430 research outputs found
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G
The next wave of wireless technologies is proliferating in connecting things
among themselves as well as to humans. In the era of the Internet of things
(IoT), billions of sensors, machines, vehicles, drones, and robots will be
connected, making the world around us smarter. The IoT will encompass devices
that must wirelessly communicate a diverse set of data gathered from the
environment for myriad new applications. The ultimate goal is to extract
insights from this data and develop solutions that improve quality of life and
generate new revenue. Providing large-scale, long-lasting, reliable, and near
real-time connectivity is the major challenge in enabling a smart connected
world. This paper provides a comprehensive survey on existing and emerging
communication solutions for serving IoT applications in the context of
cellular, wide-area, as well as non-terrestrial networks. Specifically,
wireless technology enhancements for providing IoT access in fifth-generation
(5G) and beyond cellular networks, and communication networks over the
unlicensed spectrum are presented. Aligned with the main key performance
indicators of 5G and beyond 5G networks, we investigate solutions and standards
that enable energy efficiency, reliability, low latency, and scalability
(connection density) of current and future IoT networks. The solutions include
grant-free access and channel coding for short-packet communications,
non-orthogonal multiple access, and on-device intelligence. Further, a vision
of new paradigm shifts in communication networks in the 2030s is provided, and
the integration of the associated new technologies like artificial
intelligence, non-terrestrial networks, and new spectra is elaborated. Finally,
future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector
Due to the importance of Critical Infrastructure (CI) in a nation's economy,
they have been lucrative targets for cyber attackers. These critical
infrastructures are usually Cyber-Physical Systems (CPS) such as power grids,
water, and sewage treatment facilities, oil and gas pipelines, etc. In recent
times, these systems have suffered from cyber attacks numerous times.
Researchers have been developing cyber security solutions for CIs to avoid
lasting damages. According to standard frameworks, cyber security based on
identification, protection, detection, response, and recovery are at the core
of these research. Detection of an ongoing attack that escapes standard
protection such as firewall, anti-virus, and host/network intrusion detection
has gained importance as such attacks eventually affect the physical dynamics
of the system. Therefore, anomaly detection in physical dynamics proves an
effective means to implement defense-in-depth. PASAD is one example of anomaly
detection in the sensor/actuator data, representing such systems' physical
dynamics. We present EPASAD, which improves the detection technique used in
PASAD to detect these micro-stealthy attacks, as our experiments show that
PASAD's spherical boundary-based detection fails to detect. Our method EPASAD
overcomes this by using Ellipsoid boundaries, thereby tightening the boundaries
in various dimensions, whereas a spherical boundary treats all dimensions
equally. We validate EPASAD using the dataset produced by the TE-process
simulator and the C-town datasets. The results show that EPASAD improves
PASAD's average recall by 5.8% and 9.5% for the two datasets, respectively.Comment: Submitte
Data security in European healthcare information systems
This thesis considers the current requirements for data security in European healthcare systems and
establishments. Information technology is being increasingly used in all areas of healthcare
operation, from administration to direct care delivery, with a resulting dependence upon it by
healthcare staff. Systems routinely store and communicate a wide variety of potentially sensitive
data, much of which may also be critical to patient safety. There is consequently a significant
requirement for protection in many cases.
The thesis presents an assessment of healthcare security requirements at the European level, with a
critical examination of how the issue has been addressed to date in operational systems. It is
recognised that many systems were originally implemented without security needs being properly
addressed, with a consequence that protection is often weak and inconsistent between establishments.
The overall aim of the research has been to determine appropriate means by which security may be
added or enhanced in these cases.
The realisation of this objective has included the development of a common baseline standard for
security in healthcare systems and environments. The underlying guidelines in this approach cover
all of the principal protection issues, from physical and environmental measures to logical system
access controls. Further to this, the work has encompassed the development of a new protection
methodology by which establishments may determine their additional security requirements (by
classifying aspects of their systems, environments and data). Both the guidelines and the
methodology represent work submitted to the Commission of European Communities SEISMED
(Secure Environment for Information Systems in MEDicine) project, with which the research
programme was closely linked.
The thesis also establishes that healthcare systems can present significant targets for both internal
and external abuse, highlighting a requirement for improved logical controls. However, it is also
shown that the issues of easy integration and convenience are of paramount importance if security is
to be accepted and viable in practice. Unfortunately, many traditional methods do not offer these
advantages, necessitating the need for a different approach.
To this end, the conceptual design for a new intrusion monitoring system was developed, combining
the key aspects of authentication and auditing into an advanced framework for real-time user
supervision. A principal feature of the approach is the use of behaviour profiles, against which user
activities may be continuously compared to determine potential system intrusions and anomalous
events.
The effectiveness of real-time monitoring was evaluated in an experimental study of keystroke
analysis -a behavioural biometric technique that allows an assessment of user identity from their
typing style. This technique was found to have significant potential for discriminating between
impostors and legitimate users and was subsequently incorporated into a fully functional security
system, which demonstrated further aspects of the conceptual design and showed how transparent
supervision could be realised in practice.
The thesis also examines how the intrusion monitoring concept may be integrated into a wider
security architecture, allowing more comprehensive protection within both the local healthcare
establishment and between remote domains.Commission of European Communities
SEISMED proje
Design a framework for IoT- Identification, Authentication and Anomaly detection using Deep Learning: A Review
The Internet of Things (IoT) connects billions of smart gadgets so that they may communicate with one another without the need for human intervention. With an expected 50 billion devices by the end of 2020, it is one of the fastest-growing industries in computer history. On the one hand, IoT technologies are critical in increasing a variety of real-world smart applications that can help people live better lives. The cross-cutting nature of IoT systems, on the other hand, has presented new security concerns due to the diverse components involved in their deployment. For IoT devices and their inherent weaknesses, security techniques such as encryption, authentication, permissions, network monitoring, \& application security are ineffective. To properly protect the IoT ecosystem, existing security solutions need to be strengthened. Machine learning and deep learning (ML/DL) have come a long way in recent years, and machine intelligence has gone from being a laboratory curiosity to being used in a variety of significant applications. The ability to intelligently monitor IoT devices is an important defense against new or negligible assaults. ML/DL are effective data exploration techniques for learning about 'normal' and 'bad' behavior in IoT devices and systems. Following a comprehensive literature analysis on Machine Learning methods as well as the importance of IoT security within the framework of different sorts of potential attacks, multiple DL algorithms have been evaluated in terms of detecting attacks as well as anomaly detection in this work. We propose a taxonomy of authorization and authentication systems in the Internet of Things based on the review, with a focus on DL-based schemes. The authentication security threats and problems for IoT are thoroughly examined using the taxonomy supplied. This article provides an overview of projects that involve the use of deep learning to efficiently and automatically provide IoT applications
Cyber-Physical Threat Intelligence for Critical Infrastructures Security
Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies
A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks
The 5G networks have the capability to provide high compatibility for the new
applications, industries, and business models. These networks can tremendously
improve the quality of life by enabling various use cases that require high
data-rate, low latency, and continuous connectivity for applications pertaining
to eHealth, automatic vehicles, smart cities, smart grid, and the Internet of
Things (IoT). However, these applications need secure servicing as well as
resource policing for effective network formations. There have been a lot of
studies, which emphasized the security aspects of 5G networks while focusing
only on the adaptability features of these networks. However, there is a gap in
the literature which particularly needs to follow recent computing paradigms as
alternative mechanisms for the enhancement of security. To cover this, a
detailed description of the security for the 5G networks is presented in this
article along with the discussions on the evolution of osmotic and catalytic
computing-based security modules. The taxonomy on the basis of security
requirements is presented, which also includes the comparison of the existing
state-of-the-art solutions. This article also provides a security model,
"CATMOSIS", which idealizes the incorporation of security features on the basis
of catalytic and osmotic computing in the 5G networks. Finally, various
security challenges and open issues are discussed to emphasize the works to
follow in this direction of research.Comment: 34 pages, 7 tables, 7 figures, Published In 5G Enabled Secure
Wireless Networks, pp. 69-102. Springer, Cham, 201
Trick or Heat? Manipulating Critical Temperature-Based Control Systems Using Rectification Attacks
Temperature sensing and control systems are widely used in the closed-loop
control of critical processes such as maintaining the thermal stability of
patients, or in alarm systems for detecting temperature-related hazards.
However, the security of these systems has yet to be completely explored,
leaving potential attack surfaces that can be exploited to take control over
critical systems.
In this paper we investigate the reliability of temperature-based control
systems from a security and safety perspective. We show how unexpected
consequences and safety risks can be induced by physical-level attacks on
analog temperature sensing components. For instance, we demonstrate that an
adversary could remotely manipulate the temperature sensor measurements of an
infant incubator to cause potential safety issues, without tampering with the
victim system or triggering automatic temperature alarms. This attack exploits
the unintended rectification effect that can be induced in operational and
instrumentation amplifiers to control the sensor output, tricking the internal
control loop of the victim system to heat up or cool down. Furthermore, we show
how the exploit of this hardware-level vulnerability could affect different
classes of analog sensors that share similar signal conditioning processes.
Our experimental results indicate that conventional defenses commonly
deployed in these systems are not sufficient to mitigate the threat, so we
propose a prototype design of a low-cost anomaly detector for critical
applications to ensure the integrity of temperature sensor signals.Comment: Accepted at the ACM Conference on Computer and Communications
Security (CCS), 201
Machine Learning Meets Communication Networks: Current Trends and Future Challenges
The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction
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