Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain

Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks performed to probe and identify potential ML-trained models’ vulnerabilities, and poisoning attacks, performed to obtain skewed models whose behavior could be driven when specific inputs are submitted, represent a severe and open issue to face in order to assure security and reliability to critical domains and systems that rely on ML-based or other AI solutions, such as healthcare and justice, for example. In this study, we aimed to perform a comprehensive analysis of the sensitivity of Artificial Intelligence approaches to corrupted data in order to evaluate their reliability and resilience. These systems need to be able to understand what is wrong, figure out how to overcome the resulting problems, and then leverage what they have learned to overcome those challenges and improve their robustness. The main research goal pursued was the evaluation of the sensitivity and responsiveness of Artificial Intelligence algorithms to poisoned signals by comparing several models solicited with both trusted and corrupted data. A case study from the healthcare domain was provided to support the pursued analyses. The results achieved with the experimental campaign were evaluated in terms of accuracy, specificity, sensitivity, F1-score, and ROC area

SafeWeb: A Middleware for Securing Ruby-Based Web Applications

Web applications in many domains such as healthcare and finance must process sensitive data, while complying with legal policies regarding the release of different classes of data to different parties. Currently, software bugs may lead to irreversible disclosure of confidential data in multi-tier web applications. An open challenge is how developers can guarantee these web applications only ever release sensitive data to authorised users without costly, recurring security audits. Our solution is to provide a trusted middleware that acts as a “safety net” to event-based enterprise web applications by preventing harmful data disclosure before it happens. We describe the design and implementation of SafeWeb, a Ruby-based middleware that associates data with security labels and transparently tracks their propagation at different granularities across a multi-tier web architecture with storage and complex event processing. For efficiency, maintainability and ease-of-use, SafeWeb exploits the dynamic features of the Ruby programming language to achieve label propagation and data flow enforcement. We evaluate SafeWeb by reporting our experience of implementing a web-based cancer treatment application and deploying it as part of the UK National Health Service (NHS)

False image injection prevention using iChain

The advances in information and communication technology are consistently beneficial for the healthcare sector. A trend in the healthcare sector is the progressive shift in how data are acquired and the storage of such data in different facilities, such as in the cloud, due to the efficiency and effectiveness offered. Digital images related to healthcare are sensitive in nature and require maximum security and privacy. A malicious entity can tamper with such stored digital images to mislead healthcare personnel and the consequences of wrong diagnosis are harmful for both parties. A new type of cyber attack, a false image injection attack (FIIA) is introduced in this paper. Existing image tampering detection measures are unable to guarantee tamper-proof medical data in real time. Inspired by the effectiveness of emerging blockchain technology, a security framework, image chain (iChain) is proposed in this paper to ensure the security and privacy of the sensitive healthcare images. The practical challenges associated with the proposed framework and further research that is required are also highlighted

BANZKP: a Secure Authentication Scheme Using Zero Knowledge Proof for WBANs

-Wireless body area network(WBAN) has shown great potential in improving healthcare quality not only for patients but also for medical staff. However, security and privacy are still an important issue in WBANs especially in multi-hop architectures. In this paper, we propose and present the design and the evaluation of a secure lightweight and energy efficient authentication scheme BANZKP based on an efficient cryptographic protocol, Zero Knowledge Proof (ZKP) and a commitment scheme. ZKP is used to confirm the identify of the sensor nodes, with small computational requirement, which is favorable for body sensors given their limited resources, while the commitment scheme is used to deal with replay attacks and hence the injection attacks by committing a message and revealing the key later. Our scheme reduces the memory requirement by 56.13 % compared to TinyZKP [13], the comparable alternative so far for Body Area Networks, and uses 10 % less energy

False data injection attack (FDIA): An overview and new metrics for fair evaluation of its countermeasure

The concept of false data injection attack (FDIA) was introduced originally in the smart grid domain. While the term sounds common, it specifically means the case when an attacker compromises sensor readings in such tricky way that undetected errors are introduced into calculations of state variables and values. Due to the rapid growth of the Internet and associated complex adaptive systems, cyber attackers are interested in exploiting similar attacks in other application domains such as healthcare, finance, defense, governance, etc. In today’s increasingly perilous cyber world of complex adaptive systems, FDIA has become one of the top-priority issues to deal with. It is a necessity today for greater awareness and better mechanism to counter such attack in the cyberspace. Hence, this work presents an overview of the attack, identifies the impact of FDIA in critical domains, and talks about the countermeasures. A taxonomy of the existing countermeasures to defend against FDIA is provided. Unlike other works, we propose some evaluation metrics for FDIA detection and also highlight the scarcity of benchmark datasets to validate the performance of FDIA detection techniques. [Figure not available: see fulltext.] © 2020, The Author(s)