Near real-time security system applied to SDN environments in IoT networks using convolutional neural network

Assis, Marcos V. O. de; Carvalho, Luiz F.; Rodrigues, Joel J. P. C.; Lloret, Jaime; Proença, Mário Lemes

doi:10.1016/j.compeleceng.2020.106738

Cited by 87 publications

(42 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…ere is increasing interest in research to improve security by introducing ML or deep learning to detect DDoS (distributed denial-of-service) attacks, malicious code, abnormal behavior, and abnormal energy consumption for IoT devices [60][61][62][63][64][65][66].…”

Section: Keyword Clustering and Evolution Of Research On Iotmentioning

confidence: 99%

“…When a network attack occurs in an SDN, ML can be introduced as a detection technology to dynamically control and route the communication flow. Recently, studies using ML to detect and automatically respond to DDoS attacks, abnormal patterns, and data leaks against IoT networks and devices have increased [60,[189][190][191][192][193][194][195][196][197][198][199].…”

Section: Identification Of Topics In Iot Securitymentioning

confidence: 99%

See 1 more Smart Citation

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

View full text Add to dashboard Cite

The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.

show abstract

Section: Keyword Clustering and Evolution Of Research On Iotmentioning

confidence: 99%

Section: Identification Of Topics In Iot Securitymentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…An increase in cyber-crimes has made the detection of intrusions in the network a vital research area [1]. Traditionally, personal computers (PC) and computer networks have been the subject of cyber-attack, but recently, cyber-physical systems [2], Internet of Things (IoT) [3], internet of medical things (IoMT) [4], Internet of Connected Vehicles [5], smart factories [6], and 5G communication infrastructures [7] have become targets of numerous attacks as well. Several studies have been carried out to suggest remarkable strategies to fight against cyber actions [8,9].…”

Section: Introductionmentioning

confidence: 99%

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

et al. 2021

View full text Add to dashboard Cite

The need for timely identification of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) has become critical in minimizing security risks as the number of IoT devices deployed rapidly grows globally and the volume of such attacks rises to unprecedented levels. Instant detection facilitates network security by speeding up warning and disconnection from the network of infected IoT devices, thereby preventing the botnet from propagating and thereby stopping additional attacks. Several methods have been developed for detecting botnet attacks, such as Swarm Intelligence (SI) and Evolutionary Computing (EC)-based algorithms. In this study, we propose a Local-Global best Bat Algorithm for Neural Networks (LGBA-NN) to select both feature subsets and hyperparameters for efficient detection of botnet attacks, inferred from 9 commercial IoT devices infected by two botnets: Gafgyt and Mirai. The proposed Bat Algorithm (BA) adopted the local-global best-based inertia weight to update the bat’s velocity in the swarm. To tackle with swarm diversity of BA, we proposed Gaussian distribution used in the population initialization. Furthermore, the local search mechanism was followed by the Gaussian density function and local-global best function to achieve better exploration during each generation. Enhanced BA was further employed for neural network hyperparameter tuning and weight optimization to classify ten different botnet attacks with an additional one benign target class. The proposed LGBA-NN algorithm was tested on an N-BaIoT data set with extensive real traffic data with benign and malicious target classes. The performance of LGBA-NN was compared with several recent advanced approaches such as weight optimization using Particle Swarm Optimization (PSO-NN) and BA-NN. The experimental results revealed the superiority of LGBA-NN with 90% accuracy over other variants, i.e., BA-NN (85.5% accuracy) and PSO-NN (85.2% accuracy) in multi-class botnet attack detection.

show abstract

“…Therefore, the IoT network faces many issues, which capture the interest of researchers to improve its efficiency. The last few decades have been quite active in IoT research, which resulted in a huge amount of proposals for various routing protocols [5], [6], security models [7], [8] and clustering techniques [9] that provide secure and trustful communication in the IoT networks. However, IoT networks are always threatened to be compromised by the external nodes, which mislead the networks by sending false data for their benefit.…”

Section: Introductionmentioning

confidence: 99%

Securing Genetic Algorithm Enabled SDN Routing for Blockchain Based Internet of Things

Abbas¹,

Javaid

Almogren

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Internet of Things (IoT) is an emerging domain in which different devices communicate with each other through minimum human intervention. IoT devices are usually operated in hostile and unattended environments. Moreover, routing in current IoT architecture becomes inefficient due to malicious and unauthenticated nodes' existence, minimum network lifetime, insecure routing, etc. This paper proposes a lightweight blockchain based authentication mechanism where ordinary sensors' credentials are stored. As IoT nodes have a short lifespan due to energy depletion, few credentials are stored in the blockchain to achieve lightweight authentication. Moreover, the route calculation is performed by a genetic algorithm enabled software defined network controller, which is also used for on-demand routing to optimize the energy consumption of the nodes in the IoT network. Furthermore, a route correctness mechanism is proposed to check the existence of malicious nodes in the calculated route. Moreover, a detection mechanism is proposed to restrict the malicious nodes' activities, while a malicious node's list is maintained in the blockchain, which is used in the route correctness mechanism. The proposed model is evaluated by performing intensive simulations. The effectiveness of the proposed model is depicted in terms of gas consumption, which shows the optimized utilization of resources. The residual energy of the network shows optimized route calculation, while the malicious node detection method shows the number of packets dropped.

show abstract

Near real-time security system applied to SDN environments in IoT networks using convolutional neural network

Cited by 87 publications

References 28 publications

Current Research Trends in IoT Security: A Systematic Mapping Study

Current Research Trends in IoT Security: A Systematic Mapping Study

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

Securing Genetic Algorithm Enabled SDN Routing for Blockchain Based Internet of Things

Contact Info

Product

Resources

About