab.1 Fed4FIRE+ test-bed. The proof-of-concept results validate that the centralized control of SDN effectively supports the NDN operation in unstable topologies with frequent dynamic changes, such as the WMNs.

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a single explanation, which fails to account for the diversity of human thoughts and experiences in language. This paper thus addresses this gap, by proposing a generative XAI framework, INTERACTION (explain aNd predicT thEn queRy with contextuAl CondiTional varIational autO-eNcoder). Our novel framework presents explanation in two steps: (step one) Explanation and Label Prediction; and (step two) Diverse Evidence Generation. We conduct intensive experiments with the Transformer architecture on a benchmark dataset, e-SNLI [1]. Our method achieves competitive or better performance against state-of-the-art baseline models on explanation generation (up to 4.7% gain in BLEU) and prediction (up to 4.4% gain in accuracy) in step one; it can also generate multiple diverse explanations in step two.

This paper continues analysis of approaches of IT risk assessment and management in modern IT management frameworks. Building on systematicity principles and the review of concepts of risk and methods of risk analysis in the frameworks, we discuss applicability of the methods for business decision-making in the real world and propose ways to their improvement.

Cyber security is reliant on the actions of both machine and human and remains a domain of importance and continual evolution. While the study of human behavior has grown, less attention has been paid to the adversarial operator. Cyber environments consist of complex and dynamic situations where decisions are made with incomplete information. In such scenarios people form strategies based on simplified models of the world and are often efficient and effective, yet may result in judgement or decision-making bias. In this paper, we examine an initial list of biases affecting adversarial cyber actors. We use subject matter experts to derive examples and demonstrate these biases likely exist, and play a role in how attackers operate.

The ternary interval number contains more comprehensive information than the exact number, and the prediction of the ternary interval number is more conducive to intelligent decision-making. In order to reduce the overfitting problem of the neural network model, a combination prediction method of the BP neural network and the matrix GM (1, 1) model for the ternary interval number sequence is proposed in the paper, and based on the proposed method to predict the passenger volume. The matrix grey model for the ternary interval number sequence (MTIGM (1, 1)) can stably predict the overall development trend of a time series. Considering the integrity of interval numbers, the BP neural network model is established by combining the lower, middle and upper boundary points of the ternary interval numbers. The combined weights of MTIGM (1, 1) and the BP neural network are determined based on the grey relational degree. The combined method is used to predict the total passenger volume and railway passenger volume of China, and the prediction effect is better than MTIGM (1, 1) and BP neural network.

This article discusses the problem of information security management in computer systems and describes the process of developing an algorithm that allows to determine measures to protect personal data. The organizational and technical measures formulated by the FSTEC are used as measures.

An effective response against information security violations in the technical systems remains relevant challenge nowadays, when their number, complexity, and the level of possible losses are growing. The violation can be caused by the set of the intruder's consistent actions. In the area of countermeasure selection for a proactive and reactive response against security violations, there are a large number of techniques. The techniques based on graph models seem to be promising. These models allow representing the set of actions caused the violation. Their advantages include the ability to forecast violations for timely decision-making on the countermeasures, as well as the ability to analyze and consider the coverage of countermeasures in terms of steps caused the violation. The paper proposes and describes a decision support method for responding against information security violations in the technical systems based on the graph models, as well as the developed models, including the countermeasure model and the graph representing the set of actions caused the information security violation.

Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. Specifically, we analyze the extent to which emotion attributes extracted from the negotiation help in the prediction, above and beyond the individual difference variables. We focus on a recent dataset in chat-based negotiations, grounded in a realistic camping scenario. We study three degrees of emotion dimensions – emoticons, lexical, and contextual by leveraging affective lexicons and a state-of-the-art deep learning architecture. Our insights will be helpful in designing adaptive negotiation agents that interact through realistic communication interfaces.

This article analyzes the possibilities of using cognitive approaches in forming expert assessments for solving information security problems. The experts use the contextual approach by A.Yu. Khrennikov’s as a basic model for the mathematical description of the quantum decision-making method. In the cognitive view, expert assessments are proposed to be considered as conditional probabilities with regard to the fulfillment of a set of certain conditions. However, the conditions in this approach are contextual, but not events like in Boolean algebra.

Human Resource (HR) Analytics has been increasingly attracted attention for a past decade. This is because the study field is adopted data-driven approaches to be processed and interpreted for meaningful insights in human resources. The field is involved in HR decision making helping to understand why people, organization, or other business performance behaved the way they do. Embracing the available tools for decision making and learning in the field of computational intelligence (CI) and Artificial Intelligence (AI) to the field of HR, this creates tremendous opportunities for HR Analytics in practical aspects. However, there are still inadequate applications in this area. This paper serves as a survey of using the tools and their applications in HR involving recruitment, retention, reward and retirement. An example of using CI and AI for career development and training in the era of disruption is conceptually proposed.

5G improves previous generations not only in terms of radio access but the whole infrastructure and services paradigm. Automation, dynamism and orchestration are now key features that allow modifying network behaviour, such as Virtual Network Functions (VNFs), and resource allocation reactively and on demand. However, such dynamic ecosystem must pay special attention to security while ensuring that the system actions are trustworthy and reliable. To this aim, this paper introduces the integration of the Manufacturer Usage Description (MUD) standard alongside a Trust and Reputation Manager (TRM) into the INSPIRE-5GPlus framework, enforcing security properties defined by MUD files while the whole infrastructure, virtual and physical, as well as security metrics are continuously audited to compute trust and reputation values. These values are later fed to enhance trustworthiness on the zero-touch decision making such as the ones orchestrating end-to-end security in a closed-loop.

Data are the basis in Digital Twin (DT) to set up bidirectional mapping between physical and virtual spaces, and realize critical environmental sensing, decision making and execution. Thus, trustworthiness is a necessity in data content as well as data operations. A dual blockchain framework is proposed to realize comprehensive data security in various DT scenarios. It is highly adaptable, scalable, evolvable, and easy to be integrated into Digital Twin Network (DTN) as enhancement.

Sharing relational databases on the Internet creates the need to protect these databases. Its output in substantial losses to the data storing systems because of unauthorized access to information that could lose novelty. The research associations use the research databases to mine new information about the research works of the relational databases that are available for free. It is a great challenge to maintain authenticity because these databases are vulnerable to security issues. Watermarking is a candidate solution that fully protects databases shared with the receiver. The protection of relational database ownership that may continue to evolve against the various aquatic mechanisms shared with the recipient that arouses appetite for attacks and must continue to evolve so that they can have database knowledge to support their decision-making system is effective. The relational database based onVirtual private key Watermarking using numeric attribute) involves embedding the same watermark in the same properties in different places in the same place. Therefore, data attackers cannot remove watermarks from data. The proposed strategy is to work by inserting watermark bits in such a way that it causes minimal distortion in the data and the data usability must remain intact after the data is watermarked. The proposed strategy is to work by inserting watermark bits in such a way that it causes minimal distortion in the data and the ability to use the data after watermarking the data must remain intact. The existence of a primary key is the main feature or compulsory item for most of the strategies. Our method provides solutions no primary key feature where the integrating search system of the database remains intact after watermarking distortion.

Data is one of the most valuable assets of an organization and has a tremendous impact on its long-term success and decision-making processes. Typically, organizational data error and outlier detection processes perform manually and reactively, making them time-consuming and prone to human errors. Additionally, rich data types, unlabeled data, and increased volume have made such data more complex. Accordingly, an automated anomaly detection approach is required to improve data management and quality control processes. This study introduces an unsupervised anomaly detection approach based on models comparison, consensus learning, and a combination of rules of thumb with iterative hyper-parameter tuning to increase data quality. Furthermore, a domain expert is considered a human in the loop to evaluate and check the data quality and to judge the output of the unsupervised model. An experiment has been conducted to assess the proposed approach in the context of a case study. The experiment results confirm that the proposed approach can improve the quality of organizational data and facilitate anomaly detection processes.

The Internet of Battlefield Things (IoBT) is a dynamically composed network of intelligent sensors and actuators that operate as a command and control, communications, computers, and intelligence complex-system with the aim to enable multi-domain operations. The use of artificial intelligence can help transform the IoBT data into actionable insight to create information and decision advantage on the battlefield. In this work, we focus on how accounting for uncertainty in IoBT systems can result in more robust and safer systems. Human trust in these systems requires the ability to understand and interpret how machines make decisions. Most real-world applications currently use deterministic machine learning techniques that cannot incorporate uncertainty. In this work, we focus on the machine learning task of classifying vehicles from their audio recordings, comparing deterministic convolutional neural networks (CNNs) with Bayesian CNNs to show that correctly estimating the uncertainty can help lead to robust decision-making in IoBT.

In this paper, we propose a method of stochastic model predictive control for energy management systems including human-in-the-loop. Here, we consider an air-conditioning system consisting of some rooms. Human decision making about the set temperature is modeled by a discrete-time Markov chain. The finite-time optimal control problem solved in the controller is reduced to a mixed integer linear programming problem.

Performing a live digital forensics investigation on a running system is challenging due to the time pressure under which decisions have to be made. Newly proliferating and frequently applied types of malware (e.g., fileless malware) increase the need to conduct digital forensic investigations in real-time. In the course of these investigations, forensic experts are confronted with a wide range of different forensic tools. The decision, which of those are suitable for the current situation, is often based on the cyber forensics experts’ experience. Currently, there is no reliable automated solution to support this decision-making. Therefore, we derive requirements for visually supporting the decision-making process for live forensic investigations and introduce a research prototype that provides visual guidance for cyber forensic experts during a live digital forensics investigation. Our prototype collects relevant core information for live digital forensics and provides visual representations for connections between occurring events, developments over time, and detailed information on specific events. To show the applicability of our approach, we analyze an exemplary use case using the prototype and demonstrate the support through our approach.

This paper studies the technology of generating DDPG (deep deterministic policy gradient) by using the deep dual network and experience pool network structure, and puts forward the sampling strategy gradient algorithm to randomly select actions according to the learned strategies (action distribution) in the continuous action space, based on the dispatching control system of the power dispatching control center of a super city power grid, According to the actual characteristics and operation needs of urban power grid, The developed refined artificial intelligence on-line security analysis and emergency response plan intelligent generation function realize the emergency response auxiliary decision-making intelligent generation function. According to the hidden danger of overload and overload found in the online safety analysis, the relevant load lines of the equipment are searched automatically. Through the topology automatic analysis, the load transfer mode is searched to eliminate or reduce the overload or overload of the equipment. For a variety of load transfer modes, the evaluation index of the scheme is established, and the optimal load transfer mode is intelligently selected. Based on the D5000 system of Metropolitan power grid, a multi-objective and multi resource coordinated security risk decision-making assistant system is implemented, which provides integrated security early warning and decision support for the main network and distribution network of city power grid. The intelligent level of power grid dispatching management and dispatching operation is improved. The state reality network can analyze the joint state observations from the action reality network, and the state estimation network uses the actor action as the input. In the continuous action space task, DDPG is better than dqn and its convergence speed is faster.

In this paper we discuss distributed denial of service attacks on software defined networks, software defined networking is a network architecture approach that enables the network to be intelligently and centrally controlled using software applications. These days the usage of internet is increased because high availability of internet and low cost devices. At the same time lot of security challenges are faced by network monitors and administrators to tackle the frequent network access by the users. The main idea of SDN is to separate the control plane and the data plane, as a result all the devices in the data plane becomes forwarding devices and all the decision making activities transferred to the centralized system called controller. Openflow is the standardized and most important protocol among many SDN protocols. In this article given the overview of distributed denial of service attacks and prevention mechanisms to these malicious attacks.

The increasingly intensive use of software systems in diverse sectors, especially in business, government, healthcare, and critical infrastructures, makes it essential to deliver code that is secure. In this work, we present two sets of experiments aiming at helping developers to improve software security from the early development stages. The first experiment is focused on using software metrics to build prediction models to distinguish vulnerable from non-vulnerable code. The second experiment studies the hypothesis of developing a consensus-based decision-making approach on top of several machine learning-based prediction models, trained using software metrics data to categorize code units with respect to their security. Such categories suggest a priority (ranking) of software code units based on the potential existence of security vulnerabilities. Results show that software metrics do not constitute sufficient evidence of security issues and cannot effectively be used to build a prediction model to distinguish vulnerable from non-vulnerable code. However, with a consensus-based decision-making approach, it is possible to classify code units from a security perspective, which allows developers to decide (considering the criticality of the system under development and the available resources) which parts of the software should be the focal point for the detection and removal of security vulnerabilities.

Physical layer authentication (PLA) has recently been discussed in the context of URLLC due to its low complexity and low overhead. Nevertheless, these schemes also introduce additional sources of error through missed detections and false alarms. The trade-offs of these characteristics are strongly dependent on the deployment scenario as well as the processing architecture. Thus, considering a feature-based PLA scheme utilizing channel-state information at multiple distributed radio-heads, we study these trade-offs analytically. We model and analyze different scenarios of centralized and decentralized decision-making and decoding, as well as the impacts of a single-antenna attacker launching a Sybil attack. Based on stochastic network calculus, we provide worst-case performance bounds on the system-level delay for the considered distributed scenarios under a Sybil attack. Results show that the arrival-rate capacity for a given latency deadline is increased for the distributed scenarios. For a clustered sensor deployment, we find that the distributed approach provides 23% higher capacity when compared to the centralized scenario.

Human-machine teaming (HMT) operates in a context defined by the mission. Varying from the complexity and disturbance in the cooperation between humans and machines, a single machine has difficulty handling work with humans in the scales of efficiency and workload. Swarm of machines provides a more feasible solution in such a mission. Human-swarm teaming (HST) extends the concept of HMT in the mission, such as persistent surveillance, search-and-rescue, warfare. Bringing the concept of HST faces several scientific challenges. For example, the strategies of allocation on the high-level decision making. Here, human usually plays the supervisory or decision making role. Performance of such fixed structure of HST in actual mission operation could be affected by the supervisor’s status from many aspects, which could be considered in three general parts: workload, situational awareness, and trust towards the robot swarm teammate and mission performance. Besides, the complexity of a single human operator in accessing multiple machine agents increases the work burdens. An interface between swarm teammates and human operators to simplify the interaction process is desired in the HST.In this paper, instead of purely considering the workload of human teammates, we propose the computational model of human swarm interaction (HSI) in the simulated map surveillance mission. UAV swarm and human supervisor are both assigned in searching a predefined area of interest (AOI). The workload allocation of map monitoring is adjusted based on the status of the human worker and swarm teammate. Workload, situation awareness ability, trust are formulated as independent models, which affect each other. A communication-aware UAV swarm persistent surveillance algorithm is assigned in the swarm autonomy portion. With the different surveillance task loads, the swarm agent’s thrust parameter adjusts the autonomy level to fit the human operator’s needs. Reinforcement learning is applied in seeking the relative balance of workload in both human and swarm sides. Metrics such as mission accomplishment rate, human supervisor performance, mission performance of UAV swarm are evaluated in the end. The simulation results show that the algorithm could learn the human-machine trust interaction to seek the workload balance to reach better mission execution performance. This work inspires us to leverage a more comprehensive HST model in more practical HMT application scenarios.

How team members are treated influences their performance in the team and their desire to be a part of the team in the future. Prior research in human-robot teamwork proposes fairness definitions for human-robot teaming that are based on the work completed by each team member. However, metrics that properly capture people’s perception of fairness in human-robot teaming remains a research gap. We present work on assessing how well objective metrics capture people’s perception of fairness. First, we extend prior fairness metrics based on team members’ capabilities and workload to a bigger team. We also develop a new metric to quantify the amount of time that the robot spends working on the same task as each person. We conduct an online user study (n=95) and show that these metrics align with perceived fairness. Importantly, we discover that there are bleed-over effects in people’s assessment of fairness. When asked to rate fairness based on the amount of time that the robot spends working with each person, participants used two factors (fairness based on the robot’s time and teammates’ capabilities). This bleed-over effect is stronger when people are asked to assess fairness based on capability. From these insights, we propose design guidelines for algorithms to enable robotic teammates to consider fairness in its decision-making to maintain positive team social dynamics and team task performance.

The use of wearable devices (e.g., smartwatches) in two factor authentication (2FA) is fast emerging, as wearables promise better usability compared to smartphones. Still, the current deployments of wearable 2FA have significant usability and security issues. Specifically, one-time PIN-based wearable 2FA (PIN-2FA) requires noticeable user effort to open the app and copy random PINs from the wearable to the login terminal's (desktop/laptop) browser. An alternative approach, based on one-tap approvals via push notifications (Tap-2FA), relies upon user decision making to thwart attacks and is prone to skip-through. Both approaches are also vulnerable to traditional phishing attacks. To address this security-usability tension, we introduce a fundamentally different design of wearable 2FA, called SG-2FA, involving wrist-movement “seamless gestures” captured near transparently by the second factor wearable device while the user types a very short special sequence on the browser during the login process. The typing of the special sequence creates a wrist gesture that when identified correctly uniquely associates the login attempt with the device's owner. The special sequence can be fixed (e.g., “\$@\$@\$\$”), does not need to be a secret, and does not need to be memorized (could be simply displayed on the browser). This design improves usability over PIN-2FA since only this short sequence has to be typed as part of the login process (no interaction with or diversion of attention to the wearable and copying of random PINs is needed). It also greatly improves security compared to Tap-2FA since the attacker can not succeed in login unless the user's wrist is undergoing the exact same gesture at the exact same time. Moreover, the approach is phishing-resistant and privacy-preserving (unlike behavioral biometrics). Our results show that SG-2FA incurs only minimal errors in both benign and adversarial settings based on appropriate parameterizations.

Adaptive authentication systems identify and enforce suitable methods to verify that someone (user) or something (device) is eligible to access a service or a resource. An authentication method is usually adapted in response to changes in the security risk or the user's behaviour. Previous work on adaptive authentication systems provides limited guidance about i) what and how contextual factors can affect the selection of an authentication method; ii) which requirements are relevant to an adaptive authentication system and iii) how authentication methods can affect the satisfaction of the relevant requirements. In this paper, we provide a holistic framework informed by previous research to characterize the adaptive authentication problem and support the development of an adaptive authentication system. Our framework explicitly considers the contextual factors that can trigger an adaptation, the requirements that are relevant during decision making and their trade-offs, as well as the authentication methods that can change as a result of an adaptation. From the gaps identified in the literature, we elicit a set of challenges that can be addressed in future research on adaptive authentication.