Industrial IoT security threats and concerns by considering Cisco and Microsoft IoT reference models

Bakhshi, Zeinab; Balador, Ali; Mustafa, Jawad

doi:10.1109/wcncw.2018.8368997

Cited by 47 publications

(25 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Researchers have used the Cisco reference model in evaluating IoT security issues. In 2018, Bakhshi et al surveyed numerous publications referencing differing reference models and focused mainly on data ingestion and abstraction [15]. Peter Aufner reviews various threat frameworks in light of the Cisco model and concludes that gaps exist between the frameworks, IoT and security research [16].…”

Section: The Cisco Seven-layer Iot Reference Modelmentioning

confidence: 99%

IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework

Wheelus

Zhu

2020

IoT

View full text Add to dashboard Cite

The recent surge in Internet of Things (IoT) deployment has increased the pace of integration and extended the reach of the Internet from computers, tablets and phones to a myriad of devices in our physical world. Driven by the IoT, with each passing day, the Internet becomes more integrated with everyday life. While IoT devices provide endless new capabilities and make life more convenient, they also vastly increase the opportunity for nefarious individuals, criminal organizations and even state actors to spy on, and interfere with, unsuspecting users of IoT systems. As this looming crisis continues to grow, calls for data science approaches to address these problems have increased, and current research shows that predictive models trained with machine learning algorithms hold great potential to mitigate some of these issues. In this paper, we first carry out an analytics approach to review security risks associated with IoT systems, and then propose a machine learning-based solution to characterize and detect IoT attacks. We use a real-world IoT system with secured gate access as a platform, and introduce the IoT system in detail, including features to capture security threats/attacks to the system. By using data collected from a nine month period as our testbed, we evaluate the efficacy of predictive models trained by means of machine learning, and propose design principles and a loose framework for implementing secure IoT systems.

show abstract

Section: The Cisco Seven-layer Iot Reference Modelmentioning

confidence: 99%

IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework

Wheelus

Zhu

2020

IoT

View full text Add to dashboard Cite

show abstract

“…From (5) and (8), the time complexity between TCN residual block and 1D convolutional structure is roughly comparable. Considering the input data are determinate after pre-processing and embedding space construction, the number of convolutional filters and the length of filter kernels is the main variable parameters in convolutional structure for time consumption.…”

Section: Time Complexitymentioning

confidence: 95%

“…As one of the most energetic technology companies, Microsoft has paid great enthusiasm into the IoT field, and Windows-based applications have been well-developed via their Azure IoT platform services [8]. Focused on the Windows-based malware invasion problem on the IoT platform, this article proposes a malware categorization scheme for attributing malware into different families through a Word2Vec-based temporal convolutional network (TCN).…”

Section: Introductionmentioning

confidence: 99%

Categorizing Malware via A Word2Vec-based Temporal Convolutional Network Scheme

et al. 2020

View full text Add to dashboard Cite

As edge computing paradigm achieves great popularity in recent years, there remain some technical challenges that must be addressed to guarantee smart device security in Internet of Things (IoT) environment. Generally, smart devices transmit individual data across the IoT for various purposes nowadays, and it will cause losses and impose a huge threat to users since malware may steal and damage these data. To improve malware detection performance on IoT smart devices, we conduct a malware categorization analysis based on the Kaggle competition of Microsoft Malware Classification Challenge (BIG 2015) dataset in this article. Practically speaking, motivated by temporal convolutional network (TCN) structure, we propose a malware categorization scheme mainly using Word2Vec pre-trained model. Considering that the popular one-hot encoding converts input names from malicious files to high-dimensional vectors since each name is represented as one dimension in one-hot vector space, more compact vectors with fewer dimensions are obtained through the use of Word2Vec pre-training strategy, and then it can lead to fewer parameters and stronger malware feature representation. Moreover, compared with long short-term memory (LSTM), TCN demonstrates better performance with longer effective memory and faster training speed in sequence modeling tasks. The experimental comparisons on this malware dataset reveal better categorization performance with less memory usage and training time. Especially, through the performance comparison between our scheme and the state-of-the-art Word2Vec-based LSTM approach, our scheme shows approximately 1.3% higher predicted accuracy than the latter on this malware categorization task. Additionally, it also demonstrates that our scheme reduces about 90 thousand parameters and more than 1 hour on the model training time in this comparison.

show abstract

“…Internet of things are system of interconnected computing devices and embedded devices using internet. The embedded devices connected with smart interfaces for their communication [1].The applications of IoT are smart homes, environment monitoring, health care systems, energy management, building automation and transportation [1].Internet of things (IOT) are used for Machine to Machine (M2M) communication in the industries or factories. This revolution in Industrial sector is Industry4.0.…”

mentioning

confidence: 99%

Security Threats and Concerns, Firmware Vulnerability Analysis in Industrial Internet of Things

2020

IJETER

View full text Add to dashboard Cite

This paper includes Internet of Things(IOT), Industry 4.0, Industrial Internet of Things (IIOT).Industrial IOT applications and sectors which are used them. The basic architecture of Industrial IOT. The protocols are used in these wireless sensor networks using machine to machine communication. The communication or data transfer among these industrial devices using Industrial Internet. This paper characterize security concerns and threats in Industrial IOT. This paper also presents identification of vulnerabilities, backdoors and password leaks or weak passwords are in the IOT devices. These IOT devices are used in the smart factories or industries which are used for efficient Human to machine communication. This paper represents firmware vulnerability analysis. This analysis includes the firmware unpacking and reverse engineering. This analysis using the specified tools. This paper includes possible attack surfaces are based on vulnerabilities of the Industrial Internet of Things with in the security standards. The security standard is the IOT attack surface area of OWAPS Internet of things project.

show abstract

Industrial IoT security threats and concerns by considering Cisco and Microsoft IoT reference models

Cited by 47 publications

References 26 publications

IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework

IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework

Categorizing Malware via A Word2Vec-based Temporal Convolutional Network Scheme

Security Threats and Concerns, Firmware Vulnerability Analysis in Industrial Internet of Things

Contact Info

Product

Resources

About