Theory of deferred action: Agent-based simulation model for designing complex adaptive systems

Deferred action is the axiom that agents act in emergent organisation to achieve predetermined goals. Enabling deferred action in designed artificial complex adaptive systems like business organisations and IS is problematical. Emergence is an intractable problem for designers because it cannot be predicted. We develop proof-of-concept, conceptual proto-agent model, of emergent organisation and emergent IS to understand better design principles to enable deferred action as a mechanism for coping with emergence in artefacts. We focus on understanding the effect of emergence when designing artificial complex adaptive systems by developing an exploratory proto-agent model and evaluate its suitability for implementation as agent-based simulation

An improved agent-based adaptive protection model for distributed denial of service flooding attack and flash crowd flooding traffic

Recently, a serious disturbance for network security could be a Distributed Denial of Service (DDoS) attack. The advent of technological era has also brought along the threat of DDoS attacks for a variety of services and applications that use the Internet. Firms can incur huge financial losses even if there is a disruption in services for a fraction of period. Similar to a DDoS attack is the Flash Crowd (FC) flooding traffics, in which a particular service is assessed by many legitimate users concurrently, which results in the denial of service. Overloading of network resources is a common issue associated with both of these events, which impact CPU, available bandwidth, and memory for legitimate users, thereby leading to limited accessibility. To address this issue, this thesis proposes an adaptive agent-based protection model known as Adaptive Protection of Flooding Attacks (APFA) specific for DDoS attacks and FC flooding traffics. The APFA model is aimed to protect the Network Application Layer (NAL) against such attacks. The APFA model consists of analysis, detection, decision and filter modules. The main contribution of this work in the APFA model is the decision module that employs a software agent to adapt and recognize the DDoS attacks (Demons and Zombies) and FC flooding traffics. The agent is equipped with three analysis functions that operate on three parameters of normal traffic intensity, traffic attack behavior, and IP address history log. The agent accordingly reacts on each of these attacks with different types of filtering actions as required. APFA model was implemented and tested by applying different attack scenarios using CIDDS standard dataset. The APFA model testing results achieve an accuracy of 99.64%, a precision of 99.62% and sensitivity of 99.96%. The APFA model results outperform similar models of the related work and the adaptive agent is able to distinguish between demons and zombies of the DDoS attacks with high accuracy of 99.91%

Adaptive microfoundations for emergent macroeconomics

In this paper we present the basics of a research program aimed at providing microfoundations to macroeconomic theory on the basis of computational agentbased adaptive descriptions of individual behavior. To exemplify our proposal, a simple prototype model of decentralized multi-market transactions is offered. We show that a very simple agent-based computational laboratory can challenge more structured dynamic stochastic general equilibrium models in mimicking comovements over the business cycle.Microfoundations of macroeconomics, Agent-based economics, Adaptive behavior

Intelligent agent for formal modelling of temporal multi-agent systems

Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system

AN ADAPTATIVE EVOLUTIONARY MODEL OF FINANCIAL INVESTORS

The main purpose of the paper is to determine a general behavior of a multi-agent model capable of describing the process of deliberation of an investors group witch may repeatedly decide whether to buy or sell an asset. Each adaptive agent was modeled asProgramming Models, Genetic algorithms, Information efficiency