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Most existing adaptive information security approaches focus on simplified behavioral patterns and work as isolated models. This limits their effectiveness against advanced and dynamic cyber threats. Therefore, there is an emergent requirement for a mathematically unified framework that can dynamically capture and forecast the aggregate behavior of both the attacker and the defender in a complex environment. The paper proposes a mathematical modeling approach that combines composite behavior models into adaptive information security strategies. The framework encapsulates heterogeneous behavioral patterns into a unified dynamic model that can adapt to an ever-changing threat landscape. This result in novel adaptation rules derived from system dynamics and game theory, with the aim of enabling proactive defense mechanisms that can adapt to real-time challenges posed by adversary actors. The outcomes presented in this paper demonstrate strong improvements in threat detection, mitigation speed, and resource optimization through systematic model implementation, comprehensive simulation, and positive statistical hypothesis testing. The comparison reveals that the proposed method is generally superior to existing methods in scalability and effectiveness. It presents a new class of adaptive cybersecurity models that have deeper behavioral insights and enhanced resilience in complicated threat environments.

In well-managed coordinated networked microgrids (MGs) besides electricity interchange between MGs, global optimisation is fulfilled. Here the authors studied a networked MG architecture, in which the control centre of microgrids communicates with a distribution network operator (DNO) to fulfil their local requirements. However, communication signals are always vulnerable to cyberattacks. While the surplus/deficit powers are reported by one MG to DNO, other MGs can act as potential cyber attackers aimed at decreasing their own costs. This action may also lead to threat the global optimisation of networked MGs. When an attacker manipulates the signal sent from the attacked MG to DNO, it will result in a false power interchange schedule produced by DNO. The attacker MG in the next step, maliciously accesses and changes the signal sent from the DNO to the attacked MG. In case a successful attack executed, the operation cost of the attacker MG will be decreased. Furthermore, a game-theoretic model of attacker–defender interaction is proposed, while different behaviours of players are addressed. The optimal scheduling scheme of MGs is formulated as a mixed-integer linear programming problem and solved by CPLEX. Simulation results show the impacts of the attacks and importance of the defend strategies.

Due to the increasing number of wireless terminals and progressively extensive interconnections among them, interaction between the attack group and defense group is becoming a dominant security manifestation in future wireless networks. This paper focuses on the modeling and analysis of multi-attacker to multi-defender interaction. First, considering the continuous interaction between the attack group and defense group in real-time, a differential game-based multi-attacker to multi-defender interaction model is explicated with paralysis threshold introduced to reduce the ping-pong effect in paralysis. Optimal control theory is then introduced to obtain the equilibrium strategy with Hamilton best control method and a proposed optimal strategy selection algorithm for multi-attacker to multi-defender interaction. Finally, simulations are provided to demonstrate the evolutionary trajectory of optimal attack and defensive strategies and the relationship between the paralysis threshold and the group strength evolution results. Numerical results show the attackers and defenders are aggressive to strengthen their groups initially and then gradually decrease their strength to obtain ideal cost-effectiveness ratios. Moreover, increasing the defense paralysis threshold within a certain range will be more conducive to improving the defense effectiveness.