IoT as a Land of Opportunity for DDoS Hackers

Vlajic, Natalija; Zhou, Daiwei

doi:10.1109/mc.2018.3011046

Cited by 92 publications

(45 citation statements)

References 4 publications

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“…e Smart Detection system has reached high accuracy and low false-positive rate. Experiments were conducted using two Virtual Linux boxes, Define all the descriptor database variables as the current variables; (5) while True do (6) Split dataset in training and test partitions; (7) Create and train the model using training data partition; (8) Select the most important variables from the trained model; (9) Calculate the cumulative importance of variables from the trained model; (10) if max (cumulative importance of variables) < Variable importance threshold then (11) Exit loop; (12) end (13) Train the model using only the most important variables; (14) Test the trained model and calculate the accuracy; (15) if Calculated accuracy < Accuracy threshold then (16) Exit loop; (17) end (18) Add current model to optimized model set; (19) Define the most important variables from the trained model as the current variables; (20) end (21) end (22) Group the models by number of variables; (23) Remove outliers from the grouped model set; (24) Select the group of models with the highest frequency and their number of variables "N"; (25) Rank the variables by the mean of the importance calculated in step 7; (26) Return the "N" most important variables; [2004][2005] have been used by the researchers to evaluate the performance of their proposed intrusion detection and prevention approaches. However, many such datasets are out of date and unreliable to use [25].…”

Section: Resultsmentioning

confidence: 99%

“…As a consequence, legitimate tra c is also obstructed [6]. On the other hand, the attackers are using more advanced techniques to potentiate attacks and ood the victim such as DDoS-for-hire, IoT-based DDoS attacks, and reflection DDoS attacks [7][8][9], profiting from the computational capability and geographical distribution promoted by the wide variety of devices and its diverse mobility patterns, typically founded in IoT and mobile IoT scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning

Filho

Silveira

Júnior

et al. 2019

Security and Communication Networks

188

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Users and Internet service providers (ISPs) are constantly affected by denial-of-service (DoS) attacks. This cyber threat continues to grow even with the development of new protection technologies. Developing mechanisms to detect this threat is a current challenge in network security. This article presents a machine learning- (ML-) based DoS detection system. The proposed approach makes inferences based on signatures previously extracted from samples of network traffic. The experiments were performed using four modern benchmark datasets. The results show an online detection rate (DR) of attacks above 96%, with high precision (PREC) and low false alarm rate (FAR) using a sampling rate (SR) of 20% of network traffic.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning

Filho

Silveira

Júnior

et al. 2019

Security and Communication Networks

188

View full text Add to dashboard Cite

show abstract

“…For example, vulnerable webcams, routing devices and firewalls were analyzed by Albataineh et al [64] after detecting them with Shodan. Web cameras were also studied by Bugeja et al [65], who found that a significant number of them were weakly protected or not protected at all, which may allow attackers to use them for cyberattacks [66].…”

Section: Iiot and Industry 40 Cybersecurity Toolsmentioning

confidence: 99%

Use Case Based Blended Teaching of IIoT Cybersecurity in the Industry 4.0 Era

Fernández‐Caramés

Fraga‐Lamas

2020

Applied Sciences

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Industry 4.0 and Industrial Internet of Things (IIoT) are paradigms that are driving current industrial revolution by connecting to the Internet industrial machinery, management tools or products so as to control and gather data about them. The problem is that many IIoT/Industry 4.0 devices have been connected to the Internet without considering the implementation of proper security measures, thus existing many examples of misconfigured or weakly protected devices. Securing such systems requires very specific skills, which, unfortunately, are not taught extensively in engineering schools. This article details how Industry 4.0 and IIoT cybersecurity can be learned through practical use cases, making use of a methodology that allows for carrying out audits to students that have no previous experience in IIoT or industrial cybersecurity. The described teaching approach is blended and has been imparted at the University of A Coruña (Spain) during the last years, even during the first semester of 2020, when the university was closed due to the COVID-19 pandemic lockdown. Such an approach is supported by online tools like Shodan, which ease the detection of vulnerable IIoT devices. The feedback results provided by the students show that they consider useful the proposed methodology, which allowed them to find that 13% of the IIoT/Industry 4.0 systems they analyzed could be accessed really easily. In addition, the obtained teaching results indicate that the established course learning outcomes are accomplished. Therefore, this article provides useful guidelines for teaching industrial cybersecurity and thus train the next generation of security researchers and developers.

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“…Another significant problem in IoT security is the common threat of Distributed Denial-of-Services [5]. As the IoT nodes are connected via cloud services; therefore, it is eventual that the adversarial affecting cloud ecosystem could also easily affect the devices networks without much effort.…”

Section: Introductionmentioning

confidence: 99%

Novel authentication framework for securing communication in internet-of-things

Patil¹,

Biradar²

2020

IJECE

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Internet-of-Things (IoT) offers a big boon towards a massive network of connected devices and is considered to offer coverage to an exponential number of the smart appliance in the very near future. Owing to the nascent stage of evolution of IoT, it is shrouded by security loopholes because of various reasons. Review of existing research-based solution highlights the usage of conventional cryptographic-based solution over the traditional mechanism of data forwarding process between IoT nodes and gateway. The proposed system presents a novel solution to this problem by a model that is capable of performing a highly secured and cost-effective authentication process. The proposed system introduces Authentication Using Signature (AUS) as well as Security with Complexity Reduction (SCR) for the purpose to resist participation of any form of unknown threats.The outcome of the model shows better security strength with faster response time and energy saving of the IoT nodes.

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IoT as a Land of Opportunity for DDoS Hackers

Cited by 92 publications

References 4 publications

Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning

Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning

Use Case Based Blended Teaching of IIoT Cybersecurity in the Industry 4.0 Era

Novel authentication framework for securing communication in internet-of-things

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