Access Control Policies (ACP) specify which principals such as users have access to which resources. Ensuring the correctness and consistency of ACPs is crucial to prevent security vulnerabilities. However, in practice, ACPs are commonly written in Natural Language (NL) and buried in large documents such as requirements documents, not amenable for automated techniques to check for correctness and consistency. It is tedious to manually extract ACPs from these NL documents and validate NL functional requirements such as use cases against ACPs for detecting inconsistencies. To address these issues, we propose an approach, called Text2Policy, to automatically extract ACPs from NL software documents and resource-access information from NL scenario-based functional requirements. We conducted three evaluations on the collected ACP sentences from publicly available sources along with use cases from both open source and proprietary projects. The results show that Text2Policy effectively identifies ACP sentences with the precision of 88.7% and the recall of 89.4%, extracts ACP rules with the accuracy of 86.3%, and extracts action steps with the accuracy of 81.9%.

/pdf/automated-extraction-of-security-policies-from-natural-11rpk94jit.pdf

Automated extraction of security policies from natural-language software documents

The growth of data and categories of attacks, demand the use of Intrusion Detection System(IDS) effectively using Machine Learning(ML) and Deep Learning(DL) techniques. Apart from the ML and DL techniques, Swarm and Evolutionary (SWEVO) Algorithms have also shown significant performance to improve the efficiency of the IDS models. This survey covers SWEVO-based IDS approaches such as Genetic Algorithm(GA), Ant Colony Optimization(ACO), Particle Swarm Optimization(PSO), Artificial Bee Colony Optimization(ABC), Firefly Algorithm(FA), Bat Algorithm(BA), and Flower Pollination Algorithm(FPA). The paper also discusses applications of the SWEVO in the field of IDS along with challenges and possible future directions.

Role of swarm and evolutionary algorithms for intrusion detection system: A survey

http://www.synchromedia.ca/fr/system/files/SurveyIRS.pdf

Taxonomy of intrusion risk assessment and response system

Interest and need for Wireless Body Area Networks (WBANs) have significantly increased recently. WBAN consists of miniaturized sensors designed to collect and transmit data through wireless network, enabling medical specialists to monitor patients during their normal daily life and providing real time opinions for medical diagnosis. Many wireless technologies have proved themselves in WBAN applications, while others are still under investigations. The choice of the technology to adopt may depend on the disease to monitor and the performance requirements, i.e. reliability, latency and data rate. In addition, the suitable sensor is essential when seeking to extract the data related to a medical measure. This paper aims at surveying the wireless technologies used in WBAN systems. In addition to a detailed survey on the existing technologies, the use of the emerging Low Power Wide Area Network (LPWAN) technologies, and the future 5G, B5G and 6G is investigated, where the suitability of these technologies to WBAN applications is studied from several perspectives. Furthermore, medical applications of WBAN are discussed by presenting their methodologies, the adopted wireless technologies and the used sensors. Given that each medical application requires the appropriate sensor to extract the data, we highlight a wide range of the sensors used in the market for medical systems. Recent and future challenges in WBAN systems are given related to the power consumption, the emergence of the Internet of Things (IoT) technologies in WBAN and others.

/pdf/wireless-technologies-medical-applications-and-future-2wfun5z18e.pdf

Wireless Technologies, Medical Applications, and Future Challenges in WBAN: a Survey

The introduction of sensor technology in our daily lives has brought comfort, convenience, and improved health over the past few decades. Technological advances further expanded the use of medical sensors by reducing their size and costs. Medical sensors improve the intelligence and capabilities of healthcare services including, remote health monitoring, surgical procedures, therapy, and rehabilitation. We present a comprehensive review of medical sensors in the last 50 years focusing on their deployment in healthcare applications. The review also discusses the role of Internet of Things (IoT) technology in enhancing the capabilities of sensor technologies for the healthcare domain. Moreover, we also investigate the benefits and challenges of various integrated architectures which have been proposed recently to seamlessly integrate heterogeneous medical sensors with emerging technologies and paradigms that include edge, Mobile Cloud Computing (MCC), fog, and cloud computing technologies. Finally, we identify future challenges that must be addressed to achieve the maximum potential and benefits of medical sensor technologies and ultimately provide robust, scalable, reliable, and cost-effective healthcare delivery.

Medical Sensors and Their Integration in Wireless Body Area Networks for Pervasive Healthcare Delivery: A Review

SAM is an international conference that serves researchers, scholars, professionals, students, and academicians who are looking to both foster working relationships and gain access to the latest research results. The 2014 Congress was composed of research presentations, keynote lectures, invited presentations, tutorials, panel discussions, and poster presentations. Selected Topics: Security Education, Wireless Networks Security + Modeling of Information Security, Information Assurance, Network Security + Security Management, Biometrics and Forensics I + Cryptographic Technologies, Computer and Hardware Security, IOT and SCADA Cybersecurity Education, Cryptographic Technologies I, Cryptographic Technologies II, Security Management I, Security Management II, Biometrics and Forensics II, Security Applications, IOT and SCADA Cybersecurity Education, Security Systems and Applications.

Security and Management

The rapid uptake of the Wireless Body Area Networks (WBAN) and their services poses unprecedented security requirements. WBAN are evolving to support these requirements. Fulfilling these tasks is challenging as their mobile context is increasingly complex, heterogeneous, and evolving. One potential solution to meet the WBAN security requirements is trust management that helps to reach a more secure and adaptable WBAN environment. Accordingly, this article aims to serve as a brief survey of trust management approaches within intra-WBAN and inter-WBAN. For that, we first summarize trust management concepts, discuss WBAN challenges and classify the attacks on WBAN trust management models. Subsequently, we detail and compare the existing trust based approaches in a WBAN context. We pinpoint their limitations and provide a new classification of these different approaches. We also propose a set of best practices that may help the reader to build a robust and an efficient trust management framework. We complete this survey by highlighting the open future directions and perspectives for research.

https://www.mdpi.com/1424-8220/20/21/6041/pdf

A Survey on Trust Management for WBAN: Investigations and Future Directions.

Vehicular ad hoc networks (VANET) authorize the vehicles to communicate and exchange data among themselves and with the Road-Side Unit (RSU). However, with the increase of the vehicles number, the VANET become vulnerable to many attacks. One serious attack is the Sybil attack which threats the functionalities of VANET by generating a high number of fake identities. In this paper, we present a Multi-Levels Trust Mechanism solution (BMLT-SA) based on the blockchain to detect the Sybil attack. Our approach is divided into three main parts : (1) A Horizontal Trust Management mechanism (HTM) is introduced as vehicle to vehicle (V2V) scheme to detect a malicious vehicle. In this level, each vehicle runs a Local Machine Learning (LML) algorithm to classify their neighbors as normal and malicious ones. All the decisions made by the vehicles are broadcasted to the RSUs; (2) A Vertical Trust Management mechanism (VTM) is used to launch a verification algorithm by the RSU. This algorithm takes as input all the LML results and gives a Vehicular Trust list as output; (3) All the RSUs belonging to the same region are collaborating to form a Distributed Trust Management mechanism (DTM) based on the use of the blockchain to share the Vehicular Trust List and to identify the class of each vehicle crossing the network. Simulations and experiments demonstrate that the proposed model based on the collaboration of different VANET components is an effective method for Sybil attack detection.

Blockchain-based Multi-Levels Trust Mechanism Against Sybil Attacks for Vehicular Networks

During this last decade, in the digital era, online and real-time data management becomes essential and primordial in several scenarios. In the health domain, and especially for remote healthcare monitoring systems, the management of data in real time becomes a requirement. Indeed, care providers need to access the vital information and sometimes the processed data in order to take timely the adequate decision. However, many issues arise in the data processing regarding real-time aspect, computational complexity and patient mobility. Therefore, Federated Learning (FL) engages promising technologies such as the fog and the cloud computing as well as machine learning (ML) and deep learning (DL) to address the aforementioned issues. Fog computing enables data preprocessing in proximity of medical users, exploiting patients mobile phones or personal digital assistant (PDA) or small-scale distributed servers. In this paper, we pinpoint the important role of the FL-based system within Internet of Medical Things (IoMT) to combat COVID-19 pandemic. Specifically, we first introduce the architecture highlighting the fog layer within the smart healthcare system. We then discuss the preprocesing tasks that could be implemented at the fog layer with a particular focus on ML and DL tasks. After that, an investigation related to the FL against several COVID-19 contexts is provided. Finally, this paper explores open issues and future directions regarding the FL potentialities in pandemic situation.

Federated Learning toward Data Preprocessing: COVID-19 Context

In this paper, we investigate the concept of reputation to improve the resistance of vehicular networks against malicious and misbehaving vehicles. We propose a robust reputation management system, which consists of a model for reputation calculation and a credibility model to enhance network efficiency. The reputation score or value reflects the behavior of a vehicle towards other vehicles and network services (selfish, cooperative, malicious, misbehaving, etc.); while the credibility of vehicles is used to determine whether a reputation score given by a vehicle is correct to deal with malicious vehicles that use reputation calculation to spoil the network operation. We first describe how the reputation score of each vehicle is determined. Then, we introduce a non-cooperative game, where each vehicle aims to maximize its credibility. The effectiveness of the proposed model is demonstrated through extensive simulations.

An Efficient Reputation Management Model based on Game Theory for Vehicular Networks

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