مجال
التميز
|
إبداع علمي + تميز دراسي و بحثي
|
جوائز التكريم :
|
|
الجائزة (1):
|
|
مسمى الجائزة:
|
Best
Paper presentation
|
الجهة المانحة:
|
8th Saudi Student Conference
organising committee
|
تاريخ الجائزة:
|
01 Feb 2015
|
مجال التكريم:
|
Security
and Cloud Computing
|
الجائزة (2):
|
|
مسمى الجائزة:
|
Certificate
of Distinction
|
الجهة المانحة:
|
International
Conference on Cloud Computing ICCC 2015 organizing committee
|
تاريخ منح الجائزة:
|
26/04/2015
|
مجال التكريم:
|
The research paper
entitled: “How
Reduce Max Algorithm behaves with Symptoms Appearance in Virtual Machines in Clouds” has been selected as a distinctive paper in the
International Conference on Cloud Computing ICCC 2015
|
البحوث المنشورة
|
|
البحث (1):
|
|
عنوان البحث:
|
Discovering
Malicious Behaviour Symptoms in Cloud Systems
|
رابط إلى البحث:
|
NYA
|
تاريخ النشر:
|
02/12/2015
|
موجز عن البحث:
|
Cloud computing system is a modern technology that enables users
to share resources in a virtual storage and computing environment. A cloud
system provides an environment of a number of virtual machines (VMs), which
are used by multiple users, and implemented on a single physical server.
However, cloud security is one of the substantial concerns for researchers
and users of cloud computing. In order to secure data storage, any malicious
behaviour, which could be internal, such as data loss, or external, e.g.
attacks, should be promptly discovered and stopped if possible. In this
paper, our research approach focuses on identifying malicious behaviour via
discovering respective symptoms rather than targeting particular malicious
behaviour directly. The reason behind our research approach is that a
malicious behaviour can be characterised by a set of symptoms. Finally, this
paper provides experiments of monitoring VMs in order to discover symptoms.
|
المؤتمرات العلمية:
|
|
المؤتمر (1):
|
|
عنوان المؤتمر:
|
International
Conference on Cloud Computing ICCC 2015
|
تاريخ الإنعقاد:
|
26/04/2015
|
مكان
الإنعقاد:
|
Riyadh,
Saudi Arabia
|
طبيعة المشاركة:
|
Paper
presentation
|
عنوان المشاركة:
|
How
Reduce Max Algorithm behaves with Symptoms Appearance in Virtual Machines in
Clouds
|
ملخص المشاركة:
|
Cloud
computing is a new way of computing that gave users an opportunity to share
resources in a virtual storage. Therefore, cloud systems provide universal
environment with a considerable number of Virtual Machines (VMs) that is
available to many users who can access them on-line. This form of access
makes cloud systems weaker than physical networks, and consequently, they are
easy goals for attackers. In order to reduce the number of attacks, any
malicious behaviour should be rapidly discovered and stopped. In this paper,
there is a continuation of the research that focus on the discovery of
malicious behaviour symptoms. The main contribution of this paper is the
simulation analysis of algorithm Reduce Max with respect to its delay in
monitoring cloud systems with different demand functions according to the
appearance of symptoms that should be on a random occurrence.
|
المؤتمر (2):
|
|
عنوان المؤتمر:
|
8th Saudi Student Conference
|
تاريخ الإنعقاد:
|
31/01/2015
|
مكان
الإنعقاد:
|
London,
UK
|
طبيعة المشاركة:
|
Paper presentation
|
عنوان المشاركة:
|
Discovering
Malicious Behaviour Symptoms in Cloud Systems
|
ملخص المشاركة:
|
Cloud
computing system is a modern technology that enables users to share resources
in a virtual storage and computing environment. A cloud system provides an
environment of a number of virtual machines (VMs), which are used by multiple
users, and implemented on a single physical server. However, cloud security
is one of the substantial concerns for researchers and users of cloud
computing. In order to secure data storage, any malicious behaviour, which
could be internal, such as data loss, or external, e.g. attacks, should be
promptly discovered and stopped if possible. In this paper, our research
approach focuses on identifying malicious behaviour via discovering
respective symptoms rather than targeting particular malicious behaviour
directly. The reason behind our research approach is that a malicious
behaviour can be characterised by a set of symptoms. Finally, this paper
provides experiments of monitoring VMs in order to discover symptoms.
|
|
|
المؤتمر (3):
|
|
عنوان المؤتمر:
|
IEEE
CIT-2015/IUCC – 2015/DASC-2015/PICOM-2015 Conferences
|
تاريخ الإنعقاد:
|
26-28/10/2015
|
مكان
الإنعقاد:
|
Liverpool,
UK
|
طبيعة المشاركة:
|
Paper
presentation
|
عنوان المشاركة:
|
Efficient
Discovery of Malicious Symptoms in Clouds via Monitoring Virtual Machines
|
ملخص المشاركة:
|
Amid the rapid growth of the Internet users, cybercrime is
becoming one of the most challenging tasks for the systems and applications
designers. The cybercrime threat is reflected in the increased number of
cases and methods used by criminals. Systems based on cloud computing are
natural targets due to their complexity (greater room for security
weaknesses) and increasing popularity. Cloud computing system is a modern
technology that enables users to share resources in a virtual storage
and computing environment. A cloud system is based on multiple physical
servers. It provides universal environment with a (large) number
of Virtual Machines (VMs) that is available to many users
accessing this system via the Internet. This form of access makes
cloud systems weaker than physical networks. In order to prevent or minimize
the number of attacks and in turn to secure data storage,
any malicious behavior such as external undesirable interventions
should be rapidly identified and halted if possible. In this paper, we focus
on discovery of malicious behavior via determining
unwanted symptoms rather than via targeting
particular malicious behavior of the system directly.
The main motivation for our approach is that a malicious
behavior (e.g., a new form of threat) is very often hard to specify directly,
but it can be characterized by a set of undesired symptoms. The main
contribution of this paper refers to several new mechanisms
or monitoring Virtual Machines and further experimental
work targeting efficient discovery of malicious symptoms.
|
|
|
المؤتمر (4):
|
|
عنوان المؤتمر:
|
9th Saudi
Students’ Conference
|
تاريخ الإنعقاد:
|
13/02/2016
|
مكان
الإنعقاد:
|
Birmingham,
UK
|
طبيعة المشاركة:
|
Poster presentation
|
عنوان المشاركة:
|
Passion Distribution for Cloud Monitoring
|
ملخص المشاركة:
|
Cloud computing is an inactive
technology that enables users to provide application services, development
platforms and virtualized infrastructures. A cloud system provides an environment
of a number of Virtual Machines, which are used by multiple users, implemented
on a single physical server. Our research approach focuses on identifying
malicious behaviour via discovering respective
symptoms rather than targeting particular malicious behaviour
directly(Alshamrani et al. 2015). The reason behind our research approach is
that a malicious behaviour can be characterized by a set of symptoms.
We use Reduce-Max algorithm for monitor
Virtual Machines (VMs) in cloud. Six configurations are considered,
implemented and analysed for Reduce-Max algorithm. They are:
1-
Random
Random input here means that the values
of weights are selected randomly and independently from integer values from
1 to 10 (Alshamrani, 2015).
2-
Uniform
Uniform input means that all VMs have
same weights, for example, all of the VMs’
weights are ones (Alshamrani, 2015).
3-
Arithmetic integer series
Arithmetic integer sequence of weights
means that weight one, for the first VM, equals one, weight two equals two
and so on, until the weight of the N-th VM equals N (Alshamrani, 2015).
4- Harmonic numbers
Harmonic numbers for VMs’ weights are
one for VM1, ½ for VM2, 1/3 for VM3 and
towards
1/n for VMn (Alshamrani, 2015).
5- Exponential numbers
Weights of VMs here are put according
to exponential numbers. They are as follows: one for VM1, 1/2 for VM2, 1/4 for VM3, 1/8
for VM4 until 1/2^n for VMn (Alshamrani, 2015).
6- Poisson distribution
Poisson distribution generate numbers
for VMs weights according to specific formula. Overall, we tested these sets
of weights for VMs. From the results, some good trend occur in the case of
Poisson weights that leads to do more investigation about it in the future.
|
|
|
المؤتمر (5):
|
|
عنوان المؤتمر:
|
2nd
International Conference on Cloud Computing Technologies and Applications –
CloudTech’16
|
تاريخ الإنعقاد:
|
25/05/2016
|
مكان
الإنعقاد:
|
Marrakesh, Morocco
|
طبيعة المشاركة:
|
Paper presentation
|
عنوان المشاركة:
|
The Impact of
Hierarchical Structure on Efficiency of Cloud Monitoring
|
ملخص المشاركة:
|
Cloud computing
systems are often seen as dynamic pools of Virtual Machines (VM) installed on
provider side physical machines to be offered to Cloud users. Cloud customers
could use these Virtual Machines as services, platforms or as a whole
infrastructure.
However, in
practice the infrastructure of a computing Cloud includes several levels,
such as virtual gateways, virtual clusters and virtual nodes. In this paper,
we pursue a study of the impact of a hierarchical structure, formed of three
levels, on the process of monitoring the system with the main goal of
discovering symptoms of malicious behaviors in Clouds. We address in this
paper two major questions. First question refers to optimize the number of
clusters in the hierarchical structure to guarantee efficient monitoring. The
second question, posed in some previous papers in this area, concerns
efficient distributed implementation of the monitoring process; Namely, how
to choose locally the next VM to be visited by a Forensic Virtual Machines
(FVM) in a light and local way.
|
|
|
المؤتمر (6):
|
|
عنوان المؤتمر:
|
15th
European Conference on Cyber Warfare and Security
|
تاريخ الإنعقاد:
|
07/08/2016
|
مكان
الإنعقاد:
|
München, Germany
|
طبيعة المشاركة:
|
Paper presentation
|
عنوان المشاركة:
|
Balancing Mobility Algorithm for
Monitoring Virtual Machines in Clouds
|
ملخص المشاركة:
|
Amid the rapid growth of Internet users, cybercrime
is becoming one of the most challenging tasks for the systems and
applications designers to deal with. The cybercrime threat is reflected in
the increased number of cases and methods used by criminals. Systems based on
cloud computing are natural targets due to their complexity (greater room for
security weaknesses) and increasing popularity. Cloud computing is a modern
technology that enables users to share resources in a virtual storage and
computing environment. A cloud system is based on multiple physical servers.
It provides a universal environment with a (large) number of Virtual Machines
(VMs) that is available to many users accessing this system via the Internet.
This form of access makes cloud systems weaker than physical networks. In
order to prevent or minimize the number of attacks and in turn to secure data
storage, any malicious behaviour such as external undesirable interventions
should be rapidly identified and halted if possible. In this paper, we focus
on the discovery of malicious behaviour via determining unwanted symptoms
rather than via targeting particular malicious behaviours of the system
directly. The main contribution of this paper consists in several new
mechanisms for monitoring Virtual Machines and further experimental work
targeting efficient ways of visiting VMs in order to discover malicious
symptoms. We want to find the fastest and the best set of weights for
visiting VMs.
|
|
|
المؤتمر (7):
|
|
عنوان المؤتمر:
|
15th
European Conference on Cyber Warfare and Security (ECCWS2016)
|
تاريخ الإنعقاد:
|
07–08/07/2016
|
مكان
الإنعقاد:
|
Munich, Germany
|
طبيعة المشاركة:
|
Paper presentation
|
عنوان المشاركة:
|
Failure or Denial of Service? A Rethink
of the Cloud Recovery Model
|
ملخص المشاركة:
|
One of the dominant paradigms of cloud computing is
infrastructure as a service (IaaS), which allows organizations to outsource
computing equipment and resources such as servers, storage, networking, as
well as services such as load balancing and content delivery networks. For
vendors offering IaaS, load balancing is a critical aspect and selling point.
One component of load balancing is auto-scaling. This feature allows
applications to scale up and down dynamically based on load, performance and
‘health’ of a virtual machine (VM). It used to take years to grow businesses
to millions of customers but now this can happen in months or even days,
therefore the ability to access a seemingly infinite amount of resources on demand
is very appealing to businesses. The entire cloud model relies on dynamic
scalability and configurability because it is not practical to manually
configure on-demand services. In this paper we reconsider the scaling of
services on the cloud, and consider the definition of ‘healthy’ scaling, concept
vendors do not formally define. We also look at application layer denial of
service (DOS) attacks on application servers running compute services. While
there have been extensive efforts to defend the cloud against volumetric DOS
using network layer defences, detecting and preventing application layer DOS
attacks on the cloud is non-trivial due to the size of cloud and the
heterogeneity of applications running. We surveyed some of the key cloud providers
that offer IaaS such as Amazon Web Services, Windows Azure, Google Compute
Engine, Rack Space Open Cloud, and IBM Smart Cloud Enterprise. We
specifically analysed their auto-scaling features and looked at the cost
implications for customers. We ask the question, does the monitoring feature
of these services differentiate between load increase and Application Layer
DOS when making the decision to scale up its services VM?
|