Fundamental scaling laws of covert DDoS attacks

Ramtin, Amir Reza; Nain, Philippe; Menasché, Daniel Sadoc; Towsley, D.; Silva, Edmundo de Souza e

doi:10.1016/j.peva.2021.102236

Cited by 15 publications

(2 citation statements)

References 29 publications

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“…The knowledge-based VQA (Visual Question Answering) module developed by Zheng et al [47] is designed to extend the versatility of knowledge-based VQA. Ramtin et al [48] analyzed the maximum damage that a DDoS attacker can make without being detected by a detection system at the network edge. They considered two classical classifiers based on hypothesis testing, whether the detector knows the distribution of attack traffic or not.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

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“…One of the great advantages of data fusion is anomaly (e.g., a DDoS attack) detection. However, recently, it has been theoretically proved that the attacker is undetectable under some circumstances in the face of any anomaly detection approaches 21,22 . These novel theoretical results challenge the current anomaly and intrusion detection methods that leverage data fusion.…”

Section: Introductionmentioning

confidence: 99%

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Pourghebleh

Hekmati

Davoudnia

et al. 2022

Concurrency and Computation

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Nowadays, with the rapid progress of Internet-based and distributed systems such as cloud computing, peer-to-peer networking, and Internet of Things (IoT), significant improvements in almost every engineering and commercial field have been made. On the basis of IoT, smart cities are formed utilizing intelligent information processing, universal connectivity, ubiquitous sensing, and real-time monitoring. Energy conservation is one of the significant issues in current IoT development due to the poor battery endurance of IoT objects. Over the last years, with smart cities' explosive growth, a huge range of studies regarding energy efficiency have been done. The diversity of sparse data and multi-sourcing is utilized in developing IoT scenarios. In order to use efficiently of these data to improve the IoT services, data fusion plays an important role. It saves network resources, improves data transmission efficiency, and extracts useful information from raw data. To the best of our knowledge, there is still a lack of a comprehensive and systematic study about surveying and analyzing the available energy-efficient data fusion techniques in the IoT. Thus, this article aims to address this gap using a systematic method.

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Heat transfer improvement between a pair of heater and cooler inside an energy storage by using nano-encapsulated phase change material/water: A numerical modeling

Cao

Farouk

Ayed

et al. 2022

Case Studies in Thermal Engineering

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Fundamental scaling laws of covert DDoS attacks

Cited by 15 publications

References 29 publications

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Heat transfer improvement between a pair of heater and cooler inside an energy storage by using nano-encapsulated phase change material/water: A numerical modeling

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