Application of the Learning Automaton Model for Ensuring Cyber Resiliency
2. Materials and Methods
2.1. Approaches to Ensuring Cyber Resiliency
- Homeostatic approach that preserves the system state under external influences . Consideration of a digital system with a network structure as a homeostatic system implies the formation of some set of attributes, which should be satisfied by the system, forming a view of its state. In the case that, as a result of operation, one or more attributes cease to meet the required criteria, corrections are applied to the system [10,11,12,13];
- Functional approach, which is based on the theory of functional systems [7,14]. The main principle of the functional approach is the preservation of the system function under external influences. In its framework, a digital system is considered a system with one or more functions, and its performance under destructive influences is a priority. In this case, the goal of management is the preservation of this function or their set with the help of various methods and tools [15,16];
- Ahead reflection (anticipation) that prevents a destructive impact and its consequences before it is committed [17,18]. The essence of the ahead reflection is to predict possible security impacts and take measures to neutralize them by creating resource reserves, applying an anticipatory effect [19,20].
- Expenses reducing: choosing the way of reaction (out of acceptable ones) to a destructive impact (e.g., cyberattack) requires the minimization of costs—amount of resources or energy—for its implementation. For example, the number of operations on the graph of the system should be minimized to preserve the functional route or homeostatic equilibrium.
- Maximization of the system’s freedom degrees: This is to maximize information exchange with minimum entropy in the system. For resiliency, it causes maximization of communication links and interactions between the connected nodes of the system.
- Cyber resistance preservation: When responding to an external impact (e.g., cyberattack), the system has to ensure the preservation (if possible) of a sufficient stock of components for subsequent compensatory and anticipatory actions. For some systems, this principle can be formulated as the maintaining the margins of stability. The quantitative assessment of resiliency depends on the type of the system and approach to ensuring the system resiliency. For example, it can be expressed as a risk score or a number of reserved functional routes and a number of redundant nodes.
2.2. The Learning Automaton Model
- Type of automaton: stochastic.
- Structure: variable.
- Evaluation model: P-model (only two possible options: favorable or unfavorable).
- Learning model: combined, i.e., reward–-penalty and reward–inaction , where a—reward feature, b—penalty parameter .
- Packet re-transmission: After transmitting a packet to a neighbor node, the sending node switches to a monitoring mode to track the re-transmission of its packet further through the network. It is used to counteract the nodes that re-transmit packets selectively or not at all.
- Packet integrity: In addition to checking for re-transmissions, the sending node also checks the checksum of the packet sent by its neighbor through the network.
- Node data generation intensity: The indicator is defined as the number of packets received from a node for a certain period of time and is designed to protect nodes from attacks of energy depletion and channel clogging.
- Volume of sent data: similar to the previous one. Accounting the volume of data sent by neighboring nodes helps protect against resource exhaustion attacks.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
|AODV||Ad hoc On-Demand Distance Vector|
|FANET||Flying Ad hoc Network|
|IoT||Internet of Things|
|VANET||Vehicular Ad hoc Network|
|WSN||Wireless Sensor Network|
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|Characteristics||Homeostasis||Functional Approach||Ahead Reflection|
|Dominant principle||Expenses reducing||Maximum degrees of freedom||Preservation of cyber resistance|
|Feedback type||Negative||Positive, negative||Positive|
|Target||Homeostatic equilibrium||Functionality preserving||Maintaining the local stability||Risk minimization|
|Mechanisms of implementation||Structural, parametric, and architectural homeostasis||Self-improvement, self-regulation||Immunization, preventing threats to the system units||Risk reduction, self-regulation using a prediction and game theory|
|Technology||Conflict analysis and finding an algorithm for its resolution at the level of object transformation||State-based control; generation and analysis of available states and transitions between them based on the system model||Adjusting communications; isolating nodes. Maintaining the functionally equivalent modules and redundancy||Modeling development and risk management by varying the structure and parameters of the system|
|Methods and tools||Heuristics, control of stability indicators; graph analysis; communication graph transformation||Intelligent control; methods for generating equivalent structures; self-regulation of system graph||Communication propagation and control modeling; redundant sets based on an intruder model||Game model of variations in structures to maintain the level of risk|
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Kalinin, M.; Ovasapyan, T.; Poltavtseva, M. Application of the Learning Automaton Model for Ensuring Cyber Resiliency. Symmetry 2022, 14, 2208. https://doi.org/10.3390/sym14102208
Kalinin M, Ovasapyan T, Poltavtseva M. Application of the Learning Automaton Model for Ensuring Cyber Resiliency. Symmetry. 2022; 14(10):2208. https://doi.org/10.3390/sym14102208Chicago/Turabian Style
Kalinin, Maxim, Tigran Ovasapyan, and Maria Poltavtseva. 2022. "Application of the Learning Automaton Model for Ensuring Cyber Resiliency" Symmetry 14, no. 10: 2208. https://doi.org/10.3390/sym14102208