LEoNIDS: A Low-Latency and Energy-Efficient Network-Level Intrusion Detection System

Tsikoudis, Nikos; Papadogiannakis, Antonis; Markatos, Evangelos P.

doi:10.1109/tetc.2014.2369958

Cited by 33 publications

(14 citation statements)

References 22 publications

(20 reference statements)

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“…Compared with the simple methods of cloud computing, analyzing data on edge nodes and making critical time‐sensitive decisions on the local network make the system more robust and predictable. Moreover, the processed data can be transmitted to the cloud for storage and further analysis, which also minimizes the flow between cloud layers …”

Section: Design and Implementation Of The Sewmsmentioning

confidence: 99%

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SEWMS: An edge‐based smart wearable maintenance system in communication network

Rui

Qin

et al. 2019

Softw Pract Exp

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Summary The development of the Internet of Things (IoT) and wearable technology provides an opportunity for the development of maintenance of communication. The use of wearable technology and instant messaging technology of IoT can improve the support capabilities and data interaction ability of on‐site maintenance of the communication network. Existing communication maintenance systems lack real‐time operation and maintenance of data interaction. In the field operation decision‐making and execution process, there are problems of lack of field links and inconvenient information interaction. On‐site maintenance mainly relies on maintenance personnel to actively search for information, and the retrieval results are lacking of personalization, which makes it difficult to meet the needs of on‐site maintenance of the communication network. Edge computing and information push technology can solve these problems to some extent. In this paper, we focus on the current low level in information, complicated scenes, and various information of on‐site maintenance and propose a dynamic context‐aware information push algorithm. The simulation results demonstrate that the algorithm delivers good performance in terms of precision, recall, and F1. Besides, we present a smart wearable maintenance system, an edge computing–assisted IoT platform for the real‐time guidance of technical experts and systems for on‐site maintenance personnel, aiming to improve the efficiency and quality of on‐site maintenance.

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Section: Design and Implementation Of The Sewmsmentioning

confidence: 99%

“…Moreover, the processed data can be transmitted to the cloud for storage and further analysis, which also minimizes the flow between cloud layers. 25…”

Section: Low Latencymentioning

confidence: 99%

SEWMS: An edge‐based smart wearable maintenance system in communication network

Rui

Qin

et al. 2019

Softw Pract Exp

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“…Another NLIDS named LEoNIDS is presented in the work of Tsikoudis et al The authors try to solve energy‐latency trade‐off by achieving low latency and power efficiency at the same time. Instead of focusing on reduction in computational complexity of the IDS that would result in energy efficiency and low latency, a popular IDS, ie, Snort, is used and modern features of multicore microprocessor platforms are exploited.…”

Section: Related Workmentioning

confidence: 99%

“…Computational grid is used because IIDPS is computationally hungry. Unlike the work of Tsikoudis et al, asymptotic analysis is provided for IIDPS. Computational complexities of mining server and detection server are O ( n 6 ) and

O false(sans-serifg n^{5} false)

, respectively, where

sans-serifg

is number of legal users.…”

Section: Related Workmentioning

confidence: 99%

Convergence of detection probability, computational gains, and asymptotic analysis of an algorithm for physical‐layer intrusion detection system

Yousaf

Loan

Babiceanu

et al. 2018

Trans Emerging Tel Tech

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Asymptotic analysis of an algorithm is crucial for practical engineering design. It provides important information about the performance of an algorithm in terms of time complexity, space complexity, and input scale. Based on these performance metrics, engineers can utilize available resources for efficient implementation of the algorithm. In this paper, asymptotic analysis is performed for an algorithm designed for a physical‐layer intrusion detection system (PLIDS). The algorithm first solves Fredholm integral equation of the first kind using Tikhonov regularization. Then, the solution of this equation is used to design a criterion for detection of smart jamming attacks at physical‐layer of the TCP/IP protocol stack. The proposed detection criterion works in constant time. The asymptotic performance metric derived for PLIDS gives insights about its algorithmic behavior. Convergence of detection probability and computational gain of using PLIDS, over higher‐layer intrusion detection systems, are also explained. Finally, apart from asymptotic performance, the detection performance metrics of the PLIDS are also discussed.

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“…Detection latency is defined to be the time passed from the arrival of the last packet that contains the attack until the alert generation in the network Interrupt handling time is negligible compared to the queuing delay and network intrusion detection system (NIDS) processing time [90]. Therefore incoming packets will experience a finite delay and will be placed in a queue before being served by the processing node.…”

Section: Intrusion Detection Latencymentioning

confidence: 99%

Network intrusion detection via distributed machine learning on smart gateway network

Khalid¹

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During my time spent at NTU for my master of engineering program, i have come across several individuals who have helped me in my research work. As such i am greatly thankful to these people for their help and insight. With their guidance and help i was able to make most of this great learning opportunity and complete my research. First and foremost, i am eternally grateful and gratuitous towards my supervisor Professor Yu Hao in providing me an opportunity to pursue my graduate studies under his supervision. His feedback, advice and support enabled me for a successful completion of my studies. Professor Yu Hao always encouraged me to work hard and be enthusiastic towards research. Under his supervision i have learned new skills which will benefit me in future. Secondly i am grateful towards the confirmation exercise examiners Professor Soh Yeng Chai, Professor Lipo Wang and Professor Tay Wee Peng for accessing my project. I will also like to thank the staff of graduate pro gramme office for their assistance in administrative matters for my graduate studies. I am also grateful to JTC Corporation and Energy Research Institute @ NTU in providing me an opportunity to work on their projects

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LEoNIDS: A Low-Latency and Energy-Efficient Network-Level Intrusion Detection System

Abstract: This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Cited by 33 publications

References 22 publications

SEWMS: An edge‐based smart wearable maintenance system in communication network

SEWMS: An edge‐based smart wearable maintenance system in communication network

Convergence of detection probability, computational gains, and asymptotic analysis of an algorithm for physical‐layer intrusion detection system

Network intrusion detection via distributed machine learning on smart gateway network

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