DoS attack article in any renowed nows within the past six months 2023
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DDoS attacks remain a major security threat to Internet infrastructures, web services, and cloud platforms. Despite efforts to detect and protect against DDoS attacks, they continue to evolve and pose a significant challenge. Recent research has identified thousands of DDoS amplification attacks in a single day, using both well-known and newly discovered amplification protocols . The landscape of DDoS attack mechanisms is constantly changing, making it necessary to update our perspective on these attacks . DDoS attacks can overload backbone capacity and filtering approaches in prior work are not effective in mitigating them . The increasing sophistication and diversification of DDoS attacks, moving from the network to the application level, have made DDoS mitigation techniques less effective . It is crucial for the research community to address fundamental design issues in network architecture and develop enhanced infrastructure to combat DDoS attacks .
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Open access•Journal Article 2 Citations | There is no information provided about any specific DoS attack article in renowned news within the past six months of 2023. |
| No information is provided about any specific DoS attack articles in renowned news sources within the past six months of 2023. | |
Open access•Posted Content | No information is provided about any specific DoS attack articles in renowned news sources within the past six months of 2023. |
Open access•Posted Content | No information is provided about any specific DoS attack articles in renowned news sources within the past six months of 2023. |
29 Mar 2021 17 Citations | No information is provided about any specific DoS attack articles in renowned news sources within the past six months of 2023. |
Related Questions
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DoS attack and machine learning or deep learning5 answersDoS attacks and machine learning or deep learning techniques have been extensively studied in the field of cybersecurity. Traditional machine learning techniques have been found to be inefficient in handling extensive network data and extracting high-level features for attack detection. Therefore, researchers have proposed the use of deep learning models for the detection and classification of DoS attacks. These models incorporate techniques such as deep convolutional generative adversarial networks (DCGAN), ResNet-50, and optimized AlexNet-based classifiers. The use of deep learning algorithms has shown promising results in achieving high accuracy in detecting DoS attacks, with accuracy values ranging from 99.33% to 100%. Additionally, deep learning-based approaches have been found to outperform traditional methods in identifying and mitigating distributed denial of service (DDoS) attacks.
What are the most effective ways to mitigate DoS attacks in iot networks?4 answersThe most effective ways to mitigate DoS attacks in IoT networks include the design and placement of intrusion detection systems (IDSs) that can handle both high-rate and low-rate DDoS attacks with good accuracy. Anomaly-based intrusion detection systems (IDSs) that use statistical or machine learning methodologies, such as exponentially weighted moving average (EWMA), K-nearest neighbors (KNN), and the cumulative sum algorithm (CUSUM), have been shown to be efficient in mitigating DDoS attacks in IoT networks. Additionally, the use of Network Function Virtualization (NFV) and machine learning (ML) techniques can help in placing security Virtual Network Functions (VNFs) based on NFV performance, achieving high accuracy in mitigating DDoS attacks on Industrial IoT (IIoT) networks.
What's DOS attack?3 answersA Denial of Service (DoS) attack is a cyber-attack that aims to make network resources or services unavailable to their intended users. It involves overwhelming a targeted machine with requests, causing normal traffic to be unable to be processed and resulting in denial-of-service to additional users. DoS attacks can be classified into different types based on the network and attacker behavior, such as TCP SYN attacks and UDP attacks. Distributed Denial of Service (DDoS) attacks are an advanced version of DoS attacks that use multiple sources or compromised computers to carry out the attack. To detect and mitigate DoS attacks, machine learning methods can be used to identify unusual anomalies or patterns in network traffic. Various defense mechanisms, including client-side and server-side defenses, are available to prevent and mitigate DoS attacks.
What are the different types of DoS attacks that can be launched on smart healthcare systems?5 answersDifferent types of Denial of Service (DoS) attacks that can be launched on smart healthcare systems include rapid destruction of the network and taking control of the network gradually. These attacks can pose significant threats to the healthcare industry, especially during critical situations like the COVID-19 pandemic. The comprehensive analysis of these attacks is crucial for implementing robust security solutions in healthcare systems.These attacks can significantly degrade the performance of machine learning-based smart healthcare systems, leading to erroneous treatment and patient misclassification. Adversarial ML algorithms such as HopSkipJump, Fast Gradient Method, Crafting Decision Tree, Carlini & Wagner, and Zeroth Order Optimization can be employed to perform malicious activities like data poisoning and misclassification on smart healthcare systems.Additionally, other attacks that can endanger health monitoring systems include Fingerprint and Timing-based Snooping, Router Attack, Select and Forwarding attack, Sensor attack, and Replay Attack.
Dos attacks in campus area network5 answersDDoS attacks are a major security threat in campus area networks, particularly in academic institutions where students, faculty, and staff members use the network for various purposes. These attacks can target the legitimate nodes of the network, causing resource exhaustion and disruption of services. Traditional security mechanisms like the Defense-In-Depth (DID) model have limitations in effectively defending against complex and volatile DOS/DDoS attacks. To address this issue, researchers have proposed new approaches such as using machine learning models for early detection of DDoS attacks. Additionally, techniques like bandwidth limiting rules implemented in unified threat management (UTM) firewalls can help mitigate the impact of DDoS attacks in campus LANs. Evaluating the performance and detection capabilities of intrusion detection systems like Snort can also contribute to enhancing campus network security. Overall, protecting campus area networks from DDoS attacks requires a combination of proactive detection, effective defense mechanisms, and continuous evaluation of security solutions.