Attack Model Research Articles

Towards accurate keyspace analysis of chaos-based image ciphers

In recent years, there has been a surge in new chaos-based cryptographic algorithms, many of which claim to have unusually large keyspaces. Although cryptographic primitives such as symmetric-key ciphers should have a secret keyspace large enough to resist brute force attacks, simply increasing the size of a secret key may not lead to improved security margins. An n-bit key may not necessarily have a keyspace of 2n-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2^n-1$$\\end{document} due to the key scheduling algorithm or how the key is used. In this paper, we cryptanalyse several chaos-based algorithms from the perspective of their key schedules. Our numerical analysis is based on the known-plaintext attack model, Kerckhoff’s principle and considers the number representations used for real number computation. Our analysis reveals that the actual security margins for these ciphers are significantly lower, some by a factor of over 2100\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2^{100}$$\\end{document} than what was claimed. We then provide accurate keyspace estimates for these ciphers. Finally, we highlight alternative solutions for how secret keys can be used in the context of chaos-based cryptography and propose a simple key schedule as a proof of concept. Despite its simplicity, the proposed key schedule not only ensures that the keyspace matches the key length but also passes both the NIST and ENT statistical test suites, making it a viable option for generating secure cryptographic keys. Our work contributes towards addressing one of the fundamental problems in chaos-based cryptography that limits its real-world impact and reputation within the cryptographic community.

Dynamic sliding window‐cooperative spectrum sensing against massive SSDF attack in interweave cognitive internet of things

AbstractTo improve spectrum utilization, cognitive radio (CR) enables unauthorized internet of thing (IoT) devices to opportunistically access the channel underutilized by the primary user (PU) in a cognitive IoT (CIoT). To this end, cooperative spectrum sensing (CSS) plays a crucial role in CR technology, which leverages the spatial diversity of corroborative IoT devices to accurately detect the PU signal. However, this open cooperative paradigm may suffer from spectrum sensing data falsification (SSDF) attack in which malicious IoT devices intentionally mislead the fusion center (FC) by providing false sensing results to make an incorrect global decision regarding the PU status. To effectively characterize the attack behavior of malicious IoT devices, we propose a massive SSDF attack model described by the attack cycle and malicious ratio within a sensing period to characterize the malicious behaviors. Additionally, we introduce a delivery evaluation mechanism and propose a dynamic sliding window‐CSS (DSW‐CSS) to mitigate the impact of massive SSDF attack. Moreover, we introduce a sequential reporting mechanism to further reduce the number of samples required by the global decision‐making of the FC. Finally, simulation results show the flexibility and aggressiveness of the proposed attack model and demonstrate the correctness and effectiveness of DSW‐CSS.

Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes.

Prognostic models hold great potential for predicting asthma exacerbations, providing opportunities for early intervention, and are a popular area of current research. However, it is unclear how models should be compared and contrasted, given their differences in both design and performance, particularly with a view to potential implementation in routine practice. This systematic review aimed to identify novel predictive models of asthma attacks in adults and compare differences in construction related to populations, outcome definitions, prediction time horizons, algorithms, validation, and performance estimation. Twenty-five studies were identified for comparison, with varying definitions of asthma attacks and prediction event time horizons ranging from 15 days to 30 months. The most commonly used algorithm was logistic regression (20/25 studies); however, none of the six which tested multiple algorithms identified it as highest performing algorithm. The effect of various study design characteristics on performance was evaluated in order to provide context to the limitations of highly performing models. Models used a variety of constructs, which affected both their performance and their viability for implementation in routine practice. Consultation with stakeholders is necessary to identify priorities for model refinement and to create a benchmark of acceptable performance for implementation in clinical practice.

Improved Search for Integral, Impossible Differential and Zero-Correlation Attacks

Integral, impossible-differential (ID), and zero-correlation (ZC) attacks are three of the most important attacks on block ciphers. However, manually finding these attacks can be a daunting task, which is why automated methods are becoming increasingly important. Most automatic tools regarding integral, ZC, and ID attacks have focused only on finding distinguishers rather than complete attacks. At EUROCRYPT 2023, Hadipour et al. proposed a generic and efficient constraint programming (CP) model based on satisfiability for finding ID, ZC, and integral distinguishers. This new model can be extended to a unified CP model for finding full key recovery attacks. However, it has limitations, including determining the contradiction location beforehand and a cell-wise model unsuitable for weakly aligned ciphers like Ascon and PRESENT. They also deferred developing a CP model for the partial-sum technique in key recovery as future work.In this paper, we enhance Hadipour et al.’s method in several ways. First, we remove the limitation of determining the contradiction location in advance. Second, we show how to extend the distinguisher model to a bit-wise model, considering the internal structure of S-boxes and keeping the model based on satisfiability. Third, we introduce a CP model for the partial-sum technique for the first time. To show the usefulness and versatility of our approach, we apply it to various designs, from strongly aligned ones like ForkSKINNY and QARMAv2 to weakly aligned ones such as Ascon and PRESENT, yielding significantly improved results. To mention a few of our results, we improve the integral distinguisher of QARMAv2-128 (resp. QARMAv2-64) by 7 (resp. 5) rounds, and the integral distinguisher of ForkSKINNY by 1 round, only thanks to our cell-wise distinguisher modelings. By using our new bit-wise modeling, our tool can find a group of 2155 5-round ID and ZC distinguishers for Ascon in only one run, taking a few minutes on a regular laptop. The new CP model for the partial-sum technique enhances integral attacks on all SKINNY variants, notably improving the best attack on SKINNY-n-n in the single-key setting by 1 round. We also enhance ID attacks on ForkSKINNY and provide the first analysis of this cipher in a limited reduced-round setting. Our methods are generic and applicable to other block ciphers.

Ethical hacking: real evaluation model of brute force attacks in password cracking

Despite ongoing efforts to convince users of the value of password security and to enforce password creation standards on them, in many information systems the human factor still plays a role. In addition, not only do most users’ password creation and management practices largely remain unchanged, but password cracking tools and more critically, computer hardware also continue to advance. In this paper we present a model in ethical hacking; the proposed model concentrated on brute force attacks for password cracking. The main novelty of our work is that it first presents a mathematical model that calculates the number of different password permutations of varying lengths. Then the brute force attack is modelled using the Markov chain model and a method is developed to formulate the conventional optimization problem, which is classified as a discrete nonlinear problem. The experiments’ results demonstrate and validate the method’s effectiveness and suitability.

Communication-Efficient and Resilient Distributed Q-Learning.

This article investigates the problem of communication-efficient and resilient multiagent reinforcement learning (MARL). Specifically, we consider a setting where a set of agents are interconnected over a given network, and can only exchange information with their neighbors. Each agent observes a common Markov Decision Process and has a local cost which is a function of the current system state and the applied control action. The goal of MARL is for all agents to learn a policy that optimizes the infinite horizon discounted average of all their costs. Within this general setting, we consider two extensions to existing MARL algorithms. First, we provide an event-triggered learning rule where agents only exchange information with their neighbors if a certain triggering condition is satisfied. We show that this enables learning while reducing the amount of communication. Next, we consider the scenario where some of the agents can be adversarial (as captured by the Byzantine attack model), and arbitrarily deviate from the prescribed learning algorithm. We establish a fundamental trade-off between optimality and resilience when Byzantine agents are present. We then create a resilient algorithm and show almost sure convergence of all reliable agents' value functions to the neighborhood of the optimal value function of all reliable agents, under certain conditions on the network topology. When the optimal Q -values are sufficiently separated for different actions, we show that all reliable agents can learn the optimal policy under our algorithm.

Stochastic important‐data‐based attack model and defense strategies for cyber‐physical system: A data‐driven method

AbstractThis article investigates the security ensured state estimation problem for cyber‐physical system via a data‐driven method. First, based on the fact that different packets possess varying a degree of significance, that is, some packets play more important roles in the state estimation than others, a novel stochastic important‐data‐based (IDB) attack mechanism is constructed from the attacker's perspective, which can focus on attacking the important packets thus is expected to achieve more destructiveness. Second, as a countermeasure to the proposed IDB attack, a new data‐driven compensation method is proposed, for the first attempt, to compensate for the attack effect and enhance the estimation quality. The designed defense strategy has the following two advantages: (1) only system input and output data are utilized to establish the novel estimator, without knowing the actual system model knowledge, and (2) by constructing a data‐driven output predictor to compensate for the data loss, the accuracy of the state estimation can be efficiently improved. With the aid of the least squares technique and completing square technique, a minimum upper bound matrix for the estimation error covariance is obtained by properly designing the estimator gain. Finally, an illustrative example is given to highlight the destructiveness of the designed stochastic IDB attack and the effectiveness of the proposed novel data‐based compensation method.

Military logistics planning models for enemy targets attack by a swarm of combat drones

This article describes and investigates the planning aspect of military actions aimed at destroying enemy targets with the help of an attack drone swarm. This study attempts to solve the task of operational-tactical planning of a massive attack on enemy targets with the help of combat drones, which have different combat potential characteristics. It analyzes the problems of unmanned aerial vehicles (UAVs) swarms’ usage, which ensures maximum efficiency during combat operations. The article shows that in order to plan effective military operations, it is necessary to form the following logistical sequence: identification of relevant targets set, formation of drones into a swarm to attack targets, distribution of drones by targets, and planning flight routes of a drone swarm in conditions of military threats. It concludes that for the effective use of a combat drone swarm, it is necessary to plan logistical actions in advance to inflict maximum damage on the enemy and successfully fulfill the operational and tactical goals of the military leadership. The purpose of this study is to create information technology models that will allow planning logistical military actions for the effective use of combat drone swarms to defeat enemy targets. This article describes a systematic representation of logistical military operations for combat drone swarms. It also analyzes enemy targets, which are represented in the form of a priority list with the characteristics of relevance, the necessary combat potential to hit the targets, the risks of approaching the targets, and the flight time of the drones. From the list of targets, a sublist is formed, considering the combat potential of the drone swarm and the necessary potential to defeat the selected enemy targets. The optimization model helps to distribute the swarm of drones into groups to achieve the enemy targets and destroy them. The movement of drones is planned considering flight zones, possible anti-drone actions of the enemy, and the risks of military threats. Any Logic agent simulation platform can be used to create a simulated flight model of a drone swarm to selected enemy targets. Modeling makes it possible to form rational flight routes of a drone swarm under conditions of military threats from the enemy. An example is given to illustrate the formation of logistical actions for planning a massive attack on enemy targets with the help of a drone swarm. The scientific novelty of this study is related to the solution of the urgent problem of planning logistical military operations for the effective use of a combat drone swarm to destroy enemy targets. The results of this study should be used for the operational and tactical planning of logistical military operations to defeat enemy targets with the help of a combat drone swarm.

A Petri net model for Time‐Delay Attack detection in Precision Time Protocol‐based networks

AbstractAlong with the development of industrial and distributed systems, security concerns have also emerged in industrial communication protocols. PTP, Precision Time Protocol, is one of the most precise time synchronisation protocols for industrial devices. It ensures real‐time activity of the industrial control systems with precision equal to microseconds. In order to address the actual or potential security issues of PTP, this article firstly describes attack models applicable to PTP and then focuses on applying Coloured Petri Net to formally analyse the attack detection methods and also model PTP. The alignment of simulation results with the model and the considered assumptions show the suitability and accuracy of the proposed model.

Threats, Attacks, and Cryptography Frameworks of Cybersecurity in Critical Infrastructures

Critical Infrastructures (CIs), such as healthcare facilities, power grids, transportation systems, and financial institutions, are vital components of a functioning society, with the economy and safety being dependent on them. Nevertheless, they have become increasingly vulnerable to cyber threats and attacks in recent years. The main reason is their inability to quickly adapt to technological changes, employ updated cryptographic frameworks, and implement a thoroughly secure architecture based on their characteristics. In this study, the unique complexities of these systems are highlighted. Various verified cyberattacks that were executed against CIs in recent years are analyzed. Moreover, the general framework of CIs is demonstrated together with the employed technologies and cryptographic primitives. A thorough architecture of said technologies is developed to better understand the targeted components and easily identify potentially hidden threats. Afterwards, threat, adversary, and attack models that target critical systems and services are designed. The purpose is a better comprehension of the systems’ vulnerabilities, attack structures, motives, and targets for assisting CIs’ designers in creating secure frameworks and mechanisms, with the ability to mitigate such threats. Lastly, security controls and cryptography frameworks are demonstrated together with efficient mitigation architectures and implementations from the research community.

Hybrid‐triggered predictive control of cyber‐physical‐systems under cyber‐attacks

AbstractIn this article, the hybrid‐triggered predictive control of cyber‐physical systems (CPSs) under cyber‐attacks is researched. Firstly, considering the unpredictable state of the CPSs, an observer‐based model is constructed, and the predictor is put forward to predict state. Secondly, a hybrid‐triggered scheme is adopted to avoid unnecessary‐data transmission by introducing a hybrid switch mode. Thirdly, the deception attacks and DoS attacks are existed in sensor‐to‐observer (S‐O) and observer‐to‐controller (O‐C), separately. Two types of attack modeling are established based on their characteristics. With the proposed control method, some sufficient conditions are obtained to ensure the input‐to‐state stability (ISS) of CPSs. Finally, the results of simulation examples are received to testify the advantage of developed control strategy.

Output feedback pinning control for complex dynamical networks subjected to multiple attacks

This paper is concentrated on the secure synchronization control issue for complex dynamical networks (CDNs) subjected to multiple attacks. For the sake of conserving resources and being closer to reality, a new output feedback controller based on pinning nodes is firstly designed. Secondly, in contrast with the existent researches on secure network circumstances, we establish a novel multiple attacks model considering the influence of random deception attacks and aperiodic Denial-of-Service (DoS) attacks. Based on the piecewise Lyapunov-Krasovskii function and decoupling method, a new secure synchronization criterion is proposed to realize the mixed H∞ and passive performance of CDNs against multiple attacks, and the control gain is gained. Lastly, the availability of the raised consequence is testified by two concrete examples.

Adaptive event–based finite-time H∞ filtering for networked systems under hybrid attack

The event-based finite-time [Formula: see text] filtering problem for networked systems is concerned. A novel hybrid attack model is introduced considering denial-of-service and deception attacks in the communication channel between sampler and filter with an aperiodic form. In order to increase the effectiveness of data transmission in network communication channel and enable the filtering system against attack, an adaptive event–triggered mechanism is proposed. Sufficient condition for ensuring exponential mean-square finite-time boundedness of the filtering error system with an expected [Formula: see text] disturbance attenuation index is obtained. The design of the filter and the adaptive event–triggered mechanism is derived. A practical example is given to demonstrate the effectiveness of the proposed filtering scheme in an active suspension system.

The Modeling and Detection of Attacks in Role-Based Self-Organized Decentralized Wireless Sensor Networks

This article discusses the modeling and detection of attacks in self-organizing decentralized wireless sensor networks (WSNs) that can be applied to various critical scenarios in practice. Security issues in this type of network have previously been studied to a rather poor extent. In particular, existing attack detection approaches and algorithms do not rely on the properties of self-organization and decentralization, which an attacker is able to exploit to compromise the network and its services. We propose, first, a model of a self-organizing decentralized wireless sensor network; second, a model of the attacks on such networks; third, algorithms for data collection and attack detection; and, finally, a technique for their application. The WSN model represents a formal specification of this type of network, defining the conditions and limitations of network self-organization and decentralization. The model is characterized by a proposed underlying role-based operation of network nodes and a set of their functional states. The proposed attack model covers the possible types of attacks that are relevant to a given type of WSN and are based on the exploitation of the self-organization and decentralization of the network. The developed algorithm for collecting data for attack detection presents specific types of data and their sources. The developed combined attack detection algorithm is formed of actions that detect relevant attacks on self-organizing decentralized WSNs using machine learning methods. The distinctive element of this algorithm is a set of highly specific features that are obtained by analyzing the data collected in the WSN and used to detect attacks. The proposed technique combines the constructed models and algorithms for the sake of tuning and deploying the attack detection module and the effective detection of attacks in practice. This technique specifies the main steps for the joint use of the models and algorithms and the assignment of data collection and detection parameters. The results of the experiments confirm the correctness of the constructed models, algorithms and technique due to the high values of the attack detection quality indicators. Therefore, the practical application of the proposed apparatus will facilitate improvements in the security of self-organizing decentralized WSNs. Experimental research has confirmed the practical applicability of our proposed solutions. In particular, it has shown that the proposed algorithms and the detection technique can detect both attacks implemented through the exploitation of the network’s properties of decentralization/self-organization and common variations in these attacks (i.e., without exploiting the decentralization property). In general, the experimental results expose a high quality of detection, with an f1-score equal to 0.99.

H∞ control for cyber physical systems under DoS attacks via tracking-removed autoencoder

In this paper, we consider cyber physical systems security control problems under DoS attacks via the tracking-removed autoencoder. The DoS attack model is described by the switch signal, which occurs in the controller-actuator transmission channel. Then, the linear matrix inequality theory is used to solve the average dwell time of the attack mode that the system can tolerate. Moreover, when the attack dwell time exceeds the tolerance range, a tracking-removed autoencoder is chosen to train to get the control signal. And it can be proved that the closed-loop system with the controller based on the tracking-removed autoencoder is asymptotically stable with H ∞ performance index. Finally, the correctness and advancement of the theory are proved by an example.

Switching attack modeling and vulnerability analysis of generator for dynamics study

This paper presents novel insights about cyber-attacks in the power network and their consequence in terms of stability. Compared to existing cyber-attack related studies in power systems, the work discusses switching attack construction and evaluates the impact on generator dynamics. As such, the study is in two folds: (i) switching attack construction, with selection of switching instants based on system states and (ii) vulnerability analysis of power system components, circuit breaker, governor system and excitation system of generators. The role of inherently present communication delay/state variable delay becomes important as it has direct impact on dynamic performance of the system. The impact of the constant and timedependent delay on the stability is addressed by determining the time-delay margin using Lyapunov-Krasovskii functional. Thereafter, the condition for successful switching attack construction is given in terms of delay margin. The accuracy of the derived condition for successful switching attack is demonstrated via real-time co-simulation (RTDS) and MATLAB software. The vulnerability of power system components in IEEE 3-machine, 9-bus network are analysed as individuals or their combinations when subjected to switching attacks. The adequacy related to power system stability as a consequence of cyber-attacks is investigated with an estimation of RoCoF of generators.

Secure Fully Distributed Event-Triggered Consensus of Multi-Agent Systems Against Distributed Sequential Scaling Attacks

This article is concerned with the secure fully distributed event-triggered consensus problem of general linear multi-agent systems subject to distributed sequential scaling (DSS) attacks. First, a generic DSS attack model is proposed, which enables different attack strategies in terms of scaling factors, attack frequency and duration to be incorporated in various communication channels. Different from existing attack models, the DSS attack is a kind of scaling attacks in which attack sequences are characterized by the sequential attacks and with distributed attack strategies for different attack objectives. To resist the adverse effects of such DSS attacks and further reduce the unnecessary communication consumption of each communication channel, a channel-based dynamic event-triggered mechanism is next presented. Moreover, a fully distributed event-triggered consensus control protocol is developed such that the dependence of any global information of the network topology can be eliminated. Formal analysis criteria on the asymptotic convergence of the resultant consensus errors and Zeno-freeness are then derived, where the relationship of the triggering parameters, distributed scaling factors and attack constraints is explicitly expressed. Furthermore, an offline algorithm without any global information is provided to determine both the adaptive consensus protocol gain matrices and the triggering parameters. Therefore, the parameters calculated by this algorithm are applicable to multi-agent systems of different scales, which also confirms the flexibility and scalability of the proposed fully distributed event-triggered consensus control protocol. Finally, two simulation examples involving different scales of intelligent vehicles are given to validate the efficacy of the obtained theoretical results.

Dynamic Event-Triggered H∞ Control for Networked Control Systems With Denial-of-Service Attacks

A dynamic event-triggered H∞ control for networked control systems (NCSs) subject to denial-of-service (DoS) attacks is investigated in this paper. Firstly, the DoS attacks model in communication channel are established based on stochastic variables that obey the Bernoulli distribution, and the attacks rate over network along with switching rules are described. Secondly, the dynamic event-triggered mechanism (DETM) is applied to weaken the influence of DoS attacks and avoid excessive transmission. The threshold parameters under the event-trigger condition can be adjusted by the dynamic characteristics of the systems. Thirdly, by using Lyapunov-Krasovskii functional (LKF) and Linear matrix inequality (LMI) methods, a sufficient condition for NCSs to satisfy the asymptotic stability of given H∞ is obtained. In addition, a cooperative design scheme of DETM parameters and controller gain is proposed. Finally, a RLC series circuit systems is used as an example to show the effectiveness of the method.

Panicolytic-like effects of environment enrichment on male mice threatened by Bothrops jararaca lancehead pit vipers.

Environment enrichment (EE) is a well-known eustress model showing beneficial effects in different psychiatric diseases, but its positive properties in panic disorders are not yet established. The confrontation between prey and predator in complex arenas has been validated as a putative panic attack model. The principal aim of this work was to investigate the role of the EE on panic-like defensive responses elicited by mice threatened by venomous snakes. After 6 weeks of exposure either to an enriched or standard environments, 36 male mice were habituated in a complex polygonal arena for snakes containing an artificial burrow and elevated platforms for escape. The animals were confronted by Bothrops jararaca for 5 min, and the following antipredatory responses were recorded: defensive attention, stretched attend posture, flat back approach, prey versus predator interaction, oriented escape behavior, time spent in a safe place, and number of crossings. Mice threatened by snakes displayed several antipredatory reactions as compared to the exploratory behavior of those animals submitted to a nonthreatening situation (toy snake) in the same environment. Notably, EE causes anxiolytic- and panicolytic-like effects significantly decreasing the defensive attention and time spent in safe places and significantly increasing both prey versus predator interaction and exploratory behavior. In conclusion, our data demonstrate that EE can alter the processing of fear modulation regarding both anxiety- and panic-like responses in a dangerous condition, significantly modifying the decision-making defensive strategy.

Privacy-Preserving Adaptive Resilient Consensus for Multiagent Systems Under Cyberattacks

This article investigates the secure and privacy-preserving consensus problem of multi-agent systems (MASs) with directed interaction topologies under multiple cyber attacks, which contain deception attacks and DoS attacks. First, a unified attack model is introduced to characterize such a multiple attack phenomenon. Besides, considering the existence of eavesdroppers who can intercept the data transmitted on the links, a fully distributed agent value reconstruction method based on the idea of state decomposition is designed to prevent the leakage of the agent's initial information. Then, a novel privacy-preserving adaptive resilient consensus algorithm (PPARCA) with certain graph robustness condition for MASs under the multiple cyber attacks is proposed. The algorithm adaptively takes different countermeasures in the face of different cyber attacks. PPARCA uses the reconstructed agents' states and combines with the modified Secure Acceptance and Broadcast Algorithm (SABA). Theoretical analysis shows that the proposed algorithm can effectively protect the privacy of the initial state of the agents, and reach resilient consensus in the face of cyber attacks. Finally, numerical simulations and Raspberry Pi MASs practical application experiments demonstrate the effectiveness of the proposed results.