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The use of Unmanned Aerial Vehicles (UAVs) or drones has increased lately. This phenomenon is due to UAVs' wide range of applications in fields such as agriculture, delivery, security and surveillance, and construction. In this context, the security and the continuity of UAV operations becomes a crucial issue. Spoofing, jamming, hijacking, and Denial of Service (DoS) attacks are just a few categories of attacks that threaten drones. The present paper is focused on the security of UAVs against DoS attacks. It illustrates the pros and cons of existing methods and resulting challenges. From here, we develop a novel method to detect DoS attacks in UAV environments. DoS attacks themselves have many sub-categories and can be executed using many techniques. Consequently, there is a need for robust protection and mitigation systems to shield UAVs from DoS attacks. One promising security solution is intrusion detection systems (IDSs). IDs paired with machine learning (ML) techniques provide the ability to greatly reduce the risk, as attacks can be detected before they happen. ML plays an important part in improving the performance of IDSs. The many existing ML models that detect DoS attacks on UAVs each carry their own strengths and limitations. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is revolutionizing transportation systems by enabling seamless communication and collaboration among vehicles, roadside units (RSUs), and cloud servers. However, the dynamic and diverse nature of IoV environments raises significant concerns regarding security vulnerabilities and operational efficiency. In response to these challenges, this study proposes an innovative approach that integrates collaborative cryptographic techniques with intelligent reflecting surfaces (IRS). Our approach leverages advanced encryption methods, such as the Advanced Encryption Standard (AES), to ensure secure data transmission, while intelligent reflecting surfaces dynamically adjust their reflective properties to enhance signal propagation and reception. We present a comprehensive network model and algorithmic framework for implementing our proposed strategy, with a specific emphasis on cryptographic protocols and the role of intelligent reflecting surfaces in enhancing both communication security and efficiency. Through theoretical analysis and discussion, we highlight the potential advantages of integrating intelligent reflecting surfaces into secure physical layer (PL), IoV networks, including expanded network coverage, reduced communication overhead, and enhanced energy efficiency. Moreover, we address security threats and vulnerabilities in IoV environments, including potential attacks such as eavesdropping, data tampering, and denial of service. We discuss strategies for mitigating these security risks through the combined use of cryptographic techniques and intelligent reflecting surfaces, thereby bolstering the resilience and robustness of IoV systems. [ABSTRACT FROM AUTHOR]

The increasing number of inverter-based resources (IBR) in the grid introduces new challenges due to the fast transient response and low inertia of IBRs. Set point automatic adjustment with correction enabled (SPAACE)–based techniques smoothen the transient response of an IBR already installed in a grid by modifying its set point without accessing its internal parameters in a model-free approach. Coordinated SPAACE (CSPAACE) further enhances SPAACE performance by incorporating communication links to exchange tracking error values between IBRs; however, this creates openings for cyberattacks. This work adds a detection and mitigation algorithm for both denial of service (DoS) and false data injection (FDI) attacks on the communication channels. Long short-term memory (LSTM) detects anomalies in the inputs received from other inverters, and bidirectional LSTM (BiLSTM) mitigates the adverse effect of attacks on the voltage and frequency stability of a microgrid. A hybrid co-simulation platform is developed using a computer running PSCAD/EMTDC software, a network switch, and two Raspberry Pi computers, where the cyberattacks are conducted on the network switch using one of the Pis. The testbed is used to study the effectiveness of the proposed detection and mitigation method under DoS and FDI attacks and various grid transients.

The increasing use of Wireless Sensor Networks (WSNs) is leading to network traffic growth as data exchange among WSN nodes increases. Protecting WSNs from Denial of Service (DoS) attacks is essential for enhancing data security and avoiding interruptions that can harm productivity and reputation. Detecting a DoS attack quickly is crucial to minimize its impact on the targeted system or network. To meet this requirement, it is critical to have an effective DoS attack detection mechanism that ensures system or network availability and safeguards data and resources. The suggested approach focuses on enhancing DoS attack detection, reducing anomalies, and offering an efficient way to protect WSNs from DoS attacks. A new framework has been proposed to improve DoS attack detection by using optimization techniques and Machine Learning (ML) algorithms to detect and manage DoS attacks effectively. This system integrates Ant Colony Optimization (ACO) with ML algorithms to propose the Enhanced Ant Colony Optimization (EACO) technique. The proposed system has been compared to ACO through the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms to assess their performance in identifying DoS attacks. The results from the assessment show that when the EACO algorithm is paired with ML algorithms, it can achieve accuracy, sensitivity, specificity, and F1 scores between 87.6% and 99.8%. Furthermore, the EACO surpasses ACO in terms of accuracy, sensitivity, specificity, F1 score, precision, and Negative Predictive Value (NPV) by about 3.64%, 38.6%, 1.11%, 27.53%, 16.35%, and 2.78%, respectively.

Industrial Control Systems (ICSs) have become the cornerstone of critical sectors like energy, transportation, and manufacturing. However, the burgeoning interconnectivity of ICSs has also introduced heightened risks from cyber threats. The urgency for robust ICS security validation has never been more pronounced. This paper provides an in-depth exploration of using the MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) framework to validate ICS security. Although originally conceived for enterprise Information Technology (IT), the MITRE ATT&CK framework's adaptability makes it uniquely suited to address ICS-specific security challenges, offering a methodological approach to identifying vulnerabilities and bolstering defence mechanisms. By zeroing in on two pivotal attack scenarios within ICSs and harnessing a suite of security tools, this research identifies potential weak points and proposes solutions to rectify them. Delving into Indicators of Compromise (IOCs), investigating suitable tools, and capturing indicators, this study serves as a critical resource for organisations aiming to fortify their ICS security. Through this lens, we offer tangible recommendations and insights, pushing the envelope in the domain of ICS security validation. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Vehicles (UAVs) or drones has increased lately. This phenomenon is due to UAVs’ wide range of applications in fields such as agriculture, delivery, security and surveillance, and construction. In this context, the security and the continuity of UAV operations becomes a crucial issue. Spoofing, jamming, hijacking, and Denial of Service (DoS) attacks are just a few categories of attacks that threaten drones. The present paper is focused on the security of UAVs against DoS attacks. It illustrates the pros and cons of existing methods and resulting challenges. From here, we develop a novel method to detect DoS attacks in UAV environments. DoS attacks themselves have many sub-categories and can be executed using many techniques. Consequently, there is a need for robust protection and mitigation systems to shield UAVs from DoS attacks. One promising security solution is intrusion detection systems (IDSs). IDs paired with machine learning (ML) techniques provide the ability to greatly reduce the risk, as attacks can be detected before they happen. ML plays an important part in improving the performance of IDSs. The many existing ML models that detect DoS attacks on UAVs each carry their own strengths and limitations.

In this paper the new effective approaches for detecting and mitigating the important cyber-attacks occurred in an AC microgrid (ACMG) renewable energy, including false data injection attack (FDIA), hijack attack (HjA) and denial of service (DoS) are proposed. These methods are applied in the inner loops of the current control in the rotor side converter (RSC) of the doubly fed induction generators (DFIGs) with the marine current turbines (MCTs). The goal is to enhance the power management and consequently, the energy management system against cyber-attacks in the weak grid connected and standalone operation modes. The proposed method is based on correcting and smoothing the reference voltage profiles in the d and q axes sending to the pulse width modulation (PWM) commands of the RSC by occurring the attacks in a multi machine ACMG test system simulated via MATLAB. The obtained results in various scenarios verified the performance of the presented method to contribute for cyber security in ACMGs.

Background: As the digital age unfolds, the requirement for durable, high-throughput communication systems grows inexorably. The convergence of Machine-to-Machine Communication (M2M) with emerging sixth-generation (6G) technology provides novel solutions that can transform our communication landscapes. Objective: This research aims to clarify the capabilities and potentials of integrating Arduino Leonardo, a microcontroller platform with broad application, into 6G-enabled M2M communication systems. The goal is to identify the consequences of changing digital communication paradigms. Methods: Using a thorough experimental methodology, the study builds and evaluates M2M systems using the adaptability of the Arduino Leonardo inside a 6G network architecture. Data transmission latency, integrity, and security are all evaluated across various network situations. Protocols such as Cyclic Redundancy Check (CRC) and Advanced Encryption Standard (AES) ensure data integrity and protect against cybersecurity attacks. Results: The results show a considerable increase in data transmission rates and a decrease in latency within the 6G framework. Concurrently, CRC and AES methods provide effective data corruption prevention and a strong resistance against simulated cybersecurity attacks. However, geographical factors influence signal strength, showing difficulty in ensuring ubiquitous connection. Conclusion: Combining Arduino Leonardo and 6G technology in M2M communication systems offers interesting opportunities for optimizing digital communication. While the results indicate tremendous potential, the highlighted obstacles underline the need for continued refinement and study, creating a solid basis for future explorations in this sector.

Abstract Wireless sensor networks (WSNs) are increasingly being used for data monitoring and collection purposes. Typically, they consist of a large number of sensor nodes that are used remotely to collect data about the activities and conditions of a particular area, for example, temperature, pressure, motion. Each sensor node is usually small, inexpensive, and relatively easy to deploy compared to other sensing methods. For this reason, WSNs are used in a wide range of applications and industries. However, WSNs are vulnerable to different kinds of security threats and attacks. This is primarily because they are very limited in resources like power, storage, bandwidth, and processing power that could have been used in developing their defense. To ensure their security, an effective Intrusion detection system (IDS) need to be in place to detect these attacks even under these constraints. Today, traditional IDS are less effective as these malicious attacks are becoming more intelligent, frequent, and complex. Denial of service (DOS) attack is one of the main types of attacks that threaten WSNs. For this reason, we review related works that focus on detecting DoS attacks in WSN. In addition, we developed and implemented several Deep learning (DL) based IDS. These systems were trained on a specialized dataset for WSNs called WSN-DS in detecting four types of DoS attacks that affects WSNs. They include the Blackhole, Grayhole, Flooding, and Scheduling attacks. Finally, we evaluated and compared the results and we discuss possible future works.

Kubernetes is an orchestration tool that runs and manages container-based workloads. It works as a collection of different virtual or physical servers that support multiple storage capacities, provide network functionalities, and keep all containerized applications active in a desired state. It also provides an increasing fleet of different facilities, known as microservices. However, Kubernetes' scalability has led to a complex network structure with an increased attack vector. Attackers can launch a Denial of service (DoS) attack against servers/machines in Kubernetes by producing fake traffic load, for instance. DoS or Distributed Denial of service (DDoS) attacks are malicious attempts to disrupt a targeted service by flooding the target's service with network packets. Constant observation of the network traffic is extremely important for the early detection of such attacks. Extended Berkeley Packet Filter (eBPF) and eXpress Datapath (XDP) are advanced technologies in the Linux kernel that perform high-speed packet processing. In the case of Kubernetes, eBPF and XDP can be used to protect against DDoS attacks by enabling fast and efficient network security policies. For example, XDP can be used to filter out traffic that is not authorized to access the Kubernetes cluster, while eBPF can be used to monitor network traffic for signs of DDoS attacks, such as excessive traffic from a single source. In this research, we utilize eBPF and XDP to build a detection and observation mechanism to filter out malicious content and mitigate a Denial of Service attack on Kubernetes. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSN), attacks mostly aim in limiting or eliminating the capability of the network to do its normal function. Detecting this misbehaviour is a demanding issue. And so far the prevailing research methods show poor performance. AQN3 centred efficient Intrusion Detection Systems (IDS) is proposed in WSN to ameliorate the performance. The proposed system encompasses Data Gathering (DG) in WSN as well as Intrusion Detection (ID) phases. In DG, the Sensor Nodes (SN) is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means (DFFF) algorithm chooses the Cluster Head (CH). Then, the data is amassed by the discovered path. Next, it is tested with the trained IDS. The IDS encompasses '3' steps: pre-processing, matrix reduction, and classification. In pre-processing, the data is organized in a clear format. Then, attributes are presented on the matrix format and the ELDA (entropybased linear discriminant analysis) lessens the matrix values. Next, the output as of the matrix reduction is inputted to the QN3 classifier, which classifies the denial-of-services (DoS), Remotes to Local (R2L), Users to Root (U2R), and probes into attacked or Normal data. In an experimental estimation, the proposed algorithm's performance is contrasted with the prevailing algorithms. The proposed work attains an enhanced outcome than the prevailing methods. [ABSTRACT FROM AUTHOR]

Peningkatan penggunaan perangkat Internet of Things (IoT) rumahan dibarengi dengan berkembangnya berbagai kerentanan keamanan pada jaringan rumah. Keterbatasan sumber daya yang dimiliki perangkat IoT menjadikannya target ideal untuk diluncurkan serangan Denial of Service (DoS), termasuk SYN flood. Serangan ini cenderung menargetkan sumber daya IoT yang krusial, termasuk CPU. Apabila dilakukan secara terus menerus, serangan ini mampu menguras sumber daya IoT dan membuatnya kehilangan fungsionalitasnya. Penerapan iptables pada Raspberry Pi mampu meminimalkan dampak serangan SYN flood. Iptables memiliki berbagai chains dan tables dengan kemampuan dan fungsi yang berbeda. Penelitian ini bertujuan untuk menganalisis perbedaan pengaruh penggunaan iptables chains terhadap penggunaan CPU Raspberry Pi. Penggunaan CPU yang semakin rendah memperkecil kemungkinan terjadinya DoS. Hasil penelitian ini kemudian akan dibandingkan dengan penggunaan CPU ketika aturan iptables pada penelitian terdahulu diterapkan. Hasilnya, kedua aturan mengonsumsi CPU dalam persentase yang hampir sama.

Wireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are increasingly being used for data monitoring and collection purposes. Typically, they consist of a large number of sensor nodes that are used remotely to collect data about the activities and conditions of a particular area, for example, temperature, pressure, motion. Each sensor node is usually small, inexpensive, and relatively easy to deploy compared to other sensing methods. For this reason, WSNs are used in a wide range of applications and industries. However, WSNs are vulnerable to different kinds of security threats and attacks. This is primarily because they are very limited in resources like power, storage, bandwidth, and processing power that could have been used in developing their defense. To ensure their security, an effective Intrusion detection system (IDS) need to be in place to detect these attacks even under these constraints. Today, traditional IDS are less effective as these malicious attacks are becoming more intelligent, frequent, and complex. Denial of service (DOS) attack is one of the main types of attacks that threaten WSNs. For this reason, we review related works that focus on detecting DoS attacks in WSN. In addition, we developed and implemented several Deep learning (DL) based IDS. These systems were trained on a specialized dataset for WSNs called WSN-DS in detecting four types of DoS attacks that affects WSNs. They include the Blackhole, Grayhole, Flooding, and Scheduling attacks. Finally, we evaluated and compared the results and we discuss possible future works. [ABSTRACT FROM AUTHOR]

Cloud computing is the technology that is currently used to provide users with infrastructure, platform, and software services effectively. Under this system, Platform as a Service (PaaS) offers a medium headed for a web development platform that uniformly distributes the requests and resources. Hackers using Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks abruptly interrupt these requests. Even though several existing methods like signature-based, statistical anomaly-based, and stateful protocol analysis are available, they are not sufficient enough to get rid of Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks and hence there is a great need for a definite algorithm. Concerning this issue, we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis, Spearman coefficient, and mitigation technique. It can easily differentiate common traffic and attack traffic. Not only that, it greatly helps the network to distribute the resources only for authenticated requests. The effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99% for the numerous DoS attack as well as DDoS attacks. [ABSTRACT FROM AUTHOR]

Traffic safety and efficiency are extremely significant in both private and public transportation. The fifth-generation (5G)-enabled vehicular networks works wirelessly to share information among vehicles for helping drivers and passengers. Since the vehicle broadcasts the traffic status messages, privacy and security are considered as a challenging issue in 5G-enabled vehicular networks. In order to satisfy these privacy and security requirements, many privacy-preserving and security attacks schemes have been proposed. Nevertheless, since these schemes use a complex elliptic curve and bilinear pair cryptography operations, the performance efficiency of in terms of communication and computational costs is not satisfactory, which denial of service (DoS) attacks occurs. To address this, this paper proposes modular square root-based to resist denial of service (DoS) attacks (MSR-DoS) scheme in 5G-enabled vehicular networks. Our MSR-DoS scheme satisfies authenticity of source, integrity of message, pseudonym privacy-preserving, unlinkable, traceable and revocable in vehicular networks. The security of our work is proved under burrows abadi needham (BAN) logic. The performance analysis and comparison shows that MSR-DoS scheme has less communication and computational costs as compared to the most recent existing works. Meanwhile, the proposed MSR-DoS scheme reduces the computation overhead of signing the message and verifying the message by 99.80% and 98.55%, respectively.

Kubernetes is an orchestration tool that runs and manages container-based workloads. It works as a collection of different virtual or physical servers that support multiple storage capacities, provide network functionalities, and keep all containerized applications active in a desired state. It also provides an increasing fleet of different facilities, known as microservices. However, Kubernetes’ scalability has led to a complex network structure with an increased attack vector. Attackers can launch a Denial of service (DoS) attack against servers/machines in Kubernetes by producing fake traffic load, for instance. DoS or Distributed Denial of service (DDoS) attacks are malicious attempts to disrupt a targeted service by flooding the target’s service with network packets. Constant observation of the network traffic is extremely important for the early detection of such attacks. Extended Berkeley Packet Filter (eBPF) and eXpress Datapath (XDP) are advanced technologies in the Linux kernel that perform high-speed packet processing. In the case of Kubernetes, eBPF and XDP can be used to protect against DDoS attacks by enabling fast and efficient network security policies. For example, XDP can be used to filter out traffic that is not authorized to access the Kubernetes cluster, while eBPF can be used to monitor network traffic for signs of DDoS attacks, such as excessive traffic from a single source. In this research, we utilize eBPF and XDP to build a detection and observation mechanism to filter out malicious content and mitigate a Denial of Service attack on Kubernetes.

Over time, the use of wireless technologies has significantly increased due to bandwidth improvements, cost-effectiveness, and ease of deployment. Owing to the ease of access to the communication medium, wireless communications and technologies are inherently vulnerable to attacks. These attacks include brute force attacks such as jamming attacks and those that target the communication protocol (Wi-Fi and Bluetooth protocols). Thus, there is a need to make wireless communication resilient and secure against attacks. Existing wireless protocols and applications have attempted to address the need to improve systems security as well as privacy. They have been highly effective in addressing privacy issues, but ineffective in addressing security threats like jamming and session hijacking attacks and other types of Denial of Service Attacks. In this article, we present an “architecture for resilient wireless communications” based on the concept of Moving Target Defense. To increase the difficulty of launching successful attacks and achieve resilient operation, we changed the runtime characteristics of wireless links, such as the modulation type, network address, packet size, and channel operating frequency. The architecture reduces the overhead resulting from changing channel configurations using two communication channels, in which one is used for communication, while the other acts as a standby channel. A prototype was built using Software Defined Radio to test the performance of the architecture. Experimental evaluations showed that the approach was resilient against jamming attacks. We also present a mathematical analysis to demonstrate the difficulty of performing a successful attack against our proposed architecture.

The wide proliferation of telecommunication technologies in the last decade gives rise to the number of more sophisticated security threats. Software-Defined Networking (SDN) is a new networking architecture that isolates the network control plane from the data plane that incidentally provides better features and functionalities to detect and deal with those security threats. Its elastic programmable feature permits efficient network management and provides network operators with the flexibility to monitor and fine-tune their network. However, the new technology also created many new security concerns, and the threat of Distributed Denial of Service (DDoS) attack is one of the major concerns. This paper presents a comprehensive review of state-of-the-art techniques to detect DDoS attacks on SDN controller. It first describes the SDN technology and then elaborates on the mechanism of DDoS attacks on SDN. Additionally, this paper also describes all major DDoS detection techniques and classifies them at a very high level according to the techniques or methods used. The current survey is qualitatively compared with the existing surveys using various author-defined metrics. Finally, this paper provides a guideline for future research related to detection techniques of DDoS against the SDN controller.