ForChaos: Real Time Application DDoS Detection Using Forecasting and Chaos Theory in Smart Home IoT Network

Procopiou, Andria; Komninos, Nikos; Douligeris, Christos

doi:10.1155/2019/8469410

Cited by 39 publications

(14 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modification of a canary file is an indication of unauthorised access. Procopiou et al [15] proposed a lightweight algorithm based on forecasting and chaos theory to identify flooding and DDoS attacks launched by compromised smart home devices. For every time-series behaviour collected, a forecast is generated, and the error of the forecast against the actual value is assessed by the Lyapunov exponent to determine if an attack has occurred.…”

Section: B Behaviour-based Smart Home Idsmentioning

confidence: 99%

Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning

Heartfield

Loukas

Bezemskij

et al. 2021

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

The modern Internet of Things (IoT)-based smart 1 home is a challenging environment to secure: devices change, 2 new vulnerabilities are discovered and often remain unpatched, 3 and different users interact with their devices differently and 4 have different cyber risk attitudes. A security breach's impact is 5 not limited to cyberspace, as it can also affect or be facilitated 6 in physical space, for example, via voice. In this environment, 7 intrusion detection cannot rely solely on static models that 8 remain the same over time and are the same for all users.9We present MAGPIE, the first smart home intrusion detection 10 system that is able to autonomously adjust the decision function 11 of its underlying anomaly classification models to a smart home's 12 changing conditions (e.g., new devices, new automation rules and 13 user interaction with them). The method achieves this goal by 14 applying a novel probabilistic cluster-based reward mechanism 15 to non-stationary multi-armed bandit reinforcement learning. 16 MAGPIE rewards the sets of hyperparameters of its underlying 17 isolation forest unsupervised anomaly classifiers based on the 18 cluster silhouette scores of their output. 19 Experimental evaluation in a real household shows that MAG-20 PIE exhibits high accuracy because of two further innovations: 21 it takes into account both cyber and physical sources of data; 22 and it detects human presence to utilise models that exhibit the 23 highest accuracy in each case. MAGPIE is available in open-24 source format, together with its evaluation datasets, so it can 25 benefit from future advances in unsupervised and reinforcement 26 learning and be able to be enriched with further sources of data 27 as smart home environments and attacks evolve.

show abstract

Section: B Behaviour-based Smart Home Idsmentioning

confidence: 99%

Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning

Heartfield

Loukas

Bezemskij

et al. 2021

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

show abstract

“…The idea is based on a smart routing model for home networks. ForChaos model to detect DDoS attacks was proposed in [176], whereas forecasting method was used chaos theory. Another algorithm for the detection of malicious traffic was proposed in [177].…”

Section: B Security In Data Managementmentioning

confidence: 99%

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

et al. 2021

View full text Add to dashboard Cite

The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this paper, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry. INDEX TERMS Internet of Things (IoT), smart home, healthcare, energy management, security. I. INTRODUCTION O VER time, the type of home was evolving. The shape of the house was evolving during ages, cultural revolutions, and climate changes. We have divided our homes into rooms devoted to sleeping, cooking, work, children, sanitary needs, warehousing, etc. People started to settle in various locations and continents.

show abstract

“…Wang et al [ 26 ] proposed a new task scheduling approach to fulfil the requirements of smart homes and healthcare regarding tight set situations of the elderly or patients, using edge computing-themed processing schemes with a focus on real-time issues. Procopiou et al [ 27 ] proposed a lightweight detection algorithm for IoT devices based on chaos prediction and chaos theory–Chaos Algorithm (CA) which is used for the identification of Flooding and Distributed Denial of Service (DDoS) attacks to provide a secure network environment for IoT-based smart home systems. Yang et al [ 28 ] proposed an IoT-based smart home security monitoring system that can enhance the security performance of system by improving the False Positive Rate (FPR) and reducing the network latency over the traditional security system.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation for Edge Computing without Using Cloud Center in Smart Home Environment: A Pricing Approach

Liu

Sun

2020

Sensors

View full text Add to dashboard Cite

Recently, more and more smart homes have become one of important parts of home infrastructure. However, most of the smart home applications are not interconnected and remain isolated. They use the cloud center as the control platform, which increases the risk of link congestion and data security. Thus, in the future, smart homes based on edge computing without using cloud center become an important research area. In this paper, we assume that all applications in a smart home environment are composed of edge nodes and users. In order to maximize the utility of users, we assume that all users and edge nodes are placed in a market and formulate a pricing resource allocation model with utility maximization. We apply the Lagrangian method to analyze the model, so an edge node (provider in the market) allocates its resources to a user (customer in the market) based on the prices of resources and the utility related to the preference of users. To obtain the optimal resource allocation, we propose a pricing-based resource allocation algorithm by using low-pass filtering scheme and conform that the proposed algorithm can achieve an optimum within reasonable convergence times through some numerical examples.

show abstract

ForChaos: Real Time Application DDoS Detection Using Forecasting and Chaos Theory in Smart Home IoT Network

Cited by 39 publications

References 9 publications

Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning

Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

Resource Allocation for Edge Computing without Using Cloud Center in Smart Home Environment: A Pricing Approach

Contact Info

Product

Resources

About