Biblio
The present paper describes some of the results obtained in the Faculty of Computer Systems and Technology at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. Also is made a survey about existing hybrid methods, which are using several artificial intelligent methods for cyber defense. The paper introduces a model for intrusion detection systems where multi agent systems are the bases and artificial intelligence are applicable by the means simple real-time models constructed in laboratory environment.
In recent years, the increasing concerns around the centralized cloud web services (e.g. privacy, governance, surveillance, security) have triggered the emergence of new distributed technologies, such as IPFS or the Blockchain. These innovations have tackled technical challenges that were unresolved until their appearance. Existing models of peer-to-peer systems need a revision to cover the spectrum of potential systems that can be now implemented as peer-to-peer systems. This work presents a framework to build these systems. It uses an agent-oriented approach in an open environment where agents have only partial information of the system data. The proposal covers data access, data discovery and data trust in peer-to-peer systems where different actors may interact. Moreover, the framework proposes a distributed architecture for these open systems, and provides guidelines to decide in which cases Blockchain technology may be required, or when other technologies may be sufficient.
This paper presents a control strategy for Cyber-Physical System defense developed in the framework of the European Project ATENA, that concerns Critical Infrastructure (CI) protection. The aim of the controller is to find the optimal security configuration, in terms of countermeasures to implement, in order to address the system vulnerabilities. The attack/defense problem is modeled as a multi-agent general sum game, where the aim of the defender is to prevent the most damage possible by finding an optimal trade-off between prevention actions and their costs. The problem is solved utilizing Reinforcement Learning and simulation results provide a proof of the proposed concept, showing how the defender of the protected CI is able to minimize the damage caused by his her opponents by finding the Nash equilibrium of the game in the zero-sum variant, and, in a more general scenario, by driving the attacker in the position where the damage she/he can cause to the infrastructure is lower than the cost it has to sustain to enforce her/his attack strategy.
We provide an agent based simulation model of the Swedish payment system. The simulation model is to be used to analyze the consequences of loss of functionality, or disruptions of the payment system for the food and fuel supply chains as well as the bank sector. We propose a gaming simulation approach, using a computer based role playing game, to explore the collaborative responses from the key actors, in order to evoke and facilitate collective resilience.
Policies govern choices in the behavior of systems. They are applied to human behavior as well as to the behavior of autonomous systems but are defined differently in each case. Generally humans have the ability to interpret the intent behind the policies, to bring about their desired effects, even occasionally violating them when the need arises. In contrast, policies for automated systems fully define the prescribed behavior without ambiguity, conflicts or omissions. The increasing use of AI techniques and machine learning in autonomous systems such as drones promises to blur these boundaries and allows us to conceive in a similar way more flexible policies for the spectrum of human-autonomous systems collaborations. In coalition environments this spectrum extends across the boundaries of authority in pursuit of a common coalition goal and covers collaborations between human and autonomous systems alike. In social sciences, social exchange theory has been applied successfully to explain human behavior in a variety of contexts. It provides a framework linking the expected rewards, costs, satisfaction and commitment to explain and anticipate the choices that individuals make when confronted with various options. We discuss here how it can be used within coalition environments to explain joint decision making and to help formulate policies re-framing the concepts where appropriate. Social exchange theory is particularly attractive within this context as it provides a theory with “measurable” components that can be readily integrated in machine reasoning processes.
With the repaid growth of social tagging users, it becomes very important for social tagging systems how the required resources are recommended to users rapidly and accurately. Firstly, the architecture of an agent-based intelligent social tagging system is constructed using agent technology. Secondly, the design and implementation of user interest mining, personalized recommendation and common preference group recommendation are presented. Finally, a self-adaptive recommendation strategy for social tagging and its implementation are proposed based on the analysis to the shortcoming of the personalized recommendation strategy and the common preference group recommendation strategy. The self-adaptive recommendation strategy achieves equilibrium selection between efficiency and accuracy, so that it solves the contradiction between efficiency and accuracy in the personalized recommendation model and the common preference recommendation model.
Given social media users' plethora of interactions, appropriately controlling access to such information becomes a challenging task for users. Selecting the appropriate audience, even from within their own friend network, can be fraught with difficulties. PACMAN is a potential solution for this dilemma problem. It's a personal assistant agent that recommends personalized access control decisions based on the social context of any information disclosure by incorporating communities generated from the user's network structure and utilizing information in the user's profile. PACMAN provides accurate recommendations while minimizing intrusiveness.
Advanced Persistent Threats are increasingly becoming one of the major concerns to many industries and organizations. Currently, there exists numerous articles and industrial reports describing various case studies of recent notable Advanced Persistent Threat attacks. However, these documents are expressed in natural language. This limits the efficient reusability of the threat intelligence information due to ambiguous nature of the natural language. In this article, we propose a model to formally represent Advanced Persistent Threats as multi-agent systems. Our model is inspired by the concepts of agent-oriented social modelling approaches, generally used for software security requirement analysis.
Multi-agent simulations are useful for exploring collective patterns of individual behavior in social, biological, economic, network, and physical systems. However, there is no provenance support for multi-agent models (MAMs) in a distributed setting. To this end, we introduce ProvMASS, a novel approach to capture provenance of MAMs in a distributed memory by combining inter-process identification, lightweight coordination of in-memory provenance storage, and adaptive provenance capture. ProvMASS is built on top of the Multi-Agent Spatial Simulation (MASS) library, a framework that combines multi-agent systems with large-scale fine-grained agent-based models, or MAMs. Unlike other environments supporting MAMs, MASS parallelizes simulations with distributed memory, where agents and spatial data are shared application resources. We evaluate our approach with provenance queries to support three use cases and performance measures. Initial results indicate that our approach can support various provenance queries for MAMs at reasonable performance overhead.
Game theory serves as a powerful tool for distributed optimization in multiagent systems in different applications. In this paper we consider multiagent systems that can be modeled as a potential game whose potential function coincides with a global objective function to be maximized. This approach renders the agents the strategic decision makers and the corresponding optimization problem the problem of learning an optimal equilibruim point in the designed game. In distinction from the existing works on the topic of payoff-based learning, we deal here with the systems where agents have neither memory nor ability for communication, and they base their decision only on the currently played action and the experienced payoff. Because of these restrictions, we use the methods of reinforcement learning, stochastic approximation, and learning automata extensively reviewed and analyzed in [3], [9]. These methods allow us to set up the agent dynamics that moves the game out of inefficient Nash equilibria and leads it close to an optimal one in both cases of discrete and continuous action sets.
Healing Process is a major role in developing resiliency in cyber-physical system where the environment is diverse in nature. Cyber-physical system is modelled with Multi Agent Paradigm and biological inspired Danger Theory based-Artificial Immune Recognization2 Algorithm Methodology towards developing healing process. The Proposed methodology is implemented in a simulation environment and percentage of Convergence rates shown in achieving accuracy in the healing process to resiliency in cyber-physical system environment is shown.
The Polish Power System is becoming increasingly more dependent on Information and Communication Technologies which results in its exposure to cyberattacks, including the evolved and highly sophisticated threats such as Advanced Persistent Threats or Distributed Denial of Service attacks. The most exposed components are SCADA systems in substations and Distributed Control Systems in power plants. When addressing this situation the usual cyber security technologies are prerequisite, but not sufficient. With the rapidly evolving cyber threat landscape the use of partnerships and information sharing has become critical. However due to several anonymity concerns the relevant stakeholders may become reluctant to exchange sensitive information about security incidents. In the paper a multi-agent architecture is presented for the Polish Power System which addresses the anonymity concerns.
Information technology is continually changing, discoveries are made every other day. Cyber-physical systems consist of both physical and computational elements and are becoming more and more popular in today's society. They are complex systems, used in complex applications. Therefore, security is a critical and challenging aspect when developing cyber-physical systems. In this paper, we present a solution for ensuring data confidentiality and security by combining some of the most common methods in the area of security - cryptography and steganography. Furthermore, we use hierarchical access to information to ensure confidentiality and also increase the overall security of the cyber-physical system.
The Internet of Things (IOT) is a network of networks where massively large numbers of objects or things are interconnected to each other through the network. The Internet of Things brings along many new possibilities of applications to improve human comfort and quality of life. Complex systems such as the Internet of Things are difficult to manage because of the emergent behaviours that arise from the complex interactions between its constituent parts. Our key contribution in the paper is a proposed multiagent web for the Internet of Things. Corresponding data management architecture is also proposed. The multiagent architecture provides autonomic characteristics for IOT making the IOT manageable. In addition, the multiagent web allows for flexible processing on heterogeneous platforms as we leverage off web protocols such as HTTP and language independent data formats such as JSON for communications between agents. The architecture we proposed enables a scalable architecture and infrastructure for a web-scale multiagent Internet of Things.
The performance of ad hoc networks depends on the cooperative and trust nature of the distributed nodes. To enhance security in ad hoc networks, it is important to evaluate the trustworthiness of other nodes without central authorities. An information-theoretic framework is presented, to quantitatively measure trust and build a novel trust model (FAPtrust) with multiple trust decision factors. These decision factors are incorporated to reflect trust relationship's complexity and uncertainty in various angles. The weight of these factors is set up using fuzzy analytic hierarchy process theory based on entropy weight method, which makes the model has a better rationality. Moreover, the fuzzy logic rules prediction mechanism is adopted to update a node's trust for future decision-making. As an application of this model, a novel reactive trust-based multicast routing protocol is proposed. This new trusted protocol provides a flexible and feasible approach in routing decision-making, taking into account both the trust constraint and the malicious node detection in multi-agent systems. Comprehensive experiments have been conducted to evaluate the efficiency of trust model and multicast trust enhancement in the improvement of network interaction quality, trust dynamic adaptability, malicious node identification, attack resistance and enhancements of system's security.
One of the various features expected for a smart power distribution system - a smart grid in the power distribution level - is the possibility of the fully automated operation for certain control actions. Although this is very expected, it requires various logic, sensor and actuator technologies in a system which, historically, has a low level of automation. One of the most analyzed problems for the distribution system is the topology reconfiguration. The reconfiguration has been applied to various objectives: minimization of power losses, voltage regulation, load balancing, to name a few. The solution method in most cases is centralized and its application is not in real-time. From the new perspectives of advanced distribution systems, fast and adaptive response of the control actions are required, specially in the presence of alternative generation sources and electrical vehicles. In this context, the multi-agent system, which embeds the necessary control actions and decision making is proposed for the topology reconfiguration aiming the loss reduction. The concept of multi-agent system for distribution system is proposed and two case studies with 11-Bus and 16-Bus system are presented.
Web Service (WS) plays an important role in today's word to provide effective services for humans and these web services are built with the standard of SOAP, WSDL & UDDI. This technology enables various service providers to register and service sender their intelligent agent based privacy preserving modelservices to utilize the service over the internet through pre established networks. Also accessing these services need to be secured and protected from various types of attacks in the network environment. Exchanging data between two applications on a secure channel is a challenging issue in today communication world. Traditional security mechanism such as secured socket layer (SSL), Transport Layer Security (TLS) and Internet Protocol Security (IP Sec) is able to resolve this problem partially, hence this research paper proposes the privacy preserving named as HTTPI to secure the communication more efficiently. This HTTPI protocol satisfies the QoS requirements, such as authentication, authorization, integrity and confidentiality in various levels of the OSI layers. This work also ensures the QoS that covers non functional characteristics like performance (throughput), response time, security, reliability and capacity. This proposed intelligent agent based model results in excellent throughput, good response time and increases the QoS requirements.
The existence of mixed pixels is a major problem in remote-sensing image classification. Although the soft classification and spectral unmixing techniques can obtain an abundance of different classes in a pixel to solve the mixed pixel problem, the subpixel spatial attribution of the pixel will still be unknown. The subpixel mapping technique can effectively solve this problem by providing a fine-resolution map of class labels from coarser spectrally unmixed fraction images. However, most traditional subpixel mapping algorithms treat all mixed pixels as an identical type, either boundary-mixed pixel or linear subpixel, leading to incomplete and inaccurate results. To improve the subpixel mapping accuracy, this paper proposes an adaptive subpixel mapping framework based on a multiagent system for remote-sensing imagery. In the proposed multiagent subpixel mapping framework, three kinds of agents, namely, feature detection agents, subpixel mapping agents and decision agents, are designed to solve the subpixel mapping problem. Experiments with artificial images and synthetic remote-sensing images were performed to evaluate the performance of the proposed subpixel mapping algorithm in comparison with the hard classification method and other subpixel mapping algorithms: subpixel mapping based on a back-propagation neural network and the spatial attraction model. The experimental results indicate that the proposed algorithm outperforms the other two subpixel mapping algorithms in reconstructing the different structures in mixed pixels.
It has gradually realized in the industry that the increasing complexity of cloud computing under interaction of technology, business, society and the like, instead of being simply solved depending on research on information technology, shall be explained and researched from a systematic and scientific perspective on the basis of theory and method of a complex adaptive system (CAS). This article, for basic problems in CAS theoretical framework, makes research on definition of an active adaptive agent constituting the cloud computing system, and proposes a service agent concept and basic model through commonality abstraction from two basic levels: cloud computing technology and business, thus laying a foundation for further development of cloud computing complexity research as well as for multi-agent based cloud computing environment simulation.
Face-to-face negotiations always benefit if the interacting individuals trust each other. But trust is also important in online interactions, even for humans interacting with a computational agent. In this article, the authors describe a behavioral experiment to determine whether, by volunteering information that it need not disclose, a software agent in a multi-issue negotiation can alleviate mistrust in human counterparts who differ in their propensities to mistrust others. Results indicated that when cynical, mistrusting humans negotiated with an agent that proactively communicated its issue priority and invited reciprocation, there were significantly more agreements and better utilities than when the agent didn't volunteer such information. Furthermore, when the agent volunteered its issue priority, the outcomes for mistrusting individuals were as good as those for trusting individuals, for whom the volunteering of issue priority conferred no advantage. These findings provide insights for designing more effective, socially intelligent agents in online negotiation settings.