Standards, Security and Business Models: Key Challenges for the IoT Scenario

Bujari, Armir; Furini, Marco; Mandreoli, Federica; Martoglia, Riccardo; Montangero, Manuela; Ronzani, Daniele

doi:10.1007/s11036-017-0835-8

Cited by 81 publications

(28 citation statements)

References 32 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whilst this may increase profit margins and decrease the reaction time to market changes, the lack of segmentation allows for transversal network attacks. The third one is due to the number of off-the-shelf equipment being set up in the network [8]. By including off-the-shelf hardware, the attack surface increases, and introduce other potentially weak links (i.e.…”

mentioning

confidence: 99%

Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning

et al. 2019

View full text Add to dashboard Cite

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work focuses on notifying the operator when an anomaly occurs with a probability of the event occurring. This additional information helps in accelerating the mitigation process. The model is trained and tested using a real-world dataset.

show abstract

mentioning

confidence: 99%

Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Similarly, IoT Operating Systems (OS) are detailed and compared [24,30]. Bujari [26] considers current challenges including interoperability, security, privacy, and business models. Security challenges are studied [124,44,7] and Alaba [7] surveys existing security solutions.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things: Evolution and technologies from a security perspective

Ande

Adebisi

Hammoudeh

et al. 2020

Sustainable Cities and Society

114

View full text Add to dashboard Cite

In recent years, IoT has developed into many areas of life including smart homes, smart cities, agriculture, offices, and workplaces. Everyday physical items such as lights, locks and industrial machineries can now be part of the IoT ecosystem. IoT has redefined the management of critical and non-critical systems with the aim of making our lives more safe, efficient and comfortable. As a result, IoT technology is having a huge positive impact on our lives. However, in addition to these positives, IoT systems have also attracted negative attention from malicious users who aim to infiltrate weaknesses within IoT systems for their own gain, referred to as cyber security attacks. By creating an introduction to IoT, this paper seeks to highlight IoT cyber security vulnerabilities and mitigation techniques to the reader. The paper is suitable for developers, practitioners, and academics, particularly from fields such as computer networking, information or communication technology or electronics. The paper begins by introducing IoT as the culmination of two hundred years of evolution within communication technologies. Around 2014, IoT reached consumers, early products were mostly small closed IoT networks, followed by large networks such as smart cities, and continuing to evolve into Next Generation Internet; internet systems which incorporate human values. Following this evolutionary introduction, IoT architectures are compared and some of the technologies that are part of each architectural layer are introduced. Security threats within each architectural layer and some mitigation strategies are discussed, finally, the paper concludes with some future developments.

show abstract

“…The growing popularity of the IoT devices with significant computational power, the ubiquitous access to Internet connectivity and a huge quantity of low-cost sensors opens the door to a wide range of new applications [ 41 ]. In this perspective, it is therefore possible measuring the real impact of noise pollution through low-cost devices and microphones introducing a cheap, but powerful Wireless sensor network (WSN) platform that is readily available and widely deployed.…”

Section: Related Workmentioning

confidence: 99%

RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

Monti

Vincenzi

Mirri

et al. 2020

Sensors

View full text Add to dashboard Cite

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time–space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring.

show abstract

Standards, Security and Business Models: Key Challenges for the IoT Scenario

Cited by 81 publications

References 32 publications

Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning

Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning

Internet of Things: Evolution and technologies from a security perspective

RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

Contact Info

Product

Resources

About