Attack Tolerance Research Articles

Securing digital authentication with cryptographic innovations in intrinsic verification systems

In the digital era, authentication system’s security is crucial. This paper discusses the issue of improving digital authentication from the position of cryptographic improvements in intrinsic verification structures. The emphasis is made on creation and implementation of efficient cryptographic protocols that reinforce security in digital authentication procedures. This research focuses on developing a new cryptographic framework that is capable of improving the attack tolerance of intrinsic verification systems against various kinds of cyber-attacks such as identity theft and data breach. This structure takes advantage of innovative cryptographic algorithms, including quantum-resistant techniques that secure authentication records containing sensitive information. The Author conducts further feasibility study of these innovations that can be applied in practical settings, quantifying the success or failure through simulations. The outcomes evidenced significant security features improvements, indicating that such cryptographic developments have the potential to revolutionize digital authentication. This study is not only coming up with the solution, Prescence to current security challenges but also has shown path for development of secure digital authentication system in an interconnected world.

Inherent attack tolerance properties of model predictive control under DoS attacks

We consider the inherent attack tolerance properties of resilient model predictive control (R-MPC) for cyber–physical systems (CPSs) modeled by discrete linear time-invariant (LTI) systems subjected to limited disruptions. In this paper, the relationship between the maximum allowable duration of Denial-of-Service (DoS) attacks, namely attacks that disrupt both sensor to controller (S–C) and controller to actuator (C–A) communication channels, and the upper bound of disturbances is deduced at length. Moreover, to achieve robust recursive feasibility and closed-loop stability of the system, we discuss that MPC with state, control and terminal constraints has a certain degree of inherent attack tolerance concerning the DoS attack duration and parameters like system matrices and the nominal terminal set. Finally, numerical simulation is given to substantiate the effectiveness of the proposed approach.

Interconnectedness enhances network resilience of multimodal public transportation systems for Safe-to-Fail urban mobility

The growing interconnectedness of urban infrastructure networks presents challenges to their ability to handle unforeseen disruptions, particularly in the context of extreme weather events resulting from climate change. Understanding the resilience of interconnected infrastructure systems is imperative to effectively manage such disruptions. This study investigates the role of interconnectedness in enhancing the resilience of public transportation systems in Hong Kong, a city heavily reliant on public transit. Our results demonstrate that interconnected transportation systems improve resilience by reducing topological vulnerabilities, increasing attack tolerance, and enhancing post-disruption interoperability. Findings also identify the potential to integrate vulnerable systems for greater robustness and highlight the marginal benefits of enhancing intermodal transfer. Strengthening interconnectedness among modes of urban public transit fosters a safe-to-fail system, presenting a distinct resilience-by-design approach. This complements conventional resilience-by-intervention approaches that focus on improving individual systems or introducing entirely new systems.

Designing functionalized graphene-stitched-SiC/fluoropolymer novel composite coating with excellent corrosion resistance and hydrogen diffusion barrier properties

The investigation on ordinary hydrogen barrier composite coatings has been limited in terms of corrosion and high-pressure hydrogen environments, which significantly restricts its practical and industrial applications. Herein, a unique nanohybrid two-dimensional (2D) filler, constituting poly-dopamine (PDA) functionalized graphene (Gr) nanosheets and covalently bridged silicon carbide (SiC) nanoflakes assisted by 3-Isocyanatopropyltrimethoxysilane (IPTMS) via hydrolysis reaction, was innovatively proposed and incorporated into fluoroethylene vinyl ether resin (FEVE) to fabricate final composite coating by spin-coating technique. The hydrogen gas barrier and long-term anti-corrosion performances could be simultaneously enhanced by precisely collaborative effects of interfacial interactions fortification among Gr, SiC, and polymer phase as well as centrifugal force promoted Gr orientation during the spinning process. The optimum sample demonstrated high |Z|0.01Hz values of 3.1 × 1011 Ω cm2 and 3.37 × 1011 Ω cm2 throughout 90 d saltwater immersion and 6-day 1.5 MPa pressurized hydrogen environment exposure respectively. Moreover, its permeability coefficient decreased by 75.66% compared to the pure sample. Other significant properties including 30d constant salt-spray attack tolerance and high adhesion strength of 12.46 MPa were also achieved to meet industrial demands. This work provided paramount inspiration for designing composite coatings for hydrogen transportation pipeline protection and hydrogen energy usage.

Dual-Triggered Adaptive Torque Control Strategy for Variable-Speed Wind Turbine Against Denial-of-Service Attacks

The increasing utilization of wind energy necessitates dedicated attentions to identify vulnerabilities, raise awareness, and formulate strategies for cybersecurity defense, response, and future protection. As a potential cyber attack, denial-of-service (DoS) attacks that block the communication between the rotor speed sensor and the controller of the wind turbine may overload the drive-train and reduce the power generation efficiency. This paper attempts to propose a dual-triggered resilient torque control strategy for the variable-speed wind turbine (VSWT) against DoS attacks. Therein, the mathematical models including the dynamics of the WT and the DoS attack are established, and a neural network observer is designed for the WT system to compensate for the signal losses caused by DoS attack. To guarantee the optimal rotor speed tracking of the WT, an adaptive torque control method based on dual-triggered mechanism is developed, and the attack tolerance capability of the WT system is evaluated. The proposed control strategy can largely enhance the attack resilience capability of the WT, while saving the resources of computation and communication. Extensive studies on a 1.5 MW WT illustrate that the proposed adaptive resilient control scheme can effectively mitigate the impact of the DoS attacks and guarantee the optimal rotor speed tracking performance for the WT system.

On Attack-Resilient Service Placement and Availability in Edge-Enabled IoV Networks

Achieving network resilience in terms of attack tolerance and service availability is critically important for Internet of Vehicles (IoV) networks where vehicles require assistance in sensitive and safety-critical applications like driving. It is particularly challenging in time-varying conditions of IoV traffic. In this paper, we study an attack-resilient optimal service placement problem to ensure disruption-free service availability to the users in edge-enabled IoV network. Our work aims to improve the user experience while minimizing the delay and simultaneously considering efficient utilization of limited edge resources. First, an optimal service placement is performed while considering traffic dynamicity and meeting the service requirements with the use of a deep reinforcement learning (DRL) framework. Next, an optimal secondary mapping and service recovery placements are performed to account for the attacks/failures at the edge. The use of DRL framework helps to adapt to dynamically varying IoV traffic and service demands. In this work, we develop three ILP models and use them in the DRL-based framework to provide attack-resilient service placement and ensure service availability with efficient network performance. Extensive numerical experiments are performed to demonstrate the effectiveness of the proposed approach.

Integrated Security Control for Nonlinear CPS with Actuator Fault and FDI Attack: An Active Attack-Tolerant Approach

This paper investigated the co-design problem of less conservative integrated security control and communication for a nonlinear cyber-physical system (CPS) with an actuator fault and false data injection (FDI) attacks. Firstly, considering the efficient utilisation and allocation of computing and communication resources, an integrated framework was proposed from the perspective of active defence against FDI attacks. Secondly, the actuator fault and FDI attacks were augmented as a vector, and a robust observer was proposed to estimate the system state, actuator fault and FDI attacks. Furthermore, based on the obtained estimation results and the location of the FDI attack in the dual-end network, we designed an integrated security control strategy of active attack tolerance and active fault tolerance and, by constructing Lyapunov–Krasovskii functions and using time-delay system theory and the affine Bessel–Legendre inequality, a less conservative co-design method for integrated security control and network communication resource saving was developed. Finally, a simulation experiment of a quadruple tank was carried out to demonstrate the effectiveness of the proposed method.

The responses of mesophilic and thermophilic anaerobic digestion of municipal sludge to periodic fluctuation disturbance of organic loading rate

Fluctuation disturbance of organic loading rate (OLR) is common in actual anaerobic digestion (AD), but its effects on AD of municipal sludge gets little attention. This study investigated the responses of reactor performance and active microbial community in mesophilic and thermophilic AD of municipal sludge before, during and after OLR periodic fluctuation disturbance. The performance of both reactors were similar before and after disturbance although some parameter values changed during the disturbance, which indicated their enough buffer capacity to OLR periodic fluctuation. Different microbial community at RNA level was observed in the two reactors. When the OLR disturbance commenced, the microbial community changed greatly in thermophilic AD. Error and attack tolerance of the microbial network was analyzed in order to learn the response mechanisms to OLR disturbance. The results assisted that the thermophilic microbial community was more vulnerable, but the reactor performance of which could be maintained using the functional redundancy strategy under OLR fluctuation disturbance.

Secure Distributed Adaptive Platooning Control of Automated Vehicles Over Vehicular Ad-Hoc Networks Under Denial-of-Service Attacks.

This article deals with the problem of secure distributed adaptive platooning control of automated vehicles over vehicular ad-hoc networks (VANETs) in the presence of intermittent denial-of-service (DoS) attacks. The platoon, which is wirelessly connected via directed vehicle-to-vehicle (V2V) communication, is composed of a group of following vehicles subject to unknown heterogeneous nonlinearities and external disturbance inputs, and a leading vehicle subject to unknown nonlinearity and external disturbance as well as an unknown control input. Under such a platoon setting, this article aims to accomplish secure distributed platoon formation tracking with the desired longitudinal spacing and the same velocities and accelerations guided by the leader regardless of the simultaneous presence of nonlinearities, uncertainties, and DoS attacks. First, a new logical data packet processor is developed on each vehicle to identify the intermittent DoS attacks via verifying the time-stamps of the received data packets. Then, a scalable distributed neural-network-based adaptive control design approach is proposed to achieve secure platooning control. It is proved that under the established design procedure, the vehicle state estimation errors and platoon tracking errors can be regulated to reside in small neighborhoods around zero. Finally, comparative simulation studies are provided to substantiate the effectiveness and merits of the proposed control design approach on maintaining the desired platooning performance and attack tolerance.

RSS based multistage statistical method for attack detection and localization in IoT networks

Accurate and reliable positioning of nodes is a must for location-based services (LBS) in the Internet of things (IoT) networks. The LBS are ubiquitous and an easy target for non-cryptographic attacks that traditional security methods cannot address. In this work, we detect the Received Signal Strength (RSS) based attacks that affect the localization of smart devices in the IoT networks and report the attack tolerance of popular IoT protocols. A two-tier ratio metric method and Residue Under Curve (RUC) metric method is utilized to detect the malicious node in the IoT protocols. We propose to use a novel Geometric, and Arithmetic Mean (GM–AM) ratio as a feature to detect the RSS attacks where GM follows strictly Schur-Concavity property and AM follows non-strict concavity property. We evaluate the performance of the proposed method on real-world IoT testbeds with Wireless Fidelity (Wi-Fi), Zigbee, Bluetooth Low Energy (BLE), and Long-Range Wide-Area Network (LoRaWAN) protocols using the RSS values of these opportunistic signals. Also, the effect of RSS attacks on the localization for different protocols is investigated, and we report the method that provides the least localization error under these attacks.

Deep Learning for Securing Software-Defined Industrial Internet of Things: Attacks and Countermeasures

Software-defined networking (SDN) has become an attractive solution to carry out centralized and efficient control in Industrial Internet of Things (IIoT). However, its security has received little attention when applied to IIoT, and no comprehensive consideration has been given to attacks against forwarding nodes (FNs), the basic elements of the data plane. Therefore, in this article, we aim to investigate attacks against FNs from multiple perspectives in software-defined IIoT. To the best of our knowledge, we are the first to systematically consider this kind of attacks. Since it is difficult to predeploy all defense methods against various attacks, we propose a deep reinforcement learning (DRL)-based general attack tolerance scheme to guide the benign traffic flow bypass the attacked FNs. Furthermore, in view of the situation that the real data set is rare and the standard model-based data set is likely to be impractical, we use generative adversarial network (GAN), a representative deep generative model (DGM), to flexibly generate real-like network traffic for more sufficient and effective experimental verification on the attack tolerance scheme. Experimental results show that our proposed scheme can significantly improve the successful arrival rate of IIoT traffic and achieve near-optimal results.

Effect of dormant spare capacity on the attack tolerance of complex networks

The vulnerability of networks to targeted attacks is an issue of widespread interest for policymakers, military strategists, network engineers and systems biologists alike. Current approaches to circumvent targeted attacks seek to increase the robustness of a network by adding or swapping edges (Edge Addition (EA) or Edge Swapping (ES) method respectively) that ultimately leads to a higher size of the largest connected component for a given fraction of nodes removed when compared to that of the original network. In this work, we propose a strategy in which there is a pre-existing, dormant spare capacity already built into the network for an identified vulnerable node, such that the traffic of the disrupted node can be diverted to another pre-existing node/set of nodes in the network. Using our algorithm, the increase in robustness of canonical scale-free networks was nearly 14-fold. We also analysed real-world networks using our algorithm, where the mean increase in robustness was nearly 5-fold. We have compared our work with the results obtained from other EA and ES algorithms and have found the increase in robustness using our algorithm to be significant in light of what has been reported to our knowledge in the literature. The cost of this spare capacity and its effect on the operational parameters of the network have also been discussed.

Research on Dual Security Control for a Non-Linear CPS with Multi-Objective Constraints under DoS Attack and Actuator Fault: An Active-Passive Attack-Tolerant Approach

Based on the discrete event-triggered communication scheme (DETCS), a codesign method of dual security control and communication for the non-linear cyberphysical system (CPS) with denial-of-service (DoS) attack and actuator fault is studied under multi-objective constraints. First, to effectively distinguish DoS attacks with different energy levels, the DoS attack detection method is established based on the maximum allowable delay of the system. Second, a non-linear CPS dual security control framework that integrates active-passive attack tolerance for DoS attack and active fault tolerance for actuator fault is constructed. Third, the low-energy DoS attack is regarded as a special kind of time delay, and a robust observer for the estimation of system states and actuator fault is designed, and a codesign method of passive attack tolerance, active fault tolerance, and communication is proposed. Furthermore, a correction compensation algorithm of control quantity is proposed for high-energy DoS attack based on the PID idea to make active-passive attack tolerance more effective. Finally, the effectiveness of the proposed method is verified by the simulation of a quadruple-tank model.

Network tolerance optimisation to random and target attacks based on percolation theory

A social network system has failure characteristics for random attacks of components or target attacks. This paper constructs related models for complex network defence systems to support the integrity of the social network system. First, we discuss the impact of component failure on complex systems and determine the risk scope. Second, based on the attack tolerance of the percolation theory, we verify the robustness of the network system through the percolation threshold fc to determine its optimal distribution. Third, we build a bimodal-distributed network model based on the network optimality to resist network failure. The model simulation results show that when the degree node is Kmin > 1 and Kmax = AN2/3 in the complex networks, these nodes themselves form a largest cluster to guarantee the integrity of the network system, and to ensure that the network is still robust to subsequent attacks after the removal of the central hub nodes.

Identifying early-warning indicators of onset of sudden collapse in networked infrastructure systems against sequential disruptions

Tsunamis, power blackouts, and distribution systems failure drastically affect the networked infrastructure systems which further affect a countries economy. Moreover, if these systems reach critical thresholds, they may experience disproportionate losses in the system’s functionality. Here we propose an approach to identify the critical thresholds and observe the presence of warning regions for real-world transportation systems. While attack tolerance of networked systems has been intensively studied for the disruptions originating from a single point of failure, there have been instances where real-world systems are subject to concurrent disruptions at multiple locations. We determine the entire robustness characteristics of transportation networks of disparate architecture subject to disruptions of varying sizes. Using United States Airspace Airport network and Indian Railways Network data, and synthetic networks as prototype class of systems, we study their responses to synthetic attack strategies of varying sizes. We also observe the significant relationships between network robustness and size of simultaneous disruptions for the complex networked infrastructures for random failures and targeted attacks. Our approach can serve as a paradigm to understand the point of sudden collapse in real-world systems, and the principle can be extended to other network infrastructures to address critical issues of risk management, resilience, and system safety.

Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

AbstractAbrupt development of resources and rising expenses of infrastructure are leading institutions to take on cloud computing. Albeit, the cloud environment is vulnerable to various sorts of attacks. So, recognizing malicious software is one of the principal challenges in cloud security governance. Intrusion detection system (IDS) has turned to the most generally utilized element of computer system security that asserts the cloud from diverse sorts of attacks and threats. As evident, no systematic literature review exists that focuses on cloud computing usage within IDS processes. The previous investigations had not considered the statistical analysis method. Hence, this paper examined the IDS mechanisms in cloud computing systematically. Twenty‐two articles have been obtained using defined filters divided into four sections: hypervisor‐based IDS, network‐based IDS, machine learning‐based IDS, and hybrid IDS. The comparison is performed depending on the outcomes illustrated in the investigations. It demonstrates that IDS precision, inclusiveness, overhead, and reaction time have been discussed in many studies. Simultaneously, less attention has been paid to cost‐sensitivity, functioning, attack tolerance, and intrusion facing. This paper has made an excellent effort to organize literature drawn from multiple sources into a manuscript.

A Framework for the Attack Tolerance of Cloud Applications Based on Web Services

Information systems of companies and organizations are increasingly designed using web services that allow different applications written in different programming languages to communicate. These systems or some parts of them are often outsourced on the cloud, first to leverage the benefits of cloud platforms (e.g., scalability) and also to reduce operational costs of companies as well. However, web services as well as cloud platforms may be the target of attacks that alter their security, and the security of web services is not completely addressed. The solutions proposed in the literature are sometimes specific to certain types of attacks and they cannot ensure the attack tolerance of web services. Attack tolerance can be defined as the capability of a system to function properly with minimal degradation of performance, even if the presence of an attack is detected. As such, we claim that, to achieve attack tolerance, one should detect attacks by a continuous monitoring and mitigate the effects of these attacks by reliable reaction mechanisms. For this aim, an attack tolerance framework is proposed in this paper. This framework includes the risks analysis of attacks and is based on diversification and software reflection techniques. We applied this framework to cloud applications that are based on web services. After describing the core foundation of this approach, we express such cloud applications as choreographies of web services according to their distributed nature. The framework has been validated through an electronic voting system. The results of these experiments show the capability of the framework to ensure the required attack tolerance of cloud applications.

Load altering attack-tolerant defense strategy for load frequency control system

Cyber attacks become emerging threats to every information-oriented energy management system. By violating the cyber systems, the hacker can disrupt the security and stability due to the strong coupling between the cyber and physical facilities. In this paper, one type of cyber attacks designated as the load altering attack is studied for the power system frequency control, and corresponding defense strategies are proposed to improve the frequency control performance. Considering the difficulty of the application of model-based controller into large-scale power systems, a novel model-free defense framework is for the first time presented. Under this framework, both active defense and passive defense strategies are designed. The former assumes that the defender has the initiative to learn different attack scenarios. Adaptive defense strategies are implemented using the online attack identification information and off-line trained strategy pool. The latter assumes that the defender passively tolerates various attack scenarios via the pre-trained off-line strategy. Both approaches prove to be effective through validation based on the IEEE benchmark systems. The proposed defense framework and defense strategies can be extended to other energy control systems to enhance their attack tolerance capability.

An adaptive defense mechanism to prevent advanced persistent threats

The expansion of information technology infrastructure is encountered with Advanced Persistent Threats (APTs), which can launch data destruction, disclosure, modification, and/or Denial of Service attacks by drawing upon vulnerabilities of software and hardware. Moving Target Defense (MTD) is a promising risk mitigation technique that replies to APTs via implementing randomisation and dynamic strategies on compromised assets. However, some MTD techniques adopt the blind random mutation, which causes greater performance overhead and worse defense utility. In this paper, we formulate the cyber-attack and defense as a dynamic partially observable Markov process based on dynamic Bayesian inference. Then we develop an Inference-Based Adaptive Attack Tolerance (IBAAT) system , which includes two stages. In the first stage, a forward–backward algorithm with a time window is employed to perform a security risk assessment. To select the defense strategy, in the second stage, the attack and defense process is modelled as a two-player general-sum Markov game and the optimal defense strategy is acquired by quantitative analysis based on the first stage. The evaluation shows that the proposed algorithm has about 10% security utility improvement compared to the state-of-the-art.

An empirical attack tolerance test alters the structure and species richness of plant–pollinator networks

Abstract Ecological network theory hypothesizes that the structuring of species interactions can convey stability to the system. Investigating how these structures react to species loss is fundamental for understanding network disassembly or their robustness. However, this topic has mainly been studied in‐silico so far. Here, in an experimental manipulation, we sequentially removed four generalist plants from real plant–pollinator networks. We explored the effects on, and drivers of, species and interaction disappearance, network structure and interaction rewiring. First, we compared both the local extinctions of species and interactions and the observed network indices with those expected from three co‐extinction models. Second, we investigated the trends in network indices and rewiring rate after plant removal and the pollinator tendency at establishing novel links in relation to their proportional visitation to the removed plants. Furthermore, we explored the underlying drivers of network assembly with probability matrices based on ecological traits. Our results indicate that the cumulative local extinctions of species and interactions increased faster with generalist plant loss than what was expected by co‐extinction models, which predicted the survival or disappearance of many species incorrectly, and the observed network indices were lowly correlated to those predicted by co‐extinction models. Furthermore, the real networks reacted in complex ways to plant removal. First, network nestedness decreased and modularity increased. Second, although species abundance was a main assembly rule, opportunistic random interactions and structural unpredictability emerged as plants were removed. Both these reactions could indicate network instability and fragility. Other results showed network reorganization, as rewiring rate was high and asymmetries between network levels emerged as plants increased their centrality. Moreover, the generalist pollinators that had frequently visited both the plants targeted of removal and the non‐target plants tended to establish novel links more than who either had only visited the removal plants or avoided to do so. With the experimental manipulation of real networks, our study shows that despite their reorganizational ability, plant–pollinator networks changed towards a more fragile state when generalist plants are lost. A free Plain Language Summary can be found within the Supporting Information of this article.