Multiple Attacks Research Articles

Urban wind field prediction based on sparse sensors and physics‐informed graph‐assisted auto‐encoder

AbstractThe urban flow wind field is a critical element for downstream research, such as mitigation of urban wind disasters, assessment of urban wind environment, and urban drone route planning. However, it is impractical to arrange a large number of sensors to monitor an urban wind flow field. Hence, acquiring the entire urban wind flow field via sparse sensors would be highly valuable. To date, no scheme including deep learning (DL) model has been specifically designed for this purpose. This study presents an innovative approach to reconstruct complex high‐resolution urban wind fields based on sparse sensors, using a physics‐informed graph neural network (GNN)‐assisted auto‐encoder. The proposed method leverages the relationship between sensors and their surrounding environment enabled by deep mining capabilities of GNNs. As a result, the utilization and emphasis on sparse sensors data are significantly enhanced. The continuity equation of fluid flow is incorporated into the loss function of the convolution neural network to improve the stability and performance of the model. The findings suggest that, in contrast to prevalent generative DL models, the proposed model yields an approximate 50% reduction in root mean square error for reconstructing high‐resolution urban wind fields for multiple wind attack angles.

Relaxed Set-Membership Estimation and Cyber Attack Detection for LPV Systems Under Multiple Attacks via A Switching-Type Scheme Design

Relaxed Set-Membership Estimation and Cyber Attack Detection for LPV Systems Under Multiple Attacks via A Switching-Type Scheme Design

Automatic Search of Differential Characteristics and Improved Differential Cryptanalysis for PRINCE, QARMA, and MANTIS

Reflection structure has a significant advantage that realizing decryption and encryption results in minimum additional costs, and many block ciphers tend to adopt such structure to achieve the requirement of low overhead. PRINCE, MANTIS, QARMA, and PRINCEv2 are lightweight block ciphers with reflection feature proposed in recent years. In this paper, we consider the automatic differential cryptanalysis of reflection block ciphers based on Boolean satisfiability (SAT) method. Since reflection block ciphers have different round functions, we extend forward and backward from the middle structure and achieve to accelerate the search of the optimal differential characteristics for such block ciphers with the Matsui’s bounding conditions. As a result, we present the optimal differential characteristics for PRINCE up to 12 rounds (full round), and they are also the optimal characteristics for PRINCEv2. We also find the optimal differential characteristics for MANTIS, QARMA‐64, and QARMA‐128 up to 10, 12, and 8 rounds, respectively. To mount an efficient differential attack on such block ciphers, we present a uniform SAT model by combining the differential characteristic searching process and the key recovery process. With this model, we find two sets of 7‐round differential characteristics for PRINCE with less guessed key bits and use them to present a multiple differential attack against 11‐round PRINCE, which improves the known single‐key attack on PRINCE by one round to our knowledge.

Fault-Tolerant Consensus of Multi-Agent Systems Subject to Multiple Faults and Random Attacks

Fault-Tolerant Consensus of Multi-Agent Systems Subject to Multiple Faults and Random Attacks

Deep Learning-Based Detection for Multiple Cache Side-Channel Attacks

Deep Learning-Based Detection for Multiple Cache Side-Channel Attacks

A Generation Method of New Power System APT Attack Graph Based on DQN

Background: The new power system is more vulnerable to Advanced Persistent Threat (APT) than the traditional power system. Objective: This study aims to grasp the intent of the APT attack better; a new generation method of power system APT attack graph based on DQN is proposed. Methods: First, the network topology of the new power system was extracted by Nessus scanning as the model input. Secondly, the agent in DQN was trained for multiple rounds. Starting from the set initial state, the agent selected the action with the highest Q value to act on the system in each round, and then the system entered the next state. Then the Q network function value was updated according to the obtained system feedback value until the target state appeared. Results: After multiple rounds of training agents, multiple APT attack paths were finally obtained, thus an APT attack graph can be generated. Conclusion: The experimental results showed that the efficiency of generating an APT attack graph based on the DQN method is obviously superior to the existing methods for the large-scale industrial control system, such as the new power system.

Distributed Resilient Initialization-Free Jacobi Descent Algorithm for Constrained Optimization Against DoS Attacks

This paper investigates one type of distributed constrained optimization problem, e.g., the economic dispatch problem, in the presence of DoS attacks. Therein, multiple DoS attackers are collaborative to impede the communication transmission and change the communication topology at will. Consequently, the convergence and/or optimality of distributed algorithm may be compromised. To reduce the effect of this kind of DoS attacks, a distributed resilient initialization-free Jacobi descent algorithm is proposed. It is designed with three switched control protocols which enable the proposed algorithm reasonably employing the estimations to replace the missing information when attacks occur. Meanwhile, the proposed method is embedded with second order information, resulting in faster convergence speed. Moreover, theoretical analysis results are provided to show that the proposed algorithm can exponentially converge to the global optimal solution of the studied problem. Finally, simulation results tested in IEEE 30-bus system validate its effectiveness and flexibility. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The economic dispatch is a key issue in smart grid, which can be formulated as a kind of distributed constrained optimization problem. Since the distributed algorithms work under distributed sensor networks, they are easier to undergo DoS attacks. To address this issue, this paper presents a distributed resilient initialization-free Jacobi descent algorithm, which features strong robustness to resist DoS attacks and faster convergence. Meanwhile, the proposed method is shaped for common constrained optimization problem with better expansibility. We conduct the global convergence and optimality proofs, which benefits the practitioners to estimate the convergence performance, e.g., the convergence rate. Simulations further show the correctness and effectiveness of the proposed method. In future, we will pay more attention on the non-convex constrained optimization problem.

A Hybrid Logistic Scroll Chaotic Encryption Algorithm for Ensuring the Cloud Security to Counerfeiting the Attacks

Cloud computing is meant for storing the huge data using third party that ensures that confidential data cannot be accessed by the other users. But with rapid growth of technologies, data in the cloud normally increases which questions its security in storing in the cloud. Hence the protecting the cloud data seeks the strong security levels to counterfeit the different cloud attack. In order to achieve the highest level of security for cloud data, this study suggests a powerful encryption technique that combines chaotic scrolls and logistic maps. The proposed model exhibits the following advantages over the other algorithms: 1) High dynamic key generation 2) ability to counterfeit the multiple attacks 3) High randomness encrypted data which can provide more confusion of hacking from the intruder’s insight. To prove the strength of the proposed model, NIST National Institute of Science and Technology (NIST) is used for significant experiments. in which different statistical tests were carried out to prove the strength of the proposed model. The level of security of the suggested model is also evaluated and investigated using formal analysis using Burrows-Abadi-Needham Logic (BAN). The given model is thoroughly verified using both the Profverif tool and AVISPA. In terms of communication costs and unpredictability, the suggested model's randomness has also been contrasted with that of another existing algorithm. Results demonstrates that the proposed model shows its ability to provide more potent protection to the cloud data than the other existing encryption algorithms.

Toward a modern secure network based on next-generation firewalls: recommendations and best practices

The security architecture of modern networks undergoes periodic changes, given (i) the diversity of security solutions, (ii) the emergence of new attack vectors, and (iii) corporate needs in terms of access control (ZTNA: Zero Trust Network Access). The complexity of setting up a secure infrastructure lies in the design of the network architecture, on the one hand, and the choice of configurations appropriate to the company’s security policy, on the other. Indeed, misconfiguration is considered a vulnerability, which makes the task of setting up a secure network somewhat critical, given the technical skills it requires. In this paper, we will address this problem by proposing a modern security architecture applied to all sizes of enterprises: medium-sized, large, and sometimes even small companies. Therefore, we will describe the equipment required and their roles in the security layer, the security technologies adopted, and best practices for optimal deployment. To evaluate our proposal, we generated malicious traffic using the firewall inspection tester based on the “fit.py” script to study the architecture’s resilience and behavior in the face of multiple attacks.

Profitability Analysis of Time-Restricted Double-Spending Attack on PoW-based Large Scale Blockchains with the Aid of Multiple Attacks

Profitability Analysis of Time-Restricted Double-Spending Attack on PoW-based Large Scale Blockchains with the Aid of Multiple Attacks

Behavior of the DenStream Clustering Algorithm for Attack Detection in the Internet of Things

Multiple attack detection schemes based on supervised batch learning are presented in the literature as an alternative to improve Internet of Things (IoT) security. These schemes require benign and malicious traffic samples for training and are unable to easily adapt to changes in the analyzed data. In this work, we study how we can use DenStream, an unsupervised stream mining algorithm, to detect attacks in IoT networks. This type of algorithm does not require labeled examples and can learn incrementally, adapting to changes. We aim to investigate whether attacks can be detected by monitoring the behavior of DenStream's clusters. The results showed that DenStream could provide indicators of attack occurrence in TCP, UDP, and ICMP traffic.

A privacy preserving four-factor authentication protocol for internet of medical things

Medical applications of the Internet of Things (IoT) have gained significant attention, especially in the context of monitoring health-related information for elderly individuals living alone and patients receiving home-based care, especially during the COVID-19 pandemic. These applications offer immense convenience and contribute to the reduction of infection risk. Consequently, safeguarding the privacy of patient data has become increasingly vital. However, the resource limitations inherent to IoT present challenges in implementing complex algorithms. Numerous researchers have proposed authentication schemes based on three factors: passwords, biometric features, and smart cards. While three-factor authentication protocols are generally more secure than two-factor ones, recent studies have unveiled vulnerabilities in existing protocols designed for IoT environments, particularly concerning sensor node capture attack and smart card stolen attack. Only a few protocols provide reliable solutions to address these issues. To tackle this challenge, we propose a lightweight authentication protocol that introduces the Physical Unclonable Function (PUF) as the fourth factor, enhancing the security and privacy of the system. By integrating PUF technology into servers and embedding it into the integrated circuit chips of sensors, our protocol is specifically designed to thwart attacks like sensor node capture and smart card stolen. We validate the security of our proposed protocol using formal BAN logic and ProVerif simulator tool, demonstrating its capability to provide secure mutual authentication. Moreover, through informal analysis, we illustrate that our scheme can withstand multiple attacks. Furthermore, our proposed protocol outperforms existing protocols in terms of computational cost, storage cost, communication cost, and security requirements.

On Information Fusion in Optimal Linear FDI Attacks Against Remote State Estimation

This paper studies the problem of false data injection (FDI) attacks against remote state estimation. The scenario that malicious attackers can intercept original data packets and also eavesdrop on some side information of system states with extra sensors is considered. To clarify a counter-intuitive issue in existing work, a different innovation-based linear attack policy fusing all available information is proposed. First, the evolution of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> estimation error covariance with FDI attacks is derived. Then, explicit solutions of optimal stealthy attack coefficients are obtained without solving optimization problems numerically. The condition under which there exist multiple optimal attacks is analyzed. Additionally, an easy-to-check criterion for comparing two information fusion methods in scalar systems is given. Simulation results show that, compared with existing work, the proposed attack strategy can completely deceive the anomaly detector and cause more severe performance degradation in remote state estimation.

Distributed Multiple Attacks Detection via Consensus AA-GMPHD Filter

Distributed Multiple Attacks Detection via Consensus AA-GMPHD Filter

Deploying an energy efficient, secure & high-speed sidechain-based TinyML model for soil quality monitoring and management in agriculture

This paper presents an innovative deployment strategy for a power-aware and delay-aware TinyML model optimized for agricultural soil quality monitoring and management. The proposed method employs an enhanced fusion of Dynamic Voltage and Frequency Scaling (DVFS), Sleep/Wake Strategies based on Genetic Algorithms, Energy Harvesting, and Task Partitioning. Through this method, the model reduces energy consumption by 8.5% and delay by 10.4%, while maintaining accuracy comparable to state-of-the-art techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Q Learning techniques. The proposed model also uses a Proof-of-Energy Efficiency (PoEE) based consensus for creation of new blocks. These blocks are added to different sidechains for strengthening the security levels. While blockchains require higher energy during mining, the proposed PoEE Model uses a light weight consensus, which is backed by Q Learning based sidechain formation process. This lightweighted combination allows the model to reduce delay, maintain lower energy consumption, with better communication throughput even under multiple attack scenarios. This model is necessary due to the growing demand for sustainable and accurate agricultural practices. The quality of the soil is crucial to soil yield and productivity. Monitoring and managing soil conditions enables farmers to optimize irrigation, fertilization, and overall resource allocation, leading to an increase in soil yield and a decrease in environmental impacts. However, the deployment of energy-efficient and low-latency TinyML models is essential to enable real-time decision-making in agricultural settings with limited resources. This power-aware and delay-aware TinyML model has applications in various agricultural real-time scenarios. It can be used to continuously monitor soil quality parameters including moisture content, pH level, and nutrient levels for various farm types. Incorporating sensors and low-power microcontrollers, the model enables on-site analysis and timely farmer feedbacks. The decreased energy consumption extends the battery life of deployed devices, whereas the decreased delay ensures that soil management interventions and treatments are responsive and timely for different scenarios.

Output regulation for switched systems via dual distributed event-triggered mechanisms under multiple deception attacks

The secure output regulation (OR) is studied for networked switched systems (NSSs) that exhibit severely unstable dynamics (SUDs) and are vulnerable to multiple deception attacks. The occurrence of some destabilizing switching and unsolvable OR of each individual subsystem is permitted. Dual distributed event-triggered mechanisms (DDETMs) are proposed, incorporating both distributed event detection conditions to tackle the issue of unreliable single-channel data during deception attacks on sensor networks, and mode-matching detection conditions to reduce asynchronous intervals. A dwell time switching rule is developed without consistency constraint, i.e., the sub-controller's stabilizing/destabilizing switching may be inconsistent with that of the subsystem. Judging conditions are introduced for the occurrence of multiple deception attacks on dual-terminal sensor/transmission networks, which adjust the increment coefficients of Lyapunov functions before or after switching instants. Sufficient conditions are provided to ensure secure OR for NSSs under multiple deception attacks. With the aid of a switched RLC circuit, the effectiveness of the presented methodologies is validated.

Improving Kalman filter for cyber physical systems subject to replay attacks: An attack-detection-based compensation strategy

This paper investigates the problem of improving Kalman filtering for a class of multi-layer cyber physical systems (CPS) in the face of multiple consecutive replay attacks. CPS is vulnerable in an open network environment, and the replay attack model is described based on the number and duration of successful message transmissions. For all possible occurrences of replay attacks, a detection and compensation method is obtained with the help of the residual monitoring mechanism and an attack detection function. At the same time, in order to identify the attack has occurred in which, the register is integrated into the system. Furthermore, a novel Kalman filter with a compensation mechanism is designed to estimate the ideal states of CPS subject to replay attacks, and the Kalman filter gain is optimized based on the detection results. Finally, a simulation example is given to verify the effectiveness of the proposed improving Kalman filter.

An Empirical Study of Destructive Nodes Characterization in Wireless Networks

Mobile Ad hoc Networks (MANETs) are deployed in various new public and domestic environments, going up to new needs in terms of concert and effectiveness. According to the wireless dynamic nature, some needed services like security for network maintenance, trust-based routing and resource management among network nodes are not carried out as good as expected. Also, the ad-hoc networks are vulnerable to secure communications and multiple attacks can participate in various layers of a network stack. Destructive nodes have chances to change or discard routing specifications, sometimes it can send false routes to capture source data packets to pass through themselves. Some protocols have been designed to address the complication from secure data communication. Even though, a secure protocol cannot handle all kinds of attack detection and elimination in all situations. New secure data communication wireless protocols need to focus these challenges, because security care is not natively built in MANET. Therefore, in this research paper, analysis of destructive nodes characterization and impacts on wireless networks investigated the multiple attacks behaviour, activities of the attacks all through neighbour selection, path establishment from source to destination, creating awareness of attack presence detection knowledge to the normal devices during path discovery and data transmission mechanisms. Legitimate nodes need to be building with secure transmission knowledge to make sure trusted communication, ensure validation, honesty, and privacy to classify the attacks in MANETs.

Hybrid intrusion detection model for Internet of Things (IoT) network environment

Technology development brought numerous lifestyle changes. People move around with smart gadgets and devices in the home, work environment, and familiar places. The Internet acts as a backbone for all applications and connecting multiple devices to set up a smart environment is technically termed as IoT (Internet of Things). The feature merits of IoT are explored in numerous fields from simple psychical data measurement to complex trajectory data measurement. Where the place is inaccessible to humans, IoT devices are used to analyze the region. Though IoT provides numerous benefits, due to its size and energy limitations, it faces security and privacy issues. Intrusions in IoT networks have become common due to these limitations and various intrusion detection methods are introduced in the past decade. Existing learning-based methods lag in performance while detecting multiple attacks. Conventional detection models could not be able to detect the intrusion type in detail. The diverse IoT network data has several types of high dimensional features which could not be effectively processed by the conventional methods while detecting intrusions. Recently improvements in learning strategies proved the performance of deep learning models in intrusion detection systems. However, detecting multiple attacks using a single deep learning model is quite complex. Thus, in this research a multi deep learning model is presented to detect multiple attacks. The initial intrusion features are extracted through the AlexNet, and then essential features are selected through bidirectional LSTM. Finally, the selected features are classified using the decision tree C5.0 algorithm to attain better detection accuracy. Proposed model experimentations include benchmark NSL-KDD dataset to verify performances and compared the results with existing IDSs based on DeepNet, Multi-CNN, Auto Encoder, Gaussian mixture, Generative adversarial Network, and Convolutional Neural Network models. The proposed model attained maximum detection accuracy of 98.8% over conventional methods. Overall, an average of 15% improved detection performance is attained by the proposed model in detecting several types of intrusions in the IoT network.

A Labeled RFS-Based Framework for Multiple Integrity Attackers Detection and Identification in Cyber–Physical Systems

The problem of multiple integrity attacks (attackers) detection and identification (MIADI) in cyber-physical systems (CPSs) is still a challenging problem to date. The goal of this paper is to develop a knowledge-based method capable of simultaneously detecting and identifying multiple integrity attacks aiming at different sensors in a CPS. In this paper, with the help of labeled random finite set (RFS) theory, a new solution to solve the MIADI problem is proposed. The main contributions of this paper, lie in the the following two aspects, the first is the novel formulation of the MIADI problem, in which labeled RFSs are used to model the behaviors of multiple integrity attackers for the first time, and the second is the proposed labeled RFS-based solution, which provides an elegant framework to cope with the MIADI problem. Numerical experiments are conducted and experimental results demonstrate the effectiveness of the proposed solution. This proposed solution further extends the feasibility of the labeled RFS theory in the context of CPSs cybersecurity.