Denial Of Service Attacks Research Articles

Resilient data‐driven asymmetric bipartite consensus for nonlinear multi‐agent systems against DoS attacks

AbstractIn this article, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi‐agent systems with both cooperative and antagonistic interactions under denial‐of‐service (DoS) attacks. We first prove that the URABC problem is solved by stabilizing the neighborhood asymmetric bipartite consensus error. Then, we develop a distributed compact form dynamic linearization method to linearize the neighborhood asymmetric bipartite consensus error. By using an attack compensation mechanism to eliminate the adverse effects of DoS attacks and an extended discrete state observer to enhance the robustness against unknown dynamics, we finally propose a distributed resilient data‐driven adaptive control (DDAC) algorithm to solve the URABC problem. A numerical example validates the proposed results.

Adversarially robust and real-time DDoS detection and classification framework using AutoML

ABSTRACT Denial of Service (DoS) attacks target the availability part of the CIA triad (Confidentiality, Availability, and Integrity). A special category of these attacks is the Distributed DoS (DDoS) attack, where the attacker uses a network of compromised systems called a botnet to flood a target server with requests and refuses to serve legitimate users. DDoS attacks can cost an organization millions of dollars in terms of lost revenue, remediation costs, and damage to brand reputation. Hence, all organizations need speedy real-time detection of DDoS attacks. This work presents a DDoS detection and classification framework using the flow-based approach for feature engineering and the AutoML technique. Our detection system is trained on the latest DDoS datasets – CIC-DDoS 2019 and CIC-IDS 2017, which contain various categories of DDoS attacks. We use various tools to perform adversarial attacks on our trained model. We retrain our models using adversarially crafted network packet captures and then test our models for robustness against practical adversarial attacks that an attacker might use to evade detection. Finally, we deploy our model in real-time using a GUI-based tool. Our model achieves a validation accuracy of 99.9% and a low false positive rate of 0.05%.

Sampled-Data-Based Secure Synchronization Control for Chaotic Lur'e Systems Subject to Denial-of-Service Attacks.

This article investigates the sampled-data-based secure synchronization control problem for chaotic Lur'e systems subject to power-constrained denial-of-service (DoS) attacks, which can block data packets' transmission in communication channels. To eliminate the adverse effects, a resilient sampled data control scheme consisting of a secure controller and communication protocol is designed by considering the attack signals and periodic sampling mechanism simultaneously. Then, a novel index, i.e., the maximum anti-attack ratio, is proposed to measure the secure level. On this basis, a multi-interval-dependent functional is established for the resulting closed-loop system model. The main feature of the developed functional lies in that it can fully use the information of resilient sampling intervals and DoS attacks. In combination with the convex combination method, discrete-time Lyapunov theory, and some inequality estimate techniques, two sufficient conditions are, respectively, derived to achieve sampled-data-based secure synchronization of drive-response systems against DoS attacks. Compared with the existing Lyapunov functionals, the advantages of the proposed multi-interval-dependent functional are analyzed in detail. Finally, a synchronization example and an application to secure communication are provided to display the effectiveness and validity of the obtained results.

Resilient event-triggered observer-based control of non-linear systems under denial-of-service attacks with actuator saturation

This paper studies the control problem for a continuous-time networked system with non-linearity in the state equation as well as in the input, as saturation. The system is considered under denial-of-service (DoS), attacks which cause the blockage of input and/or output components in the overall closed-loop model. An event-triggering scheme that is resilient in nature, along with an observer-based control, has been considered under DoS attacks. The resultant scheme ensures efficient network resources and excludes Zeno behavior naturally due to the presence of a minimum positive interevent delay. Then, an event-based switched non-linear model is presented to address both the event-triggering scheme and the presence of DoS blocking attacks. A piece-wise Lyapunov–Krasovskii functional method on the described non-linear model, resulting in the switched system, is considered for achieving an exponentially stable response by driving the required feasibility conditions. In the presence of a non-linear system with saturation in the actuator, the presented design establishes quantitative relationships among the exponential decay rate, active/sleeping intervals of attacks, parameters of the event-triggering condition, and sampling period of the system. After that, linear matrix inequalities are presented for designing an event-triggered controller with an observer, while the design also includes the region of convergence for dealing with the input non-linearity. Finally, comparative results for an offshore structure model with non-linearity in states as well as in actuator, are demonstrated to verify the results of the control scheme that is developed. It has been verified that our design is less conservative than the previous designs, and can handle the non-linearities in the dynamics of plant and actuator saturation more efficiently, while DoS attacks are also present. By applying our proposed method, the overshoot and undershoot are less than ±2.5 percent, while system states converge to the origin within 55 s.

Dynamic Event-Triggered Control for Delayed Nonlinear Markov Jump Systems under Randomly Occurring DoS Attack and Packet Loss

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attacks and packet loss. Firstly, the stochastic characteristics of DoS attacks and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of the systems. Then, by constructing a general common Lyapunov functional and combining it with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through two examples.

Stability Analysis for H∞-Controlled Active Quarter-Vehicle Suspension Systems With a Resilient Event-Triggered Scheme Under Periodic DoS Attacks.

The stability analysis is studied for H∞ controlled networked active quarter-vehicle suspension systems with a resilient event-triggered scheme (RETS) under periodic denial-of-service (DoS) jamming attacks in this article. For the networked suspension system, the system-state signals are measured by sensors and transmitted to the cloud controller through a wireless network and then the control signal is transferred to the actuator to control it. An event-triggered scheme (ETS) is designed to reduce the workload of data transmission, which is effective to select some most useful information to transmit and discard some redundant data. DoS attacks can block the data transmission when it is active, so a resilient event-triggered H∞ control method is built based on the Lyapunov stability theory. The exponential stability of the controlled suspension system, as well as the H∞ performance, is analyzed in this article. Some simulation results show that the proposed control method is effective to improve driving comfort and driving safety and reduce the workload of data transmission under periodic DoS attacks.

Finite-horizon optimal secure tracking control under denial-of-service attacks

The finite-horizon optimal secure tracking control (FHOSTC) problem for cyber–physical systems under actuator denial-of-service (DoS) attacks is addressed in this paper. A model-free method based on the Q-function is designed to achieve FHOSTC without the system model information. First, an augmented time-varying Riccati equation (TVRE) is derived by integrating the system with the reference system into a unified augmented system. Then, a lower bound on malicious DoS attacks probability that guarantees the solutions of the TVRE is provided. Third, a Q-function that changes over time (time-varying Q-function, TVQF) is devised. A TVQF-based method is then proposed to solve the TVRE without the need for the knowledge of the augmented system dynamics. The developed method works backward-in-time and uses the least-squares method. To validate the performance and features of the developed method, simulation studies are conducted in the end.

Securing the Cloud: Understanding Threats and Countermeasures

Abstract: Cloud computing has completely transformed how businesses and individuals store, manage, and retrieve data. Nevertheless, despite its many benefits, there are inherent security challenges. This research investigates the different risks encountered by cloud computing, which consist of data breaches, insider threats, and denial of service attacks. It also covers successful prevention methods like encryption, multi-factor authentication, routine security checks, and staff education. By comprehending these dangers and putting in place strong security measures, organizations can improve the security of their cloud environments and guarantee the confidentiality, integrity, and availability of their data. Cloud computing is now being extensively researched and utilized in both academic and industrial sectors. Both cloud service providers (CSPs) and consumers benefit from cloud computing. The security issues related to cloud computing have been extensively researched in various studies.

Enhancing Intrusion Detection through Unsupervised Deep Learning Models

The Internet of Things (IoT) has experienced significant growth since its inception, representing a groundbreaking technological advancement. In essence, IoT involves the seamless integration of devices and data to automate and centralize various processes. This transformative technology is revolutionizing business operations and reshaping society as a whole. As IoT continues to evolve, the importance of detecting vulnerabilities and weaknesses becomes paramount in order to thwart unauthorized access to critical resources and business functions, which could potentially render the entire system unavailable. One prevalent threat in this context is Denial of Service (DoS) and Distributed DoS attacks. In this project, propose an innovative architecture known as Protocol Based Deep Intrusion Detection (PB-DID). To create our dataset, we gathered packets from IoT traffic and compared features from two well-known datasets: UNSWNB15 and Bot-IoT. We focused on flow and Transmission Control Protocol (TCP) characteristics. Our primary objective was to accurately classify network traffic into three categories: non-anomalous, DoS, and DDoS, while addressing issues like data imbalance and overfitting. Utilizing deep learning (DL) techniques, we achieved an impressive classification accuracy of 96.3%. This level of accuracy demonstrates the potential of our PB-DID architecture in effectively identifying and mitigating intrusion attempts in the context of IoT, thereby enhancing the security and reliability of IoT systems. Keywords—: Intrusion detection in IoT, Deep learning for intrusion detection, DoS detection, DDoS detection.

Advanced machine learning approach for DoS attack resilience in internet of vehicles security

Recent years have witnessed security as a great concern in vehicular networks (VANET). Particularly, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can jeopardize the network by broadcasting a storm of packets. Correspondingly, the network resources are jammed with malicious traffic. In this connection, the existing research presented various techniques to cope with DoS and DDoS attacks. Different from those traditional approaches, this study proposes an Intelligent Intrusion Detection System (IDS) by leveraging Machine Learning (ML). The proposed IDS utilizes a publicly available dataset on the application layer for mitigating DDoS attacks. The designed ML-based IDS relies on combining both the Random Projection (RP) and Randomized Matrix Factorization (RMF) methods to achieve the best results for enhancing the detection capabilities of the IDS. This amalgamation enhances the system's detection capabilities by extracting and analyzing meaningful features from network traffic data. Experimental validation of our approach involves a comprehensive evaluation of various ML models, including Extra Tree Classifier (ETC), Logistic Regression (LR), and Random Forest (RF). Remarkably, the combined accuracy of these models yields an average system accuracy of 0.98, surpassing existing methods. Unlike conventional approaches, our proposed IDS excels in efficiency and exhibits notable performance in detecting DoS and DDoS attacks in VANET. This proficiency ensures the integrity and safety of vehicle communications. Thus, our research substantially contributes to the vehicular network security field. The presented findings establish a foundation for future advancements in securing connected vehicles.

Prescribed time formation synchronous tracking of unmanned aerial vehicles with denial of service attacks

This paper addresses the issue of robust prescribed time synchronous tracking control of unmanned aerial vehicles (UAVs) formation with denial of service attacks. A novel prescribed time synchronous tracking control scheme is proposed within a denial of service attack conversion mechanism. Firstly, on account of the UAV formation in the presence of aerodynamic damping and denial of service attacks, a formation cooperative control model is established under a second-order system. Secondly, to perform formation synchronous tracking within a settling time, a time-varying auxiliary function on account of synchronous state errors is designed for the convergence of prescribed time tracking. The control parameters are designed using a key eigenvalue scaling technique under the effect of denial of service attacks on network topology. The proposed scheme is also extended to UAV formation with a detailed-balanced graph. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy.

Data-based adaptive predictive bipartite consensus for nonlinear multiagent systems against DoS attacks

This article investigates a Denial-of-Service (DoS) attack problem for nonlinear unknown discrete-time multiagent systems (MASs) to implement bipartite consensus tracking tasks with fixed and switching topologies. Firstly, an equivalent linearization data model of each agent is constructed using a pseudo partial derivative approach, where only one parameter needs to be estimated using input/output data of the controlled MASs. Meanwhile, the DoS attack behavior is described by a Bernoulli distribution process, and both cooperative and competitive relationships among agents are investigated. Moreover, an increment prediction compensator is designed to reduce the effect of DoS attacks. A data-based adaptive predictive bipartite consensus control algorithm is formulated. The corresponding theoretical analysis indicates that tracking errors of MASs with fixed and switching topologies converge to a small range around zero. Finally, several simulations and hardware tests further verify the proposed scheme’s effectiveness.

Multi-Pattern GPU Accelerated Collision-Less Rabin-Karp for NIDS

In the domain of network communication, network intrusion detection systems (NIDS) play a crucial role in maintaining security by identifying potential threats. NIDS relies on packet inspection, often using rule-based databases to scan for malicious patterns. However, the expanding scale of internet connections hampers the rate of packet inspection. To address this, some systems employ GPU accelerated pattern matching algorithms. Yet, this approach is susceptible to denial of service (DOS) attacks, inducing hashing collisions and slowing inspection. This research introduces a GPU-optimized variation of the Rabin-Karp algorithm, achieving scalability on GPUs while resisting DOS attacks. Our open-source solution (https://github.com/AnasAbbas1/NIDS) combines six polynomial hashing functions, eliminating the need for false-positive validation. This leads to a substantial improvement in inspection speed and accuracy. The proposed system ensures minimal packet misclassification rates, solidifying its role as a robust tool for real-time network security.

Subsystem-importance-aware DoS attacks and countermeasures

This paper is concerned with the security issue of remote state estimation in multi-systems. Note that different subsystems or local state information possess varying levels of importance. A novel denial-of-service (DoS) attack type, referred to as a subsystem-importance-aware DoS attack, is proposed. The importance of a subsystem refers to the quantitative impact of its information on the correctness or accuracy of decision-making for the entire system. Here, the importance of a subsystem is positively correlated with the power allocated to transmit local information. Additionally, it is assumed that the attacker has access to the transmission power of all subsystems. The optimal attack strategies are then investigated under different subsystem-importance weight functions and the error functions that are related to the packet arrival rate, and the closed-form solutions under some special cases are provided. Furthermore, potential defense strategies are investigated, leveraging the gap in the interpretation of the subsystem importance between the attacker and the defender, formulated as a Stackelberg game. Finally, numerical examples are provided to illustrate the theoretical results and provide insights into possible countermeasures against our proposed attack.

Observer‐based event‐triggered H∞$$ {H}_{\infty } $$ control of networked switched systems under hybrid attacks

AbstractThis article is concerned with the issue of observer‐based event‐triggered control for networked switched systems (NSSs) under hybrid attacks. Since hybrid attacks can cause more damage to the system than a single cyber attack, this article considers a situation where stochastic deception attacks and aperiodic denial‐of‐service (DoS) attacks coexist in NSSs. To address this issue, the dynamics of the initial switched system combined with the event‐triggered mechanism (ETM), observers and hybrid attacks are formulated as a new augmented switched system. Thereinto, the unmeasurable states are estimated by a newly constructed gain adjustable observer and an observer state‐based adaptive ETM is designed under DoS attacks to save limited communication resources. Then, employing the Lyapunov stability theory and average dwell time (ADT) approach, sufficient conditions are obtained to guarantee the mean‐square exponential stability with a performance index of the new model. And the quantitative relationship between the switching signal and DoS attacks is formulated. Moreover, a co‐design method of ETM parameters and observer‐based controller gains is proposed. Compared with some existent results, restrictions on DoS attack signals and switching signals are relaxed. Finally, a numerical example is utilized to verify the feasibility and effectiveness of the proposed theory.

Defeating Denial of Service Backdoor Attacks

Denial of Service (DoS) backdoor attacks are a significant threat to computer systems. Backdoor attacks involve the installation of unauthorized software on a target system, which is then used to launch various attacks against other systems. In this paper, we explore several techniques that can be used to defeat DoS backdoor attacks, including access control, encryption, network monitoring, patch management, incident response planning, and employee education. Denial of Service (DoS) and Backdoor attacks are common types of cyber-attacks that can cause severe damage to computer systems and networks. DoS attacks aim to disrupt the availability of a system by overwhelming it with traffic or exploiting vulnerabilities, while Backdoor attacks provide attackers with unauthorized access to a system. This paper proposes a framework for defeating both types of attacks by implementing security measures at multiple levels, including the application layer, network layer, and host layer. The proposed framework involves techniques such as traffic filtering, load balancing, network segmentation, access control, and anomaly detection. At the application layer, the framework proposes implementing security measures such as input validation, error handling, and resource allocation to prevent DoS attacks. At the network layer, the framework recommends deploying firewalls, routers, and intrusion detection systems to filter traffic and detect anomalies. At the host layer, the framework proposes implementing security measures such as operating system hardening, patch management, and antivirus software to prevent Backdoor attacks. The proposed framework aims to provide a comprehensive approach to defeating DoS and Backdoor attacks by combining multiple security measures to provide a layered defense. By implementing the proposed framework, organizations can enhance the security of their computer systems and networks and prevent these types of attacks from causing damage.

Observer‐based event‐triggered consensus control of nonlinear cyber‐physical systems under backlash‐like hysteresis and denial‐of‐service attacks

AbstractIn this article, we investigate the problem of event‐triggered distributed consensus control for a class of complex cyber‐physical systems (CPSs), where unknown backlash‐like hysteresis inputs and denial‐of‐service (DoS) attacks are considered in actuators and communication channels, respectively. In the first place, when communication networks are subjected to malicious DoS attacks, the connection weights of network communication topologies experience disruptions. To solve this issue, this work proposes a solution by constructing topological models for communication networks employing switching topologies and utilizing observers to estimate the combined measurement errors during the time interval of DoS attacks. Secondly, to address the repercussions of DoS attacks on control units and to optimize the utilization of communication resources, a distributed control protocol based on an event‐triggered scheme and employing locally deployed estimators is designed to significantly decrease the update frequency of control signals. Meanwhile, utilizing Lyapunov stability theory, the boundedness of all signals in the closed‐loop system is validated, thereby accomplishing the goal of consensus control. At last, the efficacy of the proposed control scheme is demonstrated through two simulation examples.

Examining Ethical Hacking: A Case Study for Information Security Curriculum

Denial of Service (DoS) assaults are a crucial subject in security courses, particularly those that emphasize intrusion detection and ethical hacking methods. This work offers a case study that describes how three typical DoS attacks were implemented as part of extensive offensive hands-on lab exercises. The goal of the exercises is to provide students with the necessary skills to carry out these assaults in a lab setting on a remote network. The report also addresses the moral and legal ramifications of teaching ethical hacking and offers recommendations for improving the efficacy and integrity of information security curricula at educational institutions.

Attack-resilient fault detection for interconnected systems under DoS attack

In light of the expanding cyber-space applications, the imperative consideration of cyber-attack ramifications on system security is evident. This paper presents a resilient dynamic event-triggered fault detection scheme for a class of nonlinear interconnected systems subjected to denial of service (DoS) attacks. To counteract multifaceted threats, the co-design challenge involving switched-type fault detection filters and a resilient dynamic event-triggered transmission mechanism is addressed. In the design phase of the filters, the frequency information of the signal is considered comprehensively and linear solvable conditions ensuring desired augment system performance are delineated. Through a series of comparative simulation experiments, the findings support the conclusion that the proposed attack-tolerant fault detection mechanism not only conserves network resources but also demonstrates superior detection capabilities for specific frequency fault signals.

Binary observation‐based identification for finite impulse response systems under denial of service attacks

SummaryThis article considers the parameter identification problem for the finite impulse response system based on binary observations under denial of service attacks. First, in addition to designing the identification algorithm for the unknown parameter, and its convergence is verified, simultaneously obtaining the asymptotic normality. Then, based on the convergence speed of the identification algorithm and the covariance matrix of the estimation error, the optimal attack strategy problem is transformed into an optimization problem with constraints, then the optimal solution is given. Furthermore, a defense strategy with dual time‐scale input design method is proposed and its effectiveness is demonstrated. Finally, numerical simulations are applied to evidence the correctness of the raised method.