Identifying DOS and DDOS Attack Origin: IP Traceback Methods Comparison and Evaluation for IoT

Cusack, Brian; Zhuang, Ting; Kyaw, Ar Kar

doi:10.1007/978-3-319-52727-7_14

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Recently, Cusack et al [35] discussed and compared many IP traceback approaches based on some metrics such as storage requirements, processing overhead, bandwidth overhead, scalability, etc.…”

Section: Iot Dos/ddos Countermeasure Approachesmentioning

confidence: 99%

Internet of things security: A top-down survey

2018

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Internet of Things (IoT) is one of the promising technologies that has attracted a lot of attention in both industrial and academic fields these years. It aims to integrate seamlessly both physical and digital worlds in one single ecosystem that makes up a new intelligent era of Internet. This technology offers a huge business value for organizations and provides opportunities for many existing applications such as energy, healthcare and other sectors. However, as new emergent technology, IoT suffers from several security issues which are most challenging than those from other fields regarding its complex environment and resources-constrained IoT devices. A lot of researches have been initiated in order to provide efficient security solutions in IoT, particularly to address resources constraints and scalability issues. Furthermore, some technologies related to networking and cryptocurrency fields such as Software Defined Networking (SDN) and Blockchain are revolutionizing the world of the Internet of Things thanks to their efficiency and scalability. In this paper, we provide a comprehensive top down survey of the most recent proposed security and privacy solutions in IoT. We discuss particularly the benefits that new approaches such as blockchain and Software Defined Networking can bring to the security and the privacy in IoT in terms of flexibility and scalability. Finally, we give a general classification of existing solutions and comparison based on important parameters.

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Section: Iot Dos/ddos Countermeasure Approachesmentioning

confidence: 99%

Internet of things security: A top-down survey

2018

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“…The IoT security principle of availability has been also addressed in several studies which have suggested solutions to overcome the security challenges associated with it. These include the works of Maleh et al [49], Kumar et al [50], Kasinathan et al [51], and Cusack et al [52].…”

Section: Iot Security Principlesmentioning

confidence: 99%

AIBPSF-IoMT: Artificial Intelligence and Blockchain-Based Predictive Security Framework for IoMT Technologies

Alshammari

2023

Electronics

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The latest advancements in artificial intelligence (AI) technologies, including machine and deep learning models, in prediction, recommending, and automating processes have greatly impacted IoT devices in general, and protect them from cyberattacks in particular. Blockchain also has features that assist in creating more secure IoT devices due to its abilities of traceability, acceptability, and trust. This paper studies the current advancements in the IoT and blockchain, their architectures, and their effect on security. The paper proposes a novel framework that takes into consideration the advantages and benefits of machine/deep learning models and blockchain in order to provide a solution that makes IoT devices more secure. This framework is based on the IoT four-layer architecture, and it aims to enhance the way IoT devices detect and recognise cyberattacks using blockchain and machine/deep learning algorithms. Machine and deep learning algorithms are responsible for detecting security attacks in the IoT, based on their patterns. The blockchain platform is responsible for verifying whether a specific request is secure, and it also uses cryptography to sign all new requests in order to recognise them in future requests. The MQTTset dataset, which is contains data associated with intrusion detection cases, has been used to implement a case study that aims to prove the validity of this framework. Various machine and deep learning algorithms have been used in this case study which have all achieved high results with regard to precision, recall, accuracy, and F1 performance measurements. Such results have proven the validity and reliability of the proposed framework to detect and predict new attacks before their requests are processed within a particular IoT system.

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“…The IP traceback problem is defined as identifying the actual source of any packet transmitted across the Internet [25]. Various approaches have been proposed [26], [27], and recent studies were focused especially on DDoS attacks using Internet of Things (IoT) devices [12]. Currently, there are six main categories of IP traceback methods [27]: link testing [28], messaging [29], marking [30], logging [31], overlay [32], and pattern analysis [33].…”

Section: Related Workmentioning

confidence: 99%

“…We expect that successful investigations enabled by our scheme will be a strong deterrent to attacks even if it is only partially deployed in the Tor network. IP traceback problems have been intensively studied, in particular for countering denialof-service (DoS) and distributed denial-of-service (DDoS) attacks [12]. However, to the best of our knowledge, in no papers, has means of detecting the actual attack source in the Tor network been discussed.…”

Section: Introductionmentioning

confidence: 99%

Tracing Website Attackers by Analyzing Onion Routers’ Log Files

Pei

Oida

2020

IEEE Access

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The Onion Router (Tor) is one of the major network systems that provide anonymous communication and censorship circumvention. Tor enables its users to surf the Internet, chat, and send messages anonymously; however, cyber attackers also exploit the system for circumventing criminal activity detection. Recently, various approaches that prevent or mitigate abuse of Tor have been proposed in the literature. This paper, which presents one of the approaches, addresses an IP traceback problem. In our model, onion routers that voluntarily participate in attacker tracing detect attack packets (packets carrying attacker's code or data) recorded in the log files by sharing necessary information with an attacked server over an Ethereum blockchain network. The detection algorithm in this paper uses the statistics of packet travel and relay times and outputs attack-packet candidates. The proposed method attaches a reliability degree to each candidate, which is based on the upper bounds of its Type I and II error rates. A smart contract running on the blockchain network ranks the detection results from onion routers according to the reliability degrees.

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Identifying DOS and DDOS Attack Origin: IP Traceback Methods Comparison and Evaluation for IoT

Cited by 11 publications

References 18 publications

Internet of things security: A top-down survey

Internet of things security: A top-down survey

AIBPSF-IoMT: Artificial Intelligence and Blockchain-Based Predictive Security Framework for IoMT Technologies

Tracing Website Attackers by Analyzing Onion Routers’ Log Files

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