Cyber and physical threats to the internet of everything

After over 40 years of the Internet faithfully serving the needs of the Earth’s human population for information, communication, and entertainment, we have now entered the era of the IoT. Of course, when we refer to the Internet, we also mean the Web and therefore the Web of Things (WoT), where distributed applications benefitting from networking through the Internet are no longer a privilege of humans. Things can also take full advantage of the capabilities, simplicity, and potential of Web technologies and protocols. Following current developments in this field, it is not difficult to see the inevitability of the convergence of the two worlds, of humans and of things, each using the Internet as a primary means of communication. Possibly the most appropriate term to describe this evolution has been proposed by Cisco: the Internet of Everything (IoE), which "brings together people, process, data, and things to make networked connections more relevant and valuable than ever before." In the IoE era, machines are equal to humans as Internet users. In an ecosystem in which everything is connected, and where physical and cyber converge and collaborate, the threats of the two worlds not only coexist, but also converge, creating a still largely unknown environment, in which an attack in cyberspace can propagate and have an adverse effect in physical space and vice versa. So how can we be prepared for and confront this new unknown? How can we study and learn from the ways this has been dealt with in the past? First, it is important to simplify the problem by attempting to identify the components of IoE and the threats and effects an attack can have in each one

Security in the internet of everything era - opening statement

Since Nikola Tesla’s “teleautomation”, it has taken almost 80 years for the general public to experience what culminated into the Internet of Things and another ten to truly accept it. The problem is that in recent years, a vast range of devices and systems were designed to support this new paradigm, but with little regard to security or privacy, despite the profound impact that breaches of either can have to a user’s “real life”

Wear it and share it: Wearables and security

As the amount of data generated by personal devices increases, supported by the trend of making these devices more personal (i.e., wearable, sewable), so too will the risks of personal privacy violation rise. From the technological perspective, it is important to follow privacy-by-design approaches, incorporating both data encryption and data anonymization techniques. From the perspective of enterprises and users, understanding that “wearing means sharing” is a valuable first step

Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers

This paper presents the first initial results of using radar raw I & Q data and range profiles combined with Long Short Term Memory layers to classify human activities. Although tested only on simple classification problems, this is an innovative approach that enables to bypass the conventional usage of Doppler-time patterns (spectrograms) as inputs of the Long Short Term Memory layers, and adopt instead sequences of range profiles or even raw complex data as inputs. A maximum 99.56% accuracy and a mean accuracy of 97.67% was achieved by treating the radar data as these time sequences, in an effective scheme using a deep learning approach that did not require the pre-processing of the radar data to generate spectrograms and treat them as images. The prediction time needed for a given input testing sample is also reported, showing a promising path for real-time implementation once the Long Short Term Memory layers network is properly trained

Animal lameness detection with radar sensing

Lameness is a significant problem for performance horses and farmed animals, with severe impact on animal welfare and treatment costs. Lameness is commonly diagnosed through subjective scoring methods performed by trained veterinary clinicians, but automatic methods using suitable sensors would improve efficiency and reliability. In this paper, we propose the use of radar micro-Doppler signatures for contactless and automatic identification of lameness, and present preliminary results for dairy cows, sheep, and horses. These proof-of-concept results are promising, with classification accuracy above 85% for dairy cows, around 92% for horses, and close to 99% for sheep

Digital deception: Cyber fraud and online misinformation

PHISHING, USER ACCOUNT takeovers, and other computing-related threats have made it easy for criminals to deceive people for financial and other gain. It is now considered standard practice for an advanced cyberattack, even a highly technical one, to start in a nontechnical manner: a spearphishing email deceiving an organization’s employees into providing their credentials, a watering hole website infecting their computer, and so on. It is the human that is the initial target, as well as the first line of defense. At the same time, social media has become a dominant, direct, and highly effective form of news generation and sharing at a global scale, in a manner that influences and enhances, but also challenges and often antagonizes, traditional media corporations

GANs and alternative methods of synthetic noise generation for domain adaption of defect classification of Non-destructive ultrasonic testing

This work provides a solution to the challenge of small amounts of training data in Non-Destructive Ultrasonic Testing for composite components. It was demonstrated that direct simulation alone is ineffective at producing training data that was representative of the experimental domain due to poor noise reconstruction. Therefore, four unique synthetic data generation methods were proposed which use semi-analytical simulated data as a foundation. Each method was evaluated on its classification performance of real experimental images when trained on a Convolutional Neural Network which underwent hyperparameter optimization using a genetic algorithm. The first method introduced task specific modifications to CycleGAN, to learn the mapping from physics-based simulations of defect indications to experimental indications in resulting ultrasound images. The second method was based on combining real experimental defect free images with simulated defect responses. The final two methods fully simulated the noise responses at an image and signal level respectively. The purely simulated data produced a mean classification F1 score of 0.394. However, when trained on the new synthetic datasets, a significant improvement in classification performance on experimental data was realized, with mean classification F1 scores of 0.843, 0.688, 0.629, and 0.738 for the respective approaches.Comment: 16 Page

A prototype deep learning paraphrase identification service for discovering information cascades in social networks

Identifying the provenance of information posted on social media and how this information may have changed over time can be very helpful in assessing its trustworthiness. Here, we introduce a novel mechanism for discovering “post-based” information cascades, including the earliest relevant post and how its information has evolved over subsequent posts. Our prototype leverages multiple innovations in the combination of dynamic data sub-sampling and multiple natural language processing and analysis techniques, benefiting from deep learning architectures. We evaluate its performance on EMTD, a dataset that we have generated from our private experimental instance of the decentralised social network Mastodon, as well as the benchmark Microsoft Research Paraphrase Corpus, reporting no errors in sub-sampling based on clustering, and an average accuracy of 92% and F1 score of 93% for paraphrase identification

A comparison of methods for generating synthetic training data for domain adaption of deep learning models in ultrasonic non-destructive evaluation

This work provides a solution to the challenge of small amounts of training data in Non-Destructive Ultrasonic Testing for composite components. It was demonstrated that direct simulation alone is ineffective at producing training data that was representative of the experimental domain due to poor noise reconstruction. Therefore, four unique synthetic data generation methods were proposed which use semi-analytical simulated data as a foundation. Each method was evaluated for its performance in the classification of real experimental images when trained on a Convolutional Neural Network which underwent hyperparameter optimization using a genetic algorithm. The first method introduced task specific modifications to CycleGAN, a generative network for image-to-image translation, to learn the mapping from physics-based simulations of defect indications to experimental indications in resulting ultrasound images. The second method was based on combining real experimental defect free images with simulated defect responses. The final two methods fully simulated the noise responses at an image and signal level respectively. The purely simulated data produced a mean classification F1 score of 0.394. However, when trained on the new synthetic datasets, a significant improvement in classification performance on experimental data was realized, with mean classification F1 scores of 0.843, 0.688, 0.629, and 0.738 for the respective approaches