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ردیف | عنوان | نوع |
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The “Cyber Security via Determinism” Paradigm for a Quantum Safe Zero Trust Deterministic Internet of Things (IoT)
پارادایم «امنیت سایبری از طریق جبرگرایی» برای اینترنت اشیا قطعی (IoT) ایمن صفر کوانتومی-2022 The next-generation Internet of Things (IoT) will control the critical infrastructure of the 21st
century, including the Smart Power Grid and Smart Cities. It will also support Deterministic Communications, where ‘deterministic traffic flows’ (D-flows) receive strict Quality-of-Service (QoS) guarantees.
A ‘Cybersecurity via Determinism’ paradigm for the next-generation ‘Industrial and Tactile Deterministic
IoT’ is presented. A forwarding sub-layer of simple and secure ‘deterministic packet switches’ (D-switches)
is introduced into layer-3. This sub-layer supports many deterministic Software Defined Wide Area Networks
(SD-WANs), along with 3 new tools for improving cyber security: Access Control, Rate Control, and
Isolation Control. A Software Defined Networking (SDN) control-plane configures each D-switch (ie FPGA)
with multiple deterministic schedules to support D-flows. The SDN control-plane can embed millions of
isolated Deterministic Virtual Private Networks (DVPNs) into layer 3. This paradigm offers several benefits:
1) All congestion, interference, and Distributed Denial-of-Service (DDOS) attacks are removed; 2) Buffer
sizes in D-switches are reduced by 1000C times; 3) End-to-end IoT delays can be reduced to ultra-low
latencies, i.e., the speed-of-light in fiber; 4) The D-switches do not require Gigabytes of memory to store
large IP routing tables; 5) Hardware support is provided in layer 3 for the US NIST Zero Trust Architecture;
6) Packets within a DVPN can be entirely encrypted using Quantum Safe encryption, which is impervious
to attacks by Quantum Computers using existing quantum algorithms; 7) The probability of an undetected
cyberattack targeting a DVPN can be made arbitrarily small by using long Quantum Safe encryption keys;
and 8) Savings can reach $10s of Billions per year, through reduced capital, energy and operational costs.
INDEX TERMS: Cyber security | deterministic, the Internet of Things (IoT) | quantum computing, zero trust | encryption | privacy | Software Defined Networking (SDN) | industrial internet of things (IIoT) | tactile Internet of Things | FPGA | Industry 4.0 | deterministic Internet of Things. |
مقاله انگلیسی |
2 |
Smart City Data Science: Towards data-driven smart cities with open research issues
علم داده شهر هوشمند: به سوی شهرهای هوشمند مبتنی بر داده با مسائل تحقیقاتی باز-2022 Cities are undergoing huge shifts in technology and operations in recent days, and ‘data science’
is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR).
Extracting useful knowledge or actionable insights from city data and building a corresponding
data-driven model is the key to making a city system automated and intelligent. Data science
is typically the scientific study and analysis of actual happenings with historical data using a
variety of scientific methodologies, machine learning techniques, processes, and systems. In this
paper, we concentrate on and explore ‘‘Smart City Data Science’’, where city data collected from
various sources such as sensors, Internet-connected devices, or other external sources, is being
mined for insights and hidden correlations to enhance decision-making processes and deliver
better and more intelligent services to citizens. To achieve this goal, artificial intelligence,
particularly, machine learning analytical modeling can be employed to provide deeper knowledge
about city data, which makes the computing process more actionable and intelligent in various
real-world city services. Finally, we identify and highlight ten open research issues for future
development and research in the context of data-driven smart cities. Overall, we aim to provide
an insight into smart city data science conceptualization on a broad scale, which can be used
as a reference guide for the researchers, industry professionals, as well as policy-makers of a
country, particularly, from the technological point of view.
keywords: شهرهای هوشمند | علم داده | فراگیری ماشین | اینترنت اشیا | تصمیم گیری داده محور | خدمات هوشمند | امنیت سایبری | Smartcities | Datascience | Machinelearning | InternetofThings | Data-drivendecisionmaking | Intelligentservices | Cybersecurity |
مقاله انگلیسی |
3 |
A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
بررسی جامع تشخیص حملات سایبری: مجموعه دادهها، روشها، چالش ها و جهتگیریهای تحقیقاتی آینده-2022 Rapid developments in network technologies and the amount and scope of data transferred on networks
are increasing day by day. Depending on this situation, the density and complexity of cyber threats
and attacks are also expanding. The ever-increasing network density makes it difficult for cybersecurity professionals to monitor every movement on the network. More frequent and complex cyberattacks make the detection and identification of anomalies in network events more complex. Machine
learning offers various tools and techniques for automating the detection of cyber attacks and for
rapid prediction and analysis of attack types. This study discusses the approaches to machine learning
methods used to detect attacks. We examined the detection, classification, clustering, and analysis of
anomalies in network traffic. We gave the cyber-security focus, machine learning methods, and data
sets used in each study we examined. We investigated which feature selection or dimension reduction
method was applied to the data sets used in the studies. We presented in detail the types of classification
carried out in these studies, which methods were compared with other methods, the performance
metrics used, and the results obtained in tables. We examined the data sets of network attacks presented
as open access. We suggested a basic taxonomy for cyber attacks. Finally, we discussed the difficulties
encountered in machine learning applications used in network attacks and their solutions
Keywords: Cyber attacks | Machine learning | Deep learning | Geometric deep learning | Cyber security | Adversarial machine learning | Intrusion detection |
مقاله انگلیسی |
4 |
A dataset for accounting, finance and economics research on US data breaches
یک مجموعه داده برای حسابداری، مالی و تحقیقات اقتصاد در مورد نقض داده های ایالات متحده-2021 This data article describes a dataset of data breaches in US
listed firms over a ten-year period. Data breaches represent
major events that pose serious challenges to organisations.
The number of incidents has been on the increase over the
last decade and this has attracted the interest of the media,
consumers and regulators. While there is a well-established
literature on cybersecurity in Computer Science and Information Systems journals, studies exploring the economic and
business impacts of data breaches represent a relatively recent phenomenon. There is a nascent but fast-growing literature in accounting, finance and economics that focuses on
the financial impacts of data breaches and this dataset provides a useful resource for future studies in this space. By
providing data on the company identifier, the type of breach,
the dates of breach disclosure, and relates these dates to
the company’s fiscal year, the dataset can be merged quickly
with existing accounting and finance datasets. The dataset includes data on 506 incidents over a ten-year period thereby
enabling cross-sectional and longitudinal analyses.
Keywords: Cyber security | Data breaches | Finance | Accounting | Event study |
مقاله انگلیسی |
5 |
Beware suppliers bearing gifts!: Analysing coverage of supply chain cyber security in critical national infrastructure sectorial and cross-sectorial frameworks
مراقب تأمین کنندگان هدیه باشید!: تجزیه و تحلیل پوشش امنیت سایبری زنجیره تامین در زیرساخت های مهم ملی بخش های بخشی و بین بخشی-2021 Threat actors are increasingly targeting extended supply chains and abusing client-supplier
trust to conduct third-party compromise. Governments are concerned about targeted attacks against critical national infrastructures, where compromise can have significant adverse national consequences. In this paper we identify and review advice and guidance offered by authorities in the UK, US, and the EU regarding Cyber Supply Chain Risk Management (C-SCRM). We then conduct a review of sector specific guidance in the three regions for
the chemical, energy, and water sectors. We assessed frameworks that each region’s sector
offered organisations for C-SCRM suitability. Our results found a range of interpretations for
“Supply Chain” that resulted in a diversity in the quantity and quality of advice offered by
regional authorities, sectors, and their frameworks. This is exacerbated by the lack of a common taxonomy to support supply chain procurement and risk management that has led to
limited coverage in most C-SCRM programs. Our results highlight the need for a taxonomy
regarding C-SCRM and systematic guidance (both general and sector specific) to enable controls to be deployed to mitigate against supply chain risk. We provide an outline taxonomy
based on our data analysis to promote further discussion and research. Keywords: Cyber security | Supply chain | Risk management | Critical national infrastructure | Common taxonomy |
مقاله انگلیسی |
6 |
عوامل موثر بر پذیرش بلاک چین در شیوههای مدیریت زنجیره تامین : یک مطالعه مبتنی بر صنعت نفت
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 33 |
مقاله ترجمه شده |
7 |
A semantic-based methodology for digital forensics analysis
یک روش مبتنی بر معنایی برای تجزیه و تحلیل پزشکی قانونی دیجیتال-2020 Nowadays, more than ever, digital forensics activities are involved in any criminal, civil or military
investigation and represent a fundamental tool to support cyber-security. Investigators use a variety
of techniques and proprietary software forensics applications to examine the copy of digital
devices, searching hidden, deleted, encrypted, or damaged files or folders. Any evidence found is
carefully analysed and documented in a ‘‘finding report’’ in preparation for legal proceedings that
involve discovery, depositions, or actual litigation. The aim is to discover and analyse patterns of
fraudulent activities. In this work, a new methodology is proposed to support investigators during the
analysis process, correlating evidence found through different forensics tools. The methodology was
implemented through a system able to add semantic assertion to data generated by forensics tools
during extraction processes. These assertions enable more effective access to relevant information and
enhanced retrieval and reasoning capabilities. Keywords: Digital forensics | Text analysis | Log analysis | Correlation | Cybersecurity |
مقاله انگلیسی |
8 |
Internet of Things: Evolution and technologies from a security perspective
اینترنت اشیاء: تکامل و فناوری ها از دیدگاه امنیتی-2020 In recent years, IoT has developed into many areas of life including smart homes, smart cities, agriculture,
offices, and workplaces. Everyday physical items such as lights, locks and industrial machineries can now be part
of the IoT ecosystem. IoT has redefined the management of critical and non-critical systems with the aim of
making our lives more safe, efficient and comfortable. As a result, IoT technology is having a huge positive
impact on our lives. However, in addition to these positives, IoT systems have also attracted negative attention
from malicious users who aim to infiltrate weaknesses within IoT systems for their own gain, referred to as cyber
security attacks. By creating an introduction to IoT, this paper seeks to highlight IoT cyber security vulnerabilities
and mitigation techniques to the reader.
The paper is suitable for developers, practitioners, and academics, particularly from fields such as computer
networking, information or communication technology or electronics. The paper begins by introducing IoT as
the culmination of two hundred years of evolution within communication technologies. Around 2014, IoT
reached consumers, early products were mostly small closed IoT networks, followed by large networks such as
smart cities, and continuing to evolve into Next Generation Internet; internet systems which incorporate human
values. Following this evolutionary introduction, IoT architectures are compared and some of the technologies
that are part of each architectural layer are introduced. Security threats within each architectural layer and some
mitigation strategies are discussed, finally, the paper concludes with some future developments. Keywords: IoT | Internet of Things | Security | Cyber security | Secure by Design | Next Generation Internet | Smart city | Sustainable city | Energy reduction | Building Energy Management Systems |
مقاله انگلیسی |
9 |
Hacking the AI - the Next Generation of Hijacked Systems
هک کردن هوش مصنوعی - نسل بعدی سیستم های ربوده شده-2020 Within the next decade, the need for automation, intelligent data handling
and pre-processing is expected to increase in order to cope with the vast amount of
information generated by a heavily connected and digitalised world. Over the past
decades, modern computer networks, infrastructures and digital devices have grown
in both complexity and interconnectivity. Cyber security personnel protecting these
assets have been confronted with increasing attack surfaces and advancing attack
patterns. In order to manage this, cyber defence methods began to rely on automation
and (artificial) intelligence supporting the work of humans. However, machine learning
(ML) and artificial intelligence (AI) supported methods have not only been integrated
in network monitoring and endpoint security products but are almost omnipresent in
any application involving constant monitoring, complex or large volumes of data.
Intelligent IDS, automated cyber defence, network monitoring and surveillance as
well as secure software development and orchestration are all examples of assets that
are reliant on ML and automation. These applications are of considerable interest to
malicious actors due to their importance to society. Furthermore, ML and AI methods
are also used in audio-visual systems utilised by digital assistants, autonomous
vehicles, face-recognition applications and many others. Successful attack vectors
targeting the AI of audio-visual systems have already been reported. These attacks
range from requiring little technical knowledge to complex attacks hijacking the
underlying AI. With the increasing dependence of society on ML and AI, we must prepare for the
next generation of cyber attacks being directed against these areas. Attacking a system
through its learning and automation methods allows attackers to severely damage the
system, while at the same time allowing them to operate covertly. The combination of being inherently hidden through the manipulation made, its devastating impact
and the wide unawareness of AI and ML vulnerabilities make attack vectors against
AI and ML highly favourable for malicious operators. Furthermore, AI systems
tend to be difficult to analyse post-incident as well as to monitor during operations.
Discriminating a compromised from an uncompromised AI in real-time is still
considered difficult.
In this paper, we report on the state of the art of attack patterns directed against AI
and ML methods. We derive and discuss the attack surface of prominent learning
mechanisms utilised in AI systems. We conclude with an analysis of the implications
of AI and ML attacks for the next decade of cyber conflicts as well as mitigations
strategies and their limitations. Keywords: AI hijacking | artificial intelligence | machine learning | cyber attack | cyber security |
مقاله انگلیسی |
10 |
Towards Security and Privacy for Edge AI in IoT/IoE based Digital Marketing Environments
به سمت امنیت و حفظ حریم خصوصی برای هوش مصنوعی لبه در محیط های بازاریابی دیجیتال مبتنی بر IoT / IoE-2020 Abstract—Edge Artificial Intelligence (Edge AI) is a crucial
aspect of the current and futuristic digital marketing Internet of
Things (IoT) / Internet of Everything (IoE) environment.
Consumers often provide data to marketers which is used to enhance
services and provide a personalized customer experience (CX).
However, use, storage and processing of data has been a key concern.
Edge computing can enhance security and privacy which has been
said to raise the current state of the art in these areas. For example,
when certain processing of data can be done local to where
requested, security and privacy can be enhanced. However, Edge AI
in such an environment can be prone to its own security and privacy
considerations, especially in the digital marketing context where
personal data is involved. An ongoing challenge is maintaining
security in such context and meeting various legal privacy
requirements as they themselves continue to evolve, and many of
which are not entirely clear from the technical perspective. This
paper navigates some key security and privacy issues for Edge AI in
IoT/IoE digital marketing environments along with some possible
mitigations. Keywords: edge security | edge privacy | edge AI | edge intelligence | artificial intelligence | AI | machine learning | ML | IoT | IoE | edge | cybersecurity | legal | law | digital marketing | smart | GDPR | CCPA | security | privacy |
مقاله انگلیسی |