An Agent Based Model for Ransomware Detection and Mitigation in a Cloud System

The increasing trend toward dematerialization and digitalization has prompted a surge in the adoption of IT service providers, offering cost-effective alternatives to traditional local services. Consequently, cloud services have become prevalent across various industries. While these services offer undeniable benefits, they face significant threats, particularly concerning the sensitivity of the data they handle. Many existing mathematical models struggle to accurately depict the complex scenarios of cloud systems. In response to this challenge, this paper proposes a behavioral model for ransomware propagation within such environments. In this model, each component of the environment is defined as an agent responsible for monitoring the propagation of malware. Given the distinct characteristics and criticality of these agents, the impact of malware can vary significantly. Scenario attacks are constructed based on real-world vulnerabilities documented in the Common Vulnerabilities and Exposures (CVEs) through the National Vulnerability Database. Defender actions are guided by an Intrusion Detection System (IDS) guideline. This research aims to provide a comprehensive framework for understanding and addressing ransomware threats in cloud systems. By leveraging an agent- based approach and real-world vulnerability data, our model offers valuable insights into detection and mitigation strategies for safeguarding sensitive cloud-based assets.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

基于Agent人工智能的异构网络多重覆盖节点入侵检测系统设计

异构网络具有结构复杂、多重覆盖面积大等特征,使得网络入侵检测较为隐蔽,威胁网络运行的安全性;为此,对基于Agent人工智能的异构网络多重覆盖节点入侵检测系统进行了研究;通过检测Agent和通信Agent装设主机Agent,以Cisco Stealthwatch流量传感器作为异构网络传感器检测攻击行为,采用STM32L151RDT664位微控制器传输批量数据,由MAX3232芯片实现系统电平转化,实现硬件系统设计;软件部分设计入侵检测标准,采用传感器设备捕获网络实时数据,通过Agent技术解析异构网络协议并提取数据运行特征,综合考虑协议解析结果及与检测标准匹配度,实现异构网络多重覆盖节点入侵检测;经实验测试表明,基于Agent人工智能的异构网络多重覆盖节点入侵检测系统入侵行为的漏检率和入侵类型误检率的平均值仅为6%和5%,能够有效提高检测精度,减小检测误差。

Intelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems

In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environments, as well as the need to increase the performance and safety characteristics of the related cooperation, coordi-nation and control mechanisms is encouraging the development of new information management strategies to direct and man- age the automated systems involved in the manufacturing processes. The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems. In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions. Autonomy, flexibility and adaptability of the agent-based technology are the key points to manage both automated and information processes of any industrial system. The paper describes the main features of the IAs and MASs and how their technology can be adapted to support the current and next generation advanced industrial systems. Moreover, a study of how a MAS is utilized within a productive process is depicted.

Multi Agent Systems for Fault States Diagnostics and Equipment and Process Management of Yogurt Ripening Tank

It is impossible to plan in advance unpredictable phenomena at monitoring, diagnosis and control of industrial batch and continuous equipment and processes such as chemical composition of the raw materials, the process leads to unexpected reactions and changes its parameters, etc. The agent is active, a program entity, has its own ideas how to perform the tasks of the own agenda. Agents perceive, behave ＂reasonably＂, communicate with other agents. Agents can represent equipment and operations in batch processes as recommended by the ISA $88. Jadex system is based on Java language and on FIPA org. recommendations. The description of ripening tank T406 and recipe for yogurt production in the holding of MADETA Corp. in the Czech Rep. It is described modeling and displaying of＂normal＂ and error, fault unit state of the ripening tank. Agents are within the Jadex system and describing the behavior of ripening tank T406 with state diagrams-automata and assist in diagnosing of fault states. States are described in XML language-SCXML （State Charts XML）. Jadex Control Center-JCC represents a major access point to use for operating in real time.

Secure Consensus Control for Multi-Agent Systems With Attacks and Communication Delays

This paper addresses the consensus problem for nonlinear multi-Agent systems suffering from attacks and communication delays. The network studied in this paper consists of two types of agents, namely, loyal agents and attack agents. The loyal agents update their states based on delayed state information exchanged with their neighbors. Meanwhile, the attack agents can strategically send messages with wrong values, or collude with other attack agents to disrupt the correct operation of the system. We design a novel delay robust secure consensus DRSC algorithm according to the neighboring nodes U2BC delayed information. Convergence analysis of the system under the protocol designed is provided by using Lyapunov-Krasovskii stability theory and Barbalat-like argument approach. Finally, an example and simulation results are presented to demonstrate the effectiveness of the algorithm. © 2014 Chinese Association of Automation.

Proto col Design for Output Consensus of Port-controlled Hamiltonian Multi-agent Systems

这份报纸调查控制港口的 Hamiltonian (PCH ) 多代理人系统的产量一致问题与修理并且切换的拓扑学。第一，一个分布式的组产量一致协议经由塑造方法全球性到达稳定性和组产量一致的能量被设计。第二，一个新分布式的控制协议被使用 PCH 系统的结构的性质建议。这个协议的优点是它能由构造一种虚拟邻居转变指导的图到未受指导的图。第三，一个控制协议被设计，扩大 LaSalle0s 不变性原则在联合连接的拓扑学条件下面为交换系统发展了让所有代理人当拓扑学正在切换时，到达输出一致。最后，有模拟的一些解说性的例子被提供表明在这份报纸设计的协议的有效性。

Swarming behavior of multi-agent systems

We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties. It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite size around the swarm center in a finite time. Moreover, we extend our results to more general attraction/repulsion functions. Numerical simulations demonstrate that all agents will eventually enter into and remain in a bounded region around the swarm center which may exhibit complex spiral motion due to asymmetry of the coupling structure. The model in this paper is more general than isotropic swarms and our results provide further insight into the effect of the interaction pattern on individual motion in a swarm system.

Distributed Tracking Control of a Class of Multi-agent Systems in Non-affine Pure-feedback Form Under a Directed Topology

In this paper, we consider a consensus tracking problem of a class of networked multi-agent systems(MASs)in non-affine pure-feedback form under a directed topology. A distributed adaptive tracking consensus control scheme is constructed recursively by the backstepping method, graph theory,neural networks(NNs) and the dynamic surface control(DSC)approach. The key advantage of the proposed control strategy is that, by the DSC technique, it avoids "explosion of complexity"problem along with the increase of the degree of individual agents and thus the computational burden of the scheme can be drastically reduced. Moreover, there is no requirement for prior knowledge about system parameters of individual agents and uncertain dynamics by employing NNs approximation technology.We then further show that, in theory, the designed control policy guarantees the consensus errors to be cooperatively semi-globally uniformly ultimately bounded(CSUUB). Finally, two examples are presented to validate the effectiveness of the proposed control strategy.

LMI Consensus Condition for Discrete-time Multi-agent Systems

This paper examines a consensus problem in multiagent discrete-time systems, where each agent can exchange information only from its neighbor agents. A decentralized protocol is designed for each agent to steer all agents to the same vector. The design condition is expressed in the form of a linear matrix inequality. Finally, a simulation example is presented and a comparison is made to demonstrate the effectiveness of the developed methodology.

Finite-time consensus for leader-following multi-agent systems over switching network topologies

Finite-time consensus problem of the leader-following multi-agent system under switching network topologies is studied in this paper. Based on the graph theory, matrix theory, homogeneity with dilation, and LaSalle＇s invariance principle, the control protocol of each agent using local information is designed, and the detailed analysis of the leader- following finite-time consensus is provided. Some examples and simulation results are given to illustrate the effectiveness of the obtained theoretical results.

Distributed Adaptive Cooperative Tracking of Uncertain Nonlinear Fractional-order Multi-agent Systems

In this paper, the leader-following tracking problem of fractional-order multi-agent systems is addressed. The dynamics of each agent may be heterogeneous and has unknown nonlinearities. By assumptions that the interaction topology is undirected and connected and the unknown nonlinear uncertain dynamics can be parameterized by a neural network, an adaptive learning law is proposed to deal with unknown nonlinear dynamics, based on which a kind of cooperative tracking protocols are constructed. The feedback gain matrix is obtained to solve an algebraic Riccati equation. To construct the fully distributed cooperative tracking protocols, the adaptive law is also adopted to adjust the coupling weight. With the developed control laws,we can prove that all signals in the closed-loop systems are guaranteed to be uniformly ultimately bounded. Finally, a simple simulation example is provided to illustrate the established result.

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

Hierarchical Distributed Control Design for Multi-agent Systems Using Approximate Simulation

在这份报纸，我们基于近似模拟为多代理人系统考虑一个层次控制图案。到还原剂复杂性，我们首先构造一个简单抽象系统指导代理人，然后，我们讨论在抽象系统和多重代理人之间的模拟关系。在这个抽象系统的帮助下，分布式的层次控制被建议完成一项协作任务。由普通 Lyapunov 功能的优点，我们与考虑到模拟功能交换多代理人拓扑学分析集体行为。

Fast consensus seeking for multi-agent systems

For multi-agent systems based on the local information,the agents automatically converge to a common consensus state and the convergence speed is determined by the algebraic connectivity of the communication network.To study fast consensus seeking problems of multi-agent systems in undirected networks,a consensus protocol is proposed which considers the average information of the agents＇ states in a certain time interval,and a consensus convergence criterion for the system is obtained.Based on the frequency-domain analysis and algebra graph theory,it is shown that if the time interval is chosen properly,then requiring the same maximum control effort the proposed protocol reaches consensus faster than the standard consensus protocol.Simulations are provided to demonstrate the effectiveness of these theoretical results.

Multi-target pursuit formation of multi-agent systems

The main goal of this paper is to design a team of agents that can accomplish multi-target pursuit formation using a developed leader-follower strategy. It is supposed that every target can accept a certain number of agents. First, each agent can automatically choose its target based on the distance from the agent to the target and the number of agents accepted by the target. In view of the fact that all agents are randomly dispersed in the workplace at the initial time, we present a numbering strategy for them. During the movement of agents, not every agent can always obtain pertinent state information about the targets. So, a developed leader-follower strategy and a pursuit formation algorithm are proposed. Under the proposed method, agents with the same target can maintain a circle formation. Furthermore, it turns out that the pursuit formation algorithm for agents to the desired formation is convergent. Simulation studies are provided to illustrate the effectiveness of the proposed method.

Coverage-optimization based guidance of mobile agents for improved control of distributed parameter systems

The control problem of a class of parabolic distributed parameter systems (DPSs) is investigated by using mobile agents with capabilities of sensing and actuating. The guidance strategies of mobile agents based on coverage optimization methods are proposed to improve the control performance of the system and make the state norm of the system converge to zero faster. The coverage optimization problems are constructed based on the measurement of each agent. By solving the coverage optimization problems, the local optimal moving direction of each agent can be obtained. Then the gradient-based agent motion control laws are established. With the indicator function and the surface delta function, this method is generalized to n-dimensional space, and suitable for any sensing region with piecewise smooth boundaries. The stability and control performance of the system are analyzed. Numerical simulations show the effectiveness of the proposed methods.

Target Tracking and Obstacle Avoidance for Multi-agent Systems

This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems. To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic environment, a novel control algorithm based on potential function and behavior rules is proposed. Meanwhile, the interactions among agents are also considered. According to the state whether an agent is within the area of its neighbors＇ influence, two kinds of potential functions are presented. Meanwhile, the distributed control input of each agent is determined by relative velocities as well as relative positions among agents, target and obstacle. The maximum linear speed of the agents is also discussed. Finally, simulation studies are given to demonstrate the performance of the proposed algorithm.