196,582 research outputs found
Systematic Review of Internet of Things Security
The Internet of Things has become a new paradigm of current communications technology that requires a deeper overview to map its application domains, advantages, and disadvantages. There have been a number of in-depth research efforts to study various aspects of IoT. However, to the best of our knowledge, there is no literature that have discussed specifically and deeply about the security and privacy aspects of IoT. To that end, this paper aims at providing a more comprehensive and systematic review of IoT security based on the survey result of the most recent literature over the past three years (2015 to 2017). We have classified IoT security research based on the research objectives, application domains, vulner-abilities/threats, countermeasures, platforms, proto-cols, and performance measurements. We have also provided some security challenges for further research
Industrial Internet of Things – security, weaknesses and most common types of attacks: a systematic literature review
In recent years there has been a growing interest in the Industrial Internet of Things (IIoT) coming from
business and scientific communities alike. One of the elementary concepts of the proposed Industry 4.0 is the
IIoT which proposes the implementation of the regular Internet of Things (IoT) concept on a much larger scale
within the industrial facilities, thus interconnecting devices in industrial settings. While the main focus of the
scientific community is on the cost/benefit analysis and practical application of the mentioned concept, one often
overlooked aspect is its security. The following paper presents a comprehensive systematic literature review for
the Industrial Internet of Things security. It contains a review of the most common types of attacks committed
within the Industrial Internet of Things and a consequential analysis of the weaknesses those attacks exposed.info:eu-repo/semantics/publishedVersio
Trends, Opportunities, and Challenges in Using Restricted Device Authentication in Fog Computing
The few resources available on devices restricted in Internet of Things are
an important issue when we think about security. In this perspective, our work
proposes a agile systematic review literature on works involving the Internet
of Things, authentication, and Fog Computing. As a result, related works,
opportunities, and challenges found at these areas' intersections were brought,
supporting other researchers and developers who work in these areas
Internet of Things security with machine learning techniques:a systematic literature review
Abstract. The Internet of Things (IoT) technologies are beneficial for both private and businesses. The growth of the technology and its rapid introduction to target fast-growing markets faces security challenges. Machine learning techniques have been recently used in research studies as a solution in securing IoT devices. These machine learning techniques have been implemented successfully in other fields. The objective of this thesis is to identify and analyze existing scientific literature published recently regarding the use of machine learning techniques in securing IoT devices.
In this thesis, a systematic literature review was conducted to explore the previous research on the use of machine learning in IoT security. The review was conducted by following a procedure developed in the review protocol. The data for the study was collected from three databases i.e. IEEE Xplore, Scopus and Web of Science. From a total of 855 identified papers, 20 relevant primary studies were selected to answer the research question. The study identified 7 machine learning techniques used in IoT security, additionally, several attack models were identified and classified into 5 categories.
The results show that the use of machine learning techniques in IoT security is a promising solution to the challenges facing security. Supervised machine learning techniques have better performance in comparison to unsupervised and reinforced learning. The findings also identified that data types and the learning method affects the performance of machine learning techniques. Furthermore, the results show that machine learning approach is mostly used in securing the network
Analysis of Security Mechanisms Based on Clusters IoT Environments
Internet of things is based on sensors, communication networks and intelligence that manages the entire process and the generated data. Sensors are the senses of systems, because of this, they can be used in large quantities. Sensors must have low power consumption and cost, small size and great flexibility for its use in all circumstances. Therefore, the security of these network devices, data sensors and other devices, is a major concern as it grows rapidly in terms of nodes interconnected via sensor data. This paper presents an analysis from a systematic review point of view of articles on Internet of Things (IoT), security aspects specifically at privacy level and control access in this type of environment. Finally, it presents an analysis of security issues that must be addressed, from different clusters and identified areas within the fields of application of this technology
A systematic literature review of blockchain cyber security
Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008, blockchain has (slowly) become one of the most frequently discussed methods for securing data storage and transfer through decentralized, trustless, peer-to-peer systems. This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications. Our findings show that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII). This timely systematic review also sheds light on future directions of research, education and practices in the blockchain and cyber security space, such as security of blockchain in IoT, security of blockchain for AI data, and sidechain security,etc
An SLR on Edge Computing Security and possible threat protection
Mobile and Internet of Things devices are generating enormous amounts of
multi-modal data due to their exponential growth and accessibility. As a
result, these data sources must be directly analyzed in real time at the
network edge rather than relying on the cloud. Significant processing power at
the network's edge has made it possible to gather data and make decisions prior
to data being sent to the cloud. Moreover, security problems have significantly
towered as a result of the rapid expansion of mobile devices, Internet of
Things (IoT) devices, and various network points. It's much harder than ever to
guarantee the privacy of sensitive data, including customer information. This
systematic literature review depicts the fact that new technologies are a great
weapon to fight with the attack and threats to the edge computing security
Security challenges of Internet of Underwater Things : a systematic literature review
Water covers approximately 71% of the earth surface, yet much of the underwater world remains unexplored due to technology limitations. Internet of Underwater
Things (IoUT) is a network of underwater objects that enables monitoring subsea environment remotely. Underwater Wireless Sensor Network (UWSN) is the
main enabling technology for IoUT. UWSNs are characterised by the limitations
of the underlying acoustic communication medium, high energy consumption, lack
of hardware resources to implement computationally intensive tasks and dynamic
network topology due to node mobility. These characteristics render UNWSNs vulnerable to different attacks, such as Wormhole, Sybil, flooding, jamming, spoofing
and Denial of Service (DoS) attacks. This article reviews peer-reviewed literature
that addresses the security challenges and attacks on UWSNs as well as possible
mitigative solutions. Findings show that the biggest contributing factors to security threats in UWSNs are the limited energy supply, the limited communication
medium and the harsh underwater communication conditions. Researchers in this
field agree that the security measures of terrestrial wireless sensor networks are not
directly applicable to UWSNs due to the unique nature of the underwater environment where resource management becomes a significant challenge. This article also
outlines future research directions on security and privacy challenges of IoUT and
UWSN
The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis
In recent years, mobile devices (e.g., smartphones and tablets) have met an
increasing commercial success and have become a fundamental element of the
everyday life for billions of people all around the world. Mobile devices are
used not only for traditional communication activities (e.g., voice calls and
messages) but also for more advanced tasks made possible by an enormous amount
of multi-purpose applications (e.g., finance, gaming, and shopping). As a
result, those devices generate a significant network traffic (a consistent part
of the overall Internet traffic). For this reason, the research community has
been investigating security and privacy issues that are related to the network
traffic generated by mobile devices, which could be analyzed to obtain
information useful for a variety of goals (ranging from device security and
network optimization, to fine-grained user profiling).
In this paper, we review the works that contributed to the state of the art
of network traffic analysis targeting mobile devices. In particular, we present
a systematic classification of the works in the literature according to three
criteria: (i) the goal of the analysis; (ii) the point where the network
traffic is captured; and (iii) the targeted mobile platforms. In this survey,
we consider points of capturing such as Wi-Fi Access Points, software
simulation, and inside real mobile devices or emulators. For the surveyed
works, we review and compare analysis techniques, validation methods, and
achieved results. We also discuss possible countermeasures, challenges and
possible directions for future research on mobile traffic analysis and other
emerging domains (e.g., Internet of Things). We believe our survey will be a
reference work for researchers and practitioners in this research field.Comment: 55 page
Cybersecurity and Cyber Forensics: Machine Learning Approach Systematic Review
The proliferation of cloud computing and internet of things has led to the connectivity of states and nations (developed and developing countries) worldwide in which global network provide platform for the connection.Digital forensics is a field of computer security that uses software applications and standard guidelines which support the extraction of evidences from any computer appliances which is perfectly enough for the court of law to use and make a judgment based on the comprehensiveness, authenticity and objectivity of the information obtained. Cybersecurity is of major concerned to the internet users worldwide due to the recent form of attacks,threat, viruses, intrusion among others going on every day among internet of things. However, it is noted that cybersecurity is based on confidentiality,integrity and validity of data. The aim of this work is make a systematic review on the application of machine learning algorithms to cybersecurity and cyber forensics and pave away for further research directions on the application of deep learning, computational intelligence, soft computing to cybersecurity and cyber forensics
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