DoS Attacks Research Articles

Variable impulsive bipartite coordination control of nonlinear systems with DoS attacks

Variable impulsive bipartite coordination control of nonlinear systems with DoS attacks

MACHINE LEARNING, DEEP LEARNING AND ENSEMBLE LEARNING BASED APPROACHES FOR INTRUSION DETECTION ENHANCEMENT

With the extensive application of Internet of Things and wireless sensor networks (WSNs) in various real-time fields, they have become increasingly vulnerable to several types of attacks that could have a serious impact in their functionalities. Recently, intrusion detection systems have become one of the crucial security components. We propose in this work an approach based on machine and deep learning for DoS and DDoS attack detection. We have evaluated, analyzed, and compared the efficiency of three learning models separately, including deep neural network (DNN), random forest, and decision tree, using the standard metrics of evaluation such as accuracy, precision, F1-score, and recall. In our contribution, an approach ensemble learning was introduced in order to enhance the accuracy rate of classification. Our study was carried out using two well-known benchmark and real-time datasets, CICIoT-2023 and WSN-DS, intended for wireless sensor networks and IoT. These datasets containing various types of attacks. CICIoT2023 dataset contains 33 attacks divided into 7 classes, while WSN-DS dataset contains four types of DoS attacks, including Blackhole, Grayhole, Flooding, and TDMA. The experiment result demonstrate the effectiveness of our approach in attacks detection with high accuracy achieved close to perfect. Our approach result demonstrates that using the ensemble learning technique works better than each ML architecture independently.

Detecting and Analyzing Network Attacks: A Time-Series Analysis Using the Kitsune Dataset

Network security is a critical concern in today’s digital world, requiring efficient methods for the automatic detection and analysis of cyber attacks. This study uses the Kitsune Network Attack Dataset to explore network traffic behavior for IoT devices under various attack scenarios, including ARP MitM, SYN DoS, and Mirai Botnet. Utilizing Python-based data analysis tools, we preprocess and analyze millions of network packets to uncover patterns indicative of malicious activities. The study employs packet-level time-series analysis to visualize traffic patterns and detect anomalies specific to each attack type. Key findings include high packet volumes in attacks such as SSDP Flood and Mirai Botnet, with the Mirai Botnet attack involving multiple IP addresses and lasting over 2 hours. Notable attack-specific behaviors include high traffic on port -1 and targeted traffic on specific ports like 53195. The SYN DoS and Mirai Botnet attacks are characterized by their prolonged durations, suggesting significant disruption. Overall, the study highlights distinctive attack patterns and underscores the importance of understanding these characteristics to enhance detection and response mechanisms.

Prediction-Based State Estimation and Compensation Control for Networked Systems with Communication Constraints and DoS Attacks

Prediction-Based State Estimation and Compensation Control for Networked Systems with Communication Constraints and DoS Attacks

Dynamic Event-Triggered Fuzzy Adaptive Resilient Consensus Control for Nonlinear MASs Under DoS Attacks

Dynamic Event-Triggered Fuzzy Adaptive Resilient Consensus Control for Nonlinear MASs Under DoS Attacks

Event-triggered Leader-following Consensus Control of Multiagent Systems Against DoS Attacks

Event-triggered Leader-following Consensus Control of Multiagent Systems Against DoS Attacks

DoS attack detection and hill climbing based optimal forwarder selection

DoS attack detection and hill climbing based optimal forwarder selection

H∞$$ {\boldsymbol{H}}_{\mathbf{\infty}} $$ Control With Event‐Triggered Mechanism for T‐S Fuzzy System Under Multiple Cyber‐Attacks

ABSTRACTThe problem of event‐triggered control for networked Takagi–Sugeno (T‐S) fuzzy system under aperiodic DoS attacks and deception attacks is investigated. First, an event generator is introduced in the Sensor‐to‐Controller channel to determine the transmission of data. At the same time, the Sensor‐to‐Controller channel is assumed to be subjected to deception attacks that are randomly distributed but not Bernoulli distributed. Next, the impact of aperiodic DoS attacks on the Controller‐to‐Actuator channel is further considered, and the DoS attack behavior is described in terms of attack period and frequency. The article designs an adaptive resilience event‐triggered mechanism (ARETM), which is aimed at circumventing the ineffective data updating in the “active” phase of the DoS attacks, thereby realizing the effective saving of communication resources and mitigating the adverse effects of DoS attacks. Then, the switched fuzzy system is established to cope with the different states of the DoS attackers. Using the piecewise Lyapunov function, a design method for the controller gains and the ARETM matrix is obtained, which allows the system to be stabilizable and obtain performance under the control action. Finally, the effectiveness of the ARETM‐based control strategy is confirmed by simulation experiment.

Optimized memory augmented graph neural network-based DoS attacks detection in wireless sensor network

ABSTRACT Wireless Sensor Networks (WSNs) are mainly used for data monitoring and collection purposes. Usually, they are made up of numerous sensor nodes that are utilized to gather data remotely. Each sensor node is small and inexpensive. Due to the increasing intelligence, frequency, and complexity of these malicious attacks, traditional attack detection is less effective. In this manuscript, Optimized Memory Augmented Graph Neural Network-based DoS Attacks Detection in Wireless Sensor Network (DoS-AD-MAGNN-WSN) is proposed. Here, the input data is amassed from WSN-DS dataset. The input data is pre-processing by secure adaptive event-triggered filter for handling negation and stemming. Then, the output is fed to nested patch-based feature extraction to extract the optimal features. The extracted features are given to MAGNN for the effective classification of blackhole, flooding, grayhole, scheduling, and normal. The weight parameter of MAGNN is optimized by gradient-based optimizers for better accuracy. The proposed method is activated in Python, and it attains 31.20%, 23.30%, and 26.43% higher accuracy analyzed with existing techniques, such as CNN-LSTM-based method for Denial of Service attacks detection in WSNs (CNN-DoS-AD-WSN), Trust-based DoS attack detection in WSNs for reliable data transmission (TB-DoS-AD-WSN-RDT), and FBDR-Fuzzy-based DoS attack detection with recovery mechanism for WSNs (FBDR-DoS-AD-RM-WSN), respectively.

Finite-time bounded security control of networked switched stochastic systems

This paper is dedicated to discussing stochastic finite-time bounded control of switched stochastic systems with feedback information sampled by an event-triggered mechanism and transmitted by network. Besides the incomplete information because of sample, data loss and corruption during information transmission caused by hybrid cyber attacks including DoS attacks and false-data injection attacks, are also taken into consideration. Based on the above incomplete information, state feedback controllers are established to fulfil stochastic finite-time boundedness of switched stochastic systems and make it have a certain disturbance attenuation capability, i.e. have a finite-time L 2 -gain, successfully. Sufficient conditions are cast into a convex optimisation problem which can be easily solved by fixing some parameters. Simulation results are employed to verify the validity of results.

Probability‐Based Finite‐Time Security Control for Switched Stochastic Systems via a Novel Event‐Triggered Mechanism and Its Application

ABSTRACTThis article discusses the finite‐time boundedness (FTB) issue and ‐gain analysis for switched stochastic systems, particularly under the dual influence of DoS attacks and false data injection attacks, by employing a novel event‐triggered mechanism. Different from the moment calculation method, a probability‐based FTB is studied which offers more pertinence. To obtain a less conservative condition of FTB, a switched Lyapunov function is constructed. Additionally, a memory‐based dynamic event‐triggered mechanism is designed to reduce the amounts of triggering and mitigate the state response fluctuations. Based on the incomplete information above, state feedback controllers are devised to satisfy stochastic FTB, ensuring the successful attainment of finite‐time ‐gain. Sufficient conditions are cast into a convex optimization problem by LMIs which can be solved easily. Finally, a compared numerical example and an RLC series circuit are adopted to demonstrate the availability of the theoretical results.

Anti-asynchrony output regulation for switched systems under switching-Q-learning event-triggering against DoS attacks

Anti-asynchrony output regulation for switched systems under switching-Q-learning event-triggering against DoS attacks

Security Evaluation Framework for Virtualised Environments

With the expansion of cloud computing, virtual environments remain susceptible to sophisticated security threats, such as hypervisor vulnerabilities, VM escapes, inter-VM attacks, DoS attacks, and malware injections. Hence, ensuring that virtualised environments are secure, has become increasingly crucial. These threats are particularly affecting as many enterprises which have shifted to online services and remote work. This paper examines the existing frameworks which have been designed to address the existing challenges, their gaps and strengths; and proposes an enhanced security framework that can help to mitigate these threats. The framework was developed following an iterative process encompassing several stages. It has the hypervisor layer, virtual machine layer, network layer, management layer, and monitoring and response layer components. The framework aims to enhance detection capabilities, reduce response times, minimise system performance impact, and lower false positive rates while optimising resource utilisation by providing a practical and effective approach to securing virtualised infrastructures, thereby ensuring the resilience and reliability of cloud-based services.

Encoding–decoding-based distributed state estimation over sensor networks with limited sensing range under DoS attacks

This paper is concerned with encoding–decoding-based distributed state estimation over sensor networks under DoS attacks. Different from most of the existing research results on distributed state estimation for sensor networks, where all sensors are assumed to have sufficiently wide sensing ranges, this paper considers sensors with limited sensing ranges. Therefore, the problem studied has more practical significance. To save the limited bandwidth resources of sensor networks, a two-channel encoding–decoding scheme (EDS) based on probability is proposed for each node to compress the transmitted data to an acceptable range, where independent DoS attacks are launched randomly on the communication channels between nodes. Then, a distributed state estimator with limited sensing ranges under DoS attacks in the presence of both the sensor-estimator channel EDS and the node–node channel EDS is constructed under the criterion of minimum mean-square error. Furthermore, considering the real-time changes of the communication topology resulting from independent DoS attacks and the uncertainty introduced by the node–node channel EDS, the upper bound of the expected estimation error covariance is derived and the boundedness of the upper bound is analyzed under given assumption conditions. Finally, a numerical example is exhibited to illustrate the effectiveness of the designed algorithm.

Bipartite containment control of multi‐agent systems under DoS attacks: an event‐triggered scheme

Bipartite containment control of multi‐agent systems under DoS attacks: an event‐triggered scheme

Adaptive Fuzzy Resilient Decentralized Control for Nonlinear Large-Scale CPSs Under DoS Attacks

Adaptive Fuzzy Resilient Decentralized Control for Nonlinear Large-Scale CPSs Under DoS Attacks

Design of Intelligent Control Under Machine Learning Supervision and Signal Compression Mechanism Design for NCSs Under DoS Attacks

Design of Intelligent Control Under Machine Learning Supervision and Signal Compression Mechanism Design for NCSs Under DoS Attacks

Distributed Event-Triggered Economic Environmental Resource Management for Islanded Microgrids Under DoS Attacks

Distributed Event-Triggered Economic Environmental Resource Management for Islanded Microgrids Under DoS Attacks

Distributed Fault-Tolerant Consensus Control of Vehicle Platoon Systems With DoS Attacks

Distributed Fault-Tolerant Consensus Control of Vehicle Platoon Systems With DoS Attacks

Optimal Bipartite Tracking Control for Heterogeneous Systems Under DoS Attacks

Optimal Bipartite Tracking Control for Heterogeneous Systems Under DoS Attacks