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1 |
Extending the limits for big data RSA cracking: Towards cache-oblivious TU decomposition
گسترش محدودیت های بزرگ برای شکستن داده RSA: به سمت حافظه نهان-فراموشی TU-2020 Nowadays, Big Data security processes require mining large amounts of content that was traditionally
not typically used for security analysis in the past. The RSA algorithm has become the de facto standard
for encryption, especially for data sent over the internet. RSA takes its security from the hardness of the
Integer Factorisation Problem. As the size of the modulus of an RSA key grows with the number of bytes
to be encrypted, the corresponding linear system to be solved in the adversary integer factorisation
algorithm also grows. In the age of big data this makes it compelling to redesign linear solvers over
finite fields so that they exploit the memory hierarchy. To this end, we examine several matrix layouts
based on space-filling curves that allow for a cache-oblivious adaptation of parallel TU decomposition
for rectangular matrices over finite fields. The TU algorithm of Dumas and Roche (2002) requires
index conversion routines for which the cost to encode and decode the chosen curve is significant.
Using a detailed analysis of the number of bit operations required for the encoding and decoding
procedures, and filtering the cost of lookup tables that represent the recursive decomposition of the
Hilbert curve, we show that the Morton-hybrid order incurs the least cost for index conversion routines
that are required throughout the matrix decomposition as compared to the Hilbert, Peano, or Morton
orders. The motivation lies in that cache efficient parallel adaptations for which the natural sequential
evaluation order demonstrates lower cache miss rate result in overall faster performance on parallel
machines with private or shared caches and on GPU’s. Keywords: Exact linear algebra | Cache-oblivious algorithms | Space-filling curves | Morton-hybrid order |
مقاله انگلیسی |
2 |
به سوی امنیت دادههای مبتنی بر DNA در محیط محاسبه ابری
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 36 امروزه اندازه دادهها روزانه از گیگابایت به ترابایت یا حتی پتابایت افزایش مییابد که عمدتا ً به دلیل تکامل مقادیر زیادی از دادههای زمان حقیقی است . اکثر دادههای بزرگ از طریق اینترنت منتقل میشوند و آنها در محیط محاسبه ابری ذخیره میشوند . از آنجا که رایانش ابری خدمات مبتنی بر اینترنت را فراهم میکند , مهاجمان و کاربران مخربی نیز وجود دارند . آنها همیشه سعی میکنند بدون داشتن حق دسترسی به دادههای بزرگ و محرمانه کاربر به آنها دسترسی داشته باشند . گاهی اوقات آنها دادههای اصلی را با هر گونه داده جعلی جایگزین میکنند . بنابراین , امنیت دادههای بزرگ اخیرا ً به یک نگرانی عمده تبدیل شدهاست . محاسبه اسید دی اکسی ریبونوکلئیک( DNA ) یک میدان نوظهور برای بهبود امنیت دادهها است که براساس مفهوم زیستی DNA است . یک طرح رمزگذاری داده مبتنی بر DNA در این مقاله برای محیط محاسبه ابری پیشنهاد شدهاست . در اینجا , یک کلید سری ۱۰۲۴ بیتی براساس محاسبه DNA , ویژگیهای کاربر و کنترل دسترسی رسانهای ( MAC ) , کد استاندارد آمریکا برای تبادل اطلاعات ( ASCII ) , پایگاههای DNA و قانون مکمل برای تولید کلید رمز مورد استفاده قرار میگیرد که این سیستم را قادر میسازد تا در برابر بسیاری از حملات امنیتی محافظت کند . نتایج آزمایشی و نیز تحلیلهای تئوریک , کارایی و کارایی طرح پیشنهادی را بر روی برخی از طرحهای شناختهشده موجود نشان میدهند .
واژگان کاربردی : رایانش ابری | محاسبات DNA | امنیت دادههای بزرگ | نشانی MAC | قانون کمل | شبیه ساز ابری |
مقاله ترجمه شده |
3 |
Towards DNA based data security in the cloud computing environment
به سمت امنیت داده های مبتنی بر DNA در محیط محاسبات ابری-2020 Nowadays, data size is increasing day by day from gigabytes to terabytes or even petabytes, mainly because of
the evolution of a large amount of real-time data. Most of the big data is transmitted through the internet and
they are stored on the cloud computing environment. As cloud computing provides internet-based services,
there are many attackers and malicious users. They always try to access user’s confidential big data without
having the access right. Sometimes, they replace the original data by any fake data. Therefore, big data security
has become a significant concern recently. Deoxyribonucleic Acid (DNA) computing is an advanced emerged
field for improving data security, which is based on the biological concept of DNA. A novel DNA based data
encryption scheme has been proposed in this paper for the cloud computing environment. Here, a 1024-bit
secret key is generated based on DNA computing, user’s attributes and Media Access Control (MAC) address of
the user, and decimal encoding rule, American Standard Code for Information Interchange (ASCII) value, DNA
bases and complementary rule are used to generate the secret key that enables the system to protect against
many security attacks. Experimental results, as well as theoretical analyses, show the efficiency and effectivity
of the proposed scheme over some well-known existing schemes. Keywords: Cloud computing | DNA computing | Big data security | MAC address | Complementary rule | CloudSim |
مقاله انگلیسی |
4 |
TPTVer: A Trusted Third Party Based Trusted Verifier for Multi-Layered Outsourced Big Data System in Cloud Environment
TPTVer: یک تایید کننده معتبر مبتنی بر شخص ثالث برای سیستم داده های بزرگ برون سپاری چند لایه در محیط ابری-2018 Cloud computing is very useful for
big data owner who doesn’t want to manage
IT infrastructure and big data technique details. However, it is hard for big data owner to
trust multi-layer outsourced big data system
in cloud environment and to verify which
outsourced service leads to the problem. Similarly, the cloud service provider cannot simply
trust the data computation applications. At last,
the verification data itself may also leak the
sensitive information from the cloud service
provider and data owner. We propose a new
three-level definition of the verification, threat
model, corresponding trusted policies based
on different roles for outsourced big data
system in cloud. We also provide two policy
enforcement methods for building trusted data
computation environment by measuring both
the MapReduce application and its behaviors
based on trusted computing and aspect-oriented programming. To prevent sensitive information leakage from verification process,
we provide a privacy-preserved verification
method. Finally, we implement the TPTVer, a
Trusted third Party based Trusted Verifier as a
proof of concept system. Our evaluation and
analysis show that TPTVer can provide trusted
verification for multi-layered outsourced big
data system in the cloud with low overhead.
Keywords: big data security; outsourced ser vice security; MapReduce behavior; trusted verification; trusted third party |
مقاله انگلیسی |
5 |
A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security
یک بردار حمایتی بیش از حد حقیقی بی هدف ماشین آلات برای داده های بزرگ امنیت سایبری -2018 Cyber security in the context of big data is known to be a critical problem and presents a
great challenge to the research community. Machine learning algorithms have been suggested as candidates
for handling big data security problems. Among these algorithms, support vector machines (SVMs) have
achieved remarkable success on various classification problems. However, to establish an effective SVM,
the user needs to define the proper SVM configuration in advance, which is a challenging task that requires
expert knowledge and a large amount of manual effort for trial and error. In this paper, we formulate the
SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity
are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective
optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has
been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy
and low-level heuristics. The high-level strategy uses the search performance to control the selection of
which low-level heuristic should be used to generate a new SVM configuration. The low-level heuristics
each use different rules to effectively explore the SVM configuration search space. To address bi-objective
optimization, the proposed framework adaptively integrates the strengths of decomposition- and Paretobased approaches to approximate the Pareto set of SVM configurations. The effectiveness of the proposed
framework has been evaluated on two cyber security problems: Microsoft malware big data classification and
anomaly intrusion detection. The obtained results demonstrate that the proposed framework is very effective,
if not superior, compared with its counterparts and other algorithms.
INDEX TERMS: Hyper-heuristics, big data, cyber security, optimisation |
مقاله انگلیسی |
6 |
An approach for Big Data Security based on Hadoop Distributed File system
یک رویکرد برای امنیت داده های بزرگ مبتنی بر سیستم فایل توزیع هادوپ-2018 Cloud computing appeared for huge data
because of its ability to provide users with on-demand,
reliable, flexible, and low-cost services. With the increasing
use of cloud applications, data security protection has
become an important issue for the cloud. In this work, the
proposed approach was used to improve the performance
of encryption /Decryption file by using AES and OTP
algorithms integrated on Hadoop. Where files are
encrypted within the HDFS and decrypted within the Map
Task. Encryption /Decryption in previous works used AES
algorithm, the size of the encrypted file increased by 50%
from the original file size. The proposed approach
improved this ratio as the size of the encrypted file
increased by 20% from the original file size. Also, we have
compared this approach with the previously implemented
method, we implement this new approach to secure HDFS,
and some experimental studies were conducted to verify its
effectiveness.
Keywords: Cloud storage, Hadoop, HDFS, Data Security, Encryption, Decryption |
مقاله انگلیسی |
7 |
Big Data Security Issues and challenges
چالش ها و مسائل امنیتی داده های بزرگ -2016 We have entered in data deluge already. Data Deluge
means data generated by IoT devices and humans
simultaneously. The data deluge is a Big threat for technologist
but beneficial for end users. Now the coming problem is the
security of this data. Big Data is too big, too fast and too diverse
that does not compile with traditional data base system.
Traditional data base systems are very good to analyze
structured data but these systems are not enough to analyze
unstructured data. In this paper we discourse the possible
challenges and security issues related to Big Data characteristics
and possible solutions.
Keywords: Anonymization | Big Data | Unstructured Data | IoT | Traditional DBMS | Machine Learning | Data Mining |
مقاله انگلیسی |
8 |
Big data security
امنیت داده های بزرگ-2012 The term big data has come into use recently to refer to the ever-increasing
amount of information that organisations are storing, processing and analysing,
owing to the growing number of information sources in use. According to
research conducted by IDC, there were 1.8 zettabytes (1.8 trillion gigabytes) of
information created and replicated in 2011 alone and that amount is doubling
every two years.
Within the next decade, the amount of information managed
by enterprise datacentres will grow by 50 times, whereas the number of IT
professionals will expand by just 1.5 times. |
مقاله انگلیسی |