Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy

Multi-object segmentation using coupled nonparametric shape and relative pose priors

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes

Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial intelligence (AI) models are typically ignored by the scientific community so far. However, security is also a vital part of AI algorithms because attackers can poison the AI model itself. This paper proposes a mitigation method for adversarial attacks against proposed 6G ML models for the millimeter-wave (mmWave) beam prediction using adversarial training. The main idea behind generating adversarial attacks against ML models is to produce faulty results by manipulating trained DL models for 6G applications for mmWave beam prediction. We also present a proposed adversarial learning mitigation method’s performance for 6G security in mmWave beam prediction application a fast gradient sign method attack. The results show that the defended model under attack’s mean square errors (i.e., the prediction accuracy) are very close to the undefended model without attack

Towards robust autonomous driving systems through adversarial test set generation

Correct environmental perception of objects on the road is vital for the safety of autonomous driving. Making appropriate decisions by the autonomous driving algorithm could be hindered by data perturbations and more recently, by adversarial attacks. We propose an adversarial test input generation approach based on uncertainty to make the machine learning (ML) model more robust against data perturbations and adversarial attacks. Adversarial attacks and uncertain inputs can affect the ML model’s performance, which can have severe consequences such as the misclassification of objects on the road by autonomous vehicles, leading to incorrect decision-making. We show that we can obtain more robust ML models for autonomous driving by making a dataset that includes highly-uncertain adversarial test inputs during the re-training phase. We demonstrate an improvement in the accuracy of the robust model by more than 12%, with a notable drop in the uncertainty of the decisions returned by the model. We believe our approach will assist in further developing risk-aware autonomous systems.acceptedVersio

Policy specification and verification for blockchain and smart contracts in 5G networks

open access articleBlockchain offers unprecedented opportunities for innovation in financial transactions. A whole new world of opportunities for banking, lending, insurance, money transfer, investments, and stock markets awaits. However, the potential for wide-scale adoption of blockchain is hindered with cybersecurity and privacy issues. We provide an overview of the risks and security requirements and give an outlook for future research that could be helpful in solving some of the challenges. We also present an approach for policy specification and verification of financial transactions based on smart contracts

Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach

Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit nonstationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh–ninetieth-day forecast. For daily price forecast, the error percentage is as low as 1.44%, while it varies from 2.88 to 4.10% for horizons of seven to ninety days. These results indicate that the presented models outperform the existing models in the literature

Detection of Botnet Attacks against Industrial IoT Systems by Multilayer Deep Learning Approaches

Industry 4.0 is the next revolution in manufacturing technology that is going to change the production and distribution of goods and services within the following decade. Powered by different enabling technologies that are also being developed simultaneously, it has the potential to create radical changes in our societies such as by giving rise to highly-integrated smart cities. The Industrial Internet of Things (IIoT) is one of the main areas of development for Industry 4.0. These IIoT devices are used in mission-critical sectors such as the manufacturing industry, power generation, and healthcare management. However, smart factories and cities can only function when threats to cyber security, data privacy, and information integrity are properly managed. In this regard, securing IIoT devices and their networks is vital to preserving data and privacy. The use of artificial intelligence is an enabler for more secure IIoT systems. In this study, we propose high-performing deep learning models for the classification of botnet attacks that commonly affect IIoT devices and networks. Evaluation of results shows that deep learning models such as the artificial neural network (ANN), the long short-term memory (LSTM), and the gated recurrent unit (GRU) can successfully be used for classifications of IIoT malware attacks with an accuracy of up to 99%

AI-powered malware detection with Differential Privacy for zero trust security in Internet of Things networks

The widespread usage of Android-powered devices in the Internet of Things (IoT) makes them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks, such as smart watches, smart thermometers, biosensors, and more, are powered by the Android operating system, where preserving the privacy of user-sensitive data is of utmost importance. Detecting Android malware is thus vital for protecting sensitive information and ensuring the reliability of IoT networks. This article focuses on AI-enabled Android malware detection for improving zero trust security in IoT networks, which requires Android applications to be verified and authenticated before providing access to network resources. The zero trust security model requires strict identity verification for every entity trying to access resources on a private network, regardless of whether they are inside or outside the network perimeter. Our proposed solution, DP-RFECV-FNN, an innovative approach to Android malware detection that employs Differential Privacy (DP) within a Feedforward Neural Network (FNN) designed for IoT networks under the zero trust model. By integrating DP, we ensure the confidentiality of data during the detection process, setting a new standard for privacy in cybersecurity solutions. By combining the strengths of DP and zero trust security with the powerful learning capacity of the FNN, DP-RFECV-FNN demonstrates the ability to identify both known and novel malware types and achieves higher accuracy while maintaining strict privacy controls compared with recent papers. DP-RFECV-FNN achieves an accuracy ranging from 97.78% to 99.21% while utilizing static features and 93.49% to 94.36% for dynamic features of Android applications to detect whether it is malware or benign. These results are achieved under varying privacy budgets, ranging from ϵ=0.1 to ϵ=1.0. Furthermore, our proposed feature selection pipeline enables us to outperform the state-of-the-art by significantly reducing the number of selected features and training time while improving accuracy. To the best of our knowledge, this is the first work to categorize Android malware based on both static and dynamic features through a privacy-preserving neural network model

A secure and efficient Internet of Things cloud encryption scheme with forensics investigation compatibility based on identity-based encryption

Data security is a challenge for end-users of cloud services as the users have no control over their data once it is transmitted to the cloud. A potentially corrupt cloud service provider can obtain the end-users’ data. Conventional PKI-based solutions are insufficient for large-scale cloud systems, considering efficiency, scalability, and security. In large-scale cloud systems, the key management requirements include scalable encryption, authentication, and non-repudiation services, as well as the ability to share files with different users and data recovery when the user keys of encrypted data are not accessible. Further requirements in cloud systems include the ability to provide the means for digital forensic investigations on encrypted data. Once data on the cloud is encrypted with a user's key it becomes impossible to access by forensic investigation teams. In this regard, distributing the trust of key management into multiple authorities is desirable. In the literature, there is no available secure cloud storage system with secure and efficient Type-3 pairings, supporting Encryption-as-a-Service (EaaS) and multiple Public Key Generators (PKGs). This paper proposes an efficient Identity-based cryptography (IBC) architecture for secure cloud storage, named Secure Cloud Storage System (SCSS), which supports distributed key management and encryption mechanisms and support for multiple PKGs. During forensic investigations, the legal authorities will be able to use the multiple PKG mechanism for data access, while an account locking mechanism prevents a single authority to access user data due to trust distribution. We also demonstrate that, the IBC scheme used in SCSS has better performance compared to similar schemes in the literature. For the security levels of 128-bits and above, SCSS has better scalability compared to existing schemes, with respect to encryption and decryption operations. Since the decryption operation is frequently needed for forensic analysis, the improved scalability results in a streamlined forensic investigation process on the encrypted data in the cloud

Lightweight KPABE Architecture Enabled in Mesh Networked Resource-Constrained IoT Devices

Internet of Things (IoT) environments are widely employed in industrial applications including intelligent transportation systems, healthcare systems, and building energy management systems. For such environments of highly sensitive data, adapting scalable and flexible communication with efficient security is vital. Research investigated wireless Ad-hoc/mesh networking, while Attribute Based Encryption (ABE) schemes have been highly recommended for IoT. However, a combined implementation of both mesh networking and Key-Policy Attribute Based Encryption (KPABE) on resource-constrained devices has been rarely addressed. Hence, in this work, an integrated system that deploys a lightweight KPABE security built on wireless mesh networking is proposed. Implementation results show that the proposed system ensures flexibility and scalability of self-forming and cooperative mesh networking in addition to a fine-grained security access structure for IoT nodes. Moreover, the work introduces a case study of an enabled scenario at a school building for optimizing energy efficiency, in which the proposed integrated system architecture is deployed on IoT sensing and actuating devices. Therefore, the encryption attributes and access policy are well-defined, and can be adopted in relevant IoT applications. 2013 IEEE.This publication was made possible by the National Priority Research Program (NPRP) grant [NPRP10-1203-160008] from the Qatar National Research Fund (a member of Qatar Foundation) and the co-funding by the IBERDROLA QSTP LLC. The publication of this article was funded by the Qatar National Library. The findings achieved herein are solely the responsibility of the authors.Scopus2-s2.0-8509909047