Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach

Ullah, Farhan; Naeem, Hamad; Jabbar, Sohail; Khalid, Shehzad; Latif, Muhammad Ahsan; Al‐Turjman, Fadi; Mostarda, Leonardo

doi:10.1109/access.2019.2937347

Cited by 168 publications

(72 citation statements)

References 38 publications

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“…Ullah et al [ 104 ] proposed a tensor-flow-based Deep neural network approach to detect software piracy and other malware-based attacks in the industrial IoT network. This DNN is used for capturing pirated software from the source code of different programmers from google code jam followed by an application of CNN to detect footprints via binary visualization on colored images of malware files.…”

Section: Learning-based Solutions For Securing Iotmentioning

confidence: 99%

Internet of Things: Evolution, Concerns and Security Challenges

Malhotra

Singh

Anand

et al. 2021

Sensors

133

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The escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives. The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection. This automation and continuous explosion of personal and professional information to the digital world provides a potent ground to the adversaries to perform numerous cyber-attacks, thus making security in IoT a sizeable concern. Hence, timely detection and prevention of such threats are pre-requisites to prevent serious consequences. The survey conducted provides a brief insight into the technology with prime attention towards the various attacks and anomalies and their detection based on the intelligent intrusion detection system (IDS). The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Additionally, a case study of healthcare in IoT is presented. The study depicts the architecture, security, and privacy issues and application of learning paradigms in this sector. The research assessment is finally concluded by listing the results derived from the literature. Additionally, the paper discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.

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Section: Learning-based Solutions For Securing Iotmentioning

confidence: 99%

Internet of Things: Evolution, Concerns and Security Challenges

Malhotra

Singh

Anand

et al. 2021

Sensors

133

View full text Add to dashboard Cite

show abstract

“…Security challenges and threats in industrial IoT networks call for innovative applications of ML/DL techniques for IoT security. More specifically, these techniques can be employed for authentication and access control, anomaly and intrusion detection, malware analysis and distributed denial-of-service (DDoS) attacks detection and mitigation [24], [25]. The main challenges of implementing ML/DL models at the edge are scalability issues and IoT edge platforms resource limitations [13].…”

Section: B Machine Learning For Anomaly Detection At the Edgementioning

confidence: 99%

Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

Savić

Lukić

Danilovic³

et al. 2021

IEEE Access

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The number of connected Internet of Things (IoT) devices within cyber-physical infrastructure systems grows at an increasing rate. This poses significant device management and security challenges to current IoT networks. Among several approaches to cope with these challenges, data-based methods rooted in deep learning (DL) are receiving an increased interest. In this paper, motivated by the upcoming surge of 5G IoT connectivity in industrial environments, we propose to integrate a DL-based anomaly detection (AD) as a service into the 3GPP mobile cellular IoT architecture. The proposed architecture embeds autoencoder based anomaly detection modules both at the IoT devices (ADM-EDGE) and in the mobile core network (ADM-FOG), thereby balancing between the system responsiveness and accuracy. We design, integrate, demonstrate and evaluate a testbed that implements the above service in a real-world deployment integrated within the 3GPP Narrow-Band IoT (NB-IoT) mobile operator network.

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“…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%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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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.

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Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach

Cited by 168 publications

References 38 publications

Internet of Things: Evolution, Concerns and Security Challenges

Internet of Things: Evolution, Concerns and Security Challenges

Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

Current Research Trends in IoT Security: A Systematic Mapping Study

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