A comprehensive survey on DDoS attacks on various intelligent systems and it's defense techniques

Gaurav, Akshat; Gupta, Brij B.; Alhalabi, Wadee; Visvizi, Anna; Asiri, Yousef

doi:10.1002/int.23048

Cited by 11 publications

(4 citation statements)

References 74 publications

(74 reference statements)

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“…• DoS/DDoS Attacks: DDoS attacks are carried out by a group of compromised computers operating from multiple locations that flood a particular target with traffic. In this attack, the primary goal is to overwhelm and render unavailable a server, website, or online service [126] [127]. In the event of a DDoS attack, IoT devices such as smart appliances, healthcare monitors, and industrial sensors can be rendered useless [128].…”

Section: Figure 7: Cybersecurity Threats In Application Layermentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

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Section: Figure 7: Cybersecurity Threats In Application Layermentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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“…Error messages are used to report errors that can occur during data packet delivery. Diagnostics, testing, and other informational purposes are served by informational messages [45], [46], [47], [48], [49], [50], [51]. An attacker can launch different types of ICMP based attacks, including Ping There are some common types of ICMP (Internet Control Message Protocol) based cyber security attacks such as Ping flood attacks [50] and Smurf attacks [40].…”

Section: A Icmp Based Cyber Attacksmentioning

confidence: 99%

Experimental Evaluation of Smart Electric Meters’ Resilience Under Cyber Security Attacks

2023

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“…The techniques employed in DDoS attacks are subject to constant evolution, as evidenced by the literature [8]. Employing outdated countermeasures is insufficient to protect against novel threats [9].…”

Section: Introductionmentioning

confidence: 99%

Detection of Unknown DDoS Attack Using Convolutional Neural Networks Featuring Geometrical Metric

2023

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DDoS attacks remain a persistent cybersecurity threat, blocking services to legitimate users and causing significant damage to reputation, finances, and potential customers. For the detection of DDoS attacks, machine learning techniques such as supervised learning have been extensively employed, but their effectiveness declines when the framework confronts patterns exterior to the dataset. In addition, DDoS attack schemes continue to improve, rendering conventional data model-based training ineffectual. We have developed a novelty open-set recognition framework for DDoS attack detection to overcome the challenges of traditional methods. Our framework is built on a Convolutional Neural Network (CNN) construction featuring geometrical metric (CNN-Geo), which utilizes deep learning techniques to enhance accuracy. In addition, we have integrated an incremental learning module that can efficiently incorporate novel unknown traffic identified by telecommunication experts through the monitoring process. This unique approach provides an effective solution for identifying and alleviating DDoS. The module continuously improves the model’s performance by incorporating new knowledge and adapting to new attack patterns. The proposed model can detect unknown DDoS attacks with a detection rate of over 99% on conventional attacks from CICIDS2017. The model’s accuracy is further enhanced by 99.8% toward unknown attacks with the open datasets CICDDoS2019.

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A comprehensive survey on DDoS attacks on various intelligent systems and it's defense techniques

Cited by 11 publications

References 74 publications

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Experimental Evaluation of Smart Electric Meters’ Resilience Under Cyber Security Attacks

Detection of Unknown DDoS Attack Using Convolutional Neural Networks Featuring Geometrical Metric

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