Attack-defense Scenarios Research Articles

Railway Cyber-Security in the Era of Interconnected Systems: A Survey

Technological advances in the telecommunications industry have brought significant advantages in the management and performance of communication networks. The railway industry is among the ones that have benefited the most. These interconnected systems, however, have a wide area exposed to cyberattacks. This survey examines the cybersecurity aspects of railway systems by considering the standards, guidelines, frameworks, and technologies used in the industry to assess and mitigate cybersecurity risks, particularly regarding the relationship between safety and security. To do so, we dedicate specific attention to signaling, which fundamental reliance on computer and communication technologies allows us to explore better the multifaceted nature of the security of modern hyperconnected railway systems. With this in mind, we then move on to analyzing the approaches and tools that practitioners can use to facilitate the cyber security process. In detail, we present a view on cyber ranges as an enabling technology to model and emulate computer networks and attack-defense scenarios, study vulnerabilities’ impact, and finally devise countermeasures. We also discuss several possible use cases strongly connected to the railway industry reality.

Optimal Network Defense Strategy Selection Method: A Stochastic Differential Game Model

In a real-world network confrontation process, attack and defense actions change rapidly and continuously. The network environment is complex and dynamically random. Therefore, attack and defense strategies are inevitably subject to random disturbances during their execution, and the transition of the network security state is affected accordingly. In this paper, we construct a network security state transition model by referring to the epidemic evolution process, use Gaussian noise to describe random effects during the strategy execution, and introduce a random disturbance intensity factor to describe the degree of random effects. On this basis, we establish an attack-defense stochastic differential game model, propose a saddle point equilibrium solution method, and provide an algorithm to select the optimal defense strategy. Our method achieves real-time defense decision-making in network attack-defense scenarios with random disturbances and has better real-time performance and practicality than current methods. Results of a simulation experiment show that our model and algorithm are effective and feasible.

A Framework for Modeling and Structural Vulnerability Analysis of Spatial Cyber-Physical Power Systems From an Attack–Defense Perspective

This article provides a framework for modeling and analysis of spatial cyber-physical power systems (CPPSs). Previous studies on structural vulnerability analysis of CPPS mainly focused on network topology without any spatial information. In this article, we first propose a hierarchical information system evolution model for spatial power grids. Then, a spatial CPPS model is established by combining power and information systems that accounts for overlapped power and information lines. Finally, the structural vulnerability under global and localized failures is investigated based on direct current (dc) power flow. Under global failures, different types of components have distinguishing effects, and the power-node failure is the most serious case. For localized failures, key zones can be identified by the cascading failure scale, and similarly, different failure modes affect different areas. Furthermore, considering power system scenarios with only defenders and those with both attackers and defenders, we investigate two resource allocation situations. For defenders, increased resources and appropriately dispersed allocation can work effectively. However, in the attack-defense scenario, the perfect subgame equilibrium suggests that both attackers and defenders must concentrate all their resources on the most vulnerable zone. These findings may establish a foundation for future efforts to model and protect spatial CPPSs.

Optimal Timing Selection Approach to Moving Target Defense: A FlipIt Attack-Defense Game Model

The centralized control characteristics of software-defined networks (SDNs) make them susceptible to advanced persistent threats (APTs). Moving target defense, as an effective defense means, is constantly developing. It is difficult to effectively characterize an MTD attack and defense game with existing game models and effectively select the defense timing to balance SDN service quality and MTD decision-making benefits. From the hidden confrontation between the actual attack and defense sides, existing attack-defense scenarios are abstractly characterized and analyzed. Based on the APT attack process of the Cyber Kill Chain (CKC), a state transition model of the MTD attack surface based on the susceptible-infective-recuperative-malfunctioned (SIRM) infectious disease model is defined. An MTD attack-defense timing decision model based on the FlipIt game (FG-MTD) is constructed, which expands the static analysis in the traditional game to a dynamic continuous process. The Nash equilibrium of the proposed method is analyzed, and the optimal timing selection algorithm of the MTD is designed to provide decision support for the selection of MTD timing under moderate security. Finally, the application model is used to verify the model and method. Through numerical analysis, the timings of different types of attack-defense strategies are summarized.

Colonel Blotto Games in Network Systems: Models, Strategies, and Applications

Network systems, such as Internet, smart grids, transportation networks, social networks, etc., play a critical role in human society. However, due to their inherent vulnerability as well as the limited management and operational capability, these network systems are constantly under the threat of malicious attackers. Therefore, in such attack-defense scenarios, it is particularly significant to make the best use of defenders’ limited resources and capability. In this paper, we propose a networked Colonel Blotto game for the attack–defense strategy, where the attackers and defenders allocate the limited resources on each node, and their utility depends on certain network performance metrics, which are defined for evaluating the performance of the whole network system. Furthermore, considering the complexity of the equilibrium analysis in large-scale network systems, a coevolution-based algorithm is proposed for obtaining the practical action sets as well as achieving the mixed-strategy Nash equilibrium. Finally, relying on four real-world network systems, i.e., computer networks, Internet of vehicles, air transportation systems, and social networks, simulation results show the effectiveness and feasibility of our proposed model, which is conducive to the design, management, and maintenance of network systems.

Stackelberg game modeling of cloud security defending strategy in the case of information leaks and corruption

The paper presents the Stackelberg Game (SG) based model for automating security decisions in Cloud Computing systems (CC). The presented model enables to describe the attack-defense scenarios. The game incorporates two types of players competing against each other: defender and attacker. The Cloud provider is the leader. He is allowed to play his strategy first. The attackers, hackers or other malicious individuals, were aggregated into the second player. Second player’s decisions are made based on the leader actions and based on their own aims. The paper presents the black-box method for calculating the strategy of the attacker. In the paper, the utility function was obtained by applying several pipelines of Artificial Neural Networks (ANNs). Additionally, the model assumes information leakages about the attacker strategy and corruption against standard SG models. The solution has been verified by the experimental simulation of Cloud security attacks based on logs from open data set provided by Los Alamos National Security Lab. The best strategy for security controls applying is calculated based on security threats occurrence. It enables finding the relevant defense strategy by the cloud provider.

Non‐zero‐sum game‐based trust model to enhance wireless sensor networks security for IoT applications

Nowadays, trust models of wireless sensor networks (WSNs) security have flourished due to the day-to-day attack challenges, which are most popular for internet of things (IoT). This article proposes a trust model based on non-zero-sum game approach for clustered-WSNs (CWSNs) security to maximise the data trustworthiness transmission. The proposed model is developed for two different attack-defence scenarios. In the first scenario, the trust model is used to face a denial-of-service (DoS) attack in which the attacker is able to drop or partially drop the delivered acknowledgments (ACKs) from a cluster member (CM) to the cluster head (CH). In the second scenario, the model target is to protect CWSNs from ON–OFF attack where the attacker is capable to frequently infect the CMs. Simulation results show improved performance of protecting the CWSNs against DoS/ON–OFF attacks and maximising data trustworthiness represented by the CMs compliance of sending the ACKs to the CH. Consequently, this mechanism can attain the appropriate security and performance for WSN-based IoT systems.

Stackelberg games for modeling defense scenarios against cloud security threats

Stackelberg games may reveal to be extremely useful in supporting decisions in attack-defense scenarios. We call such games Security Stackelberg games. They are characterized by two kinds of players: the defender, who defines his strategy in advance, and the attacker, who follows the defender's decisions. Security Stackelberg games may be used to model a typical cloud security scenario, where the provider has to expose its defense strategy and the attackers act by following the provider's actions. In this work we define a model based on Stackelberg games that enables the automatic selection of provider-level security decisions in Cloud Computing environments. Potential Cloud attack scenarios are modeled as nonzero-sum Security Stackelberg games between the attacker and the Cloud provider. This allows the estimation of the strategy that minimizes the reward for the attacker and maximizes the gain of the defender. The model has been experimentally verified to be effective in automatically determining defense strategies from the cloud provider perspective.

BAIT: Behavior aided Intruder Testimony Technique for Attacker Intention Prediction in Business Data Handling

During business transactions there are lot of opportunity for data theft and data misinterpretation. Mostly, the legitimate users act like malicious users and try to misuse their privileges. So, it is very important to know their intentions and different strategies they apply for business data theft. In this paper, we develop an information analytics based technique for inferring attacker intent objectives and strategies (AIOS). The input to the model is the alert logs in real-world attack-defense scenario and output are the discovered attack strategies or patterns. The implementation of this model is done on a real-world attack-defence scenario to increase the learning efficiency of the technique. Experimental results on expected impact and attack path shows that the technique provides better results than conventional intrusion detection systems.

A Game Theoretic Software Test-bed for Cyber Security Analysis of Critical Infrastructure

<p class="p1">National critical infrastructures are vital to the functioning of modern societies and economies. The dependence on these infrastructures is so succinct that their incapacitation or destruction has a debilitating and cascading effect on national security. Critical infrastructure sectors ranging from financial services to power and transportation to communications and health care, all depend on massive information communication technology networks. Cyberspace is composed of numerous interconnected computers, servers and databases that hold critical data and allow critical infrastructures to function. Securing critical data in a cyberspace that holds against growing and evolving cyber threats is an important focus area for most countries across the world. A novel approach is proposed to assess the vulnerabilities of own networks against adversarial attackers, where the adversary’s perception of strengths and vulnerabilities are modelled using game theoretic techniques. The proposed game theoretic framework models the uncertainties of information with the players (attackers and defenders) in terms of their information sets and their behaviour is modelled and assessed using a probability and belief function framework. The attack-defence scenarios are exercised on a virtual cyber warfare test-bed to assess and evaluate vulnerability of cyber systems. Optimal strategies for attack and defence are computed for the players which are validated using simulation experiments on the cyber war-games testbed, the results of which are used for security analyses.</p>

Using Stackelberg game to enhance cognitive radio sensor networks security

The authors propose a game-theoretic approach using the Stackelberg game for securing cognitive radio sensor network (CRSN) against the spectrum sensing data falsification attack; this attack aims at corrupting the spectrum decisions communicated from the ambient sensor nodes (ASNs) to the fusion centre by imposing interference power. The proposed game approach is developed for two different attack–defence scenarios. In the first scenario, the attacker selects to attack a group of delivered reports of the ASNs that have a protection degree below a specific threshold. In the second scenario, the attacker applies its maximum attack interference power to the delivered reports of the ASNs that have been reported to be least protected in the previous round. Simulation results indicate the improved performance of the proposed protection model as compared with two baseline defence mechanisms, namely, the random and equal-protection defence mechanisms with static signal-to-noise ratio (SNR) and variable SNRs. Consequently, Stackelberg game features prove to be beneficial for securing communication over CRSN.

Probabilistic reasoning with graphical security models

This work provides a computational framework for meaningful probabilistic evaluation of attack–defense scenarios involving dependent actions. We combine the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. In order to improve the efficiency of probability computations on attack–defense trees, we make use of inference algorithms and encoding techniques from constraint reasoning. The proposed approach is illustrated on a running example and the computations are automated with the help of suitable software tools. We show that the computational routines developed in this paper form a conservative generalization of the attack–defense tree formalism defined previously. We discuss the algebraic theory underlying our framework and point out several generalizations which are possible thanks to the use of semiring theory. Finally, our results apply directly to the analysis of the industrially recognized model of attack trees.

Effective Proactive and Reactive Defense Strategies against Malicious Attacks in a Virtualized Honeynet

Virtualization plays an important role in the recent trend of cloud computing. It allows the administrator to manage and allocate hardware resources flexibly. However, it also causes some security issues. This is a critical problem for service providers, who simultaneously strive to defend against malicious attackers while providing legitimate users with high quality service. In this paper, the attack-defense scenario is formulated as a mathematical model where the defender applies both proactive and reactive defense mechanisms against attackers with different attack strategies. In order to simulate real-world conditions, the attackers are assumed to have incomplete information and imperfect knowledge of the target network. This raises the difficulty of solving the model greatly, by turning the problem nondeterministic. After examining the experiment results, effective proactive and reactive defense strategies are proposed. This paper finds that a proactive defense strategy is suitable for dealing with aggressive attackers under “winner takes all” circumstances, while a reactive defense strategy works better in defending against less aggressive attackers under “fight to win or die” circumstances.

Recovery and Resource Allocation Strategies to Maximize Mobile Network Survivability by Using Game Theories and Optimization Techniques

With more and more mobile device users, an increasingly important and critical issue is how to efficiently evaluate mobile network survivability. In this paper, a novel metric called Average Degree of Disconnectivity (Average DOD) is proposed, in which the concept of probability is calculated by the contest success function. The DOD metric is used to evaluate the damage degree of the network, where the larger the value of the Average DOD, the more the damage degree of the network. A multiround network attack-defense scenario as a mathematical model is used to support network operators to predict all the strategies both cyber attacker and network defender would likely take. In addition, the Average DOD would be used to evaluate the damage degree of the network. In each round, the attacker could use the attack resources to launch attacks on the nodes of the target network. Meanwhile, the network defender could reallocate its existing resources to recover compromised nodes and allocate defense resources to protect the survival nodes of the network. In the approach to solving this problem, the “gradient method” and “game theory” are adopted to find the optimal resource allocation strategies for both the cyber attacker and mobile network defender.

On Adversarial Games in Dynamic Spectrum Access Networking based Covert Timing Channels?

In this paper, we study tactical covert timing networks with dynamic spectrum access capability amidst adversaries. We present a two-tier game framework to model the attackdefense scenario. There are very few studies available in the literature on covert timing channels with multiple parallel transmissions. This paper presents a new paradigm combining the time diversity provided by covert timing channels and frequency diversity provided by dynamic spectrum switching, to combat jamming. The dynamic sensing of different spectrum bands and subsequent jamming by the attacker, and the camouflaging defense by the covert network are modeled as a two-tier game. We present a dynamic minimax camouflaging strategy for the covert network and sensing and jamming strategies for the attacker. We compare the performance of our proposed equilibrium strategies with that of other well known strategies and demonstrate the effectiveness of our proposed solution. We use theoretical analysis, simulations and testbed experiments to illustrate our ideas.