3,901 research outputs found
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An autonomic approach to denial of service defence
Denial of service attacks, viruses and worms are com- mon tools for malicious adversarial behaviour in networks. In this paper we propose the use of our autonomic routing protocol, the Cognitive Packet Network (CPN), as a means to defend nodes from Distributed Denial of Service Attacks (DDoS), where one or more attackers generate flooding traffic from multiple sources towards selected nodes or IP addresses. We use both analytical and simulation mod- elling, and experiments on our CPN testbed, to evaluate the advantages and disadvantages of our approach in the pres- ence of imperfect detection of DDoS attacks, and of false alarms
Preventing Distributed Denial-of-Service Attacks on the IMS Emergency Services Support through Adaptive Firewall Pinholing
Emergency services are vital services that Next Generation Networks (NGNs)
have to provide. As the IP Multimedia Subsystem (IMS) is in the heart of NGNs,
3GPP has carried the burden of specifying a standardized IMS-based emergency
services framework. Unfortunately, like any other IP-based standards, the
IMS-based emergency service framework is prone to Distributed Denial of Service
(DDoS) attacks. We propose in this work, a simple but efficient solution that
can prevent certain types of such attacks by creating firewall pinholes that
regular clients will surely be able to pass in contrast to the attackers
clients. Our solution was implemented, tested in an appropriate testbed, and
its efficiency was proven.Comment: 17 Pages, IJNGN Journa
Intrusion detection routers: Design, implementation and evaluation using an experimental testbed
In this paper, we present the design, the implementation details, and the evaluation results of an intrusion detection and defense system for distributed denial-of-service (DDoS) attack. The evaluation is conducted using an experimental testbed. The system, known as intrusion detection router (IDR), is deployed on network routers to perform online detection on any DDoS attack event, and then react with defense mechanisms to mitigate the attack. The testbed is built up by a cluster of sufficient number of Linux machines to mimic a portion of the Internet. Using the testbed, we conduct real experiments to evaluate the IDR system and demonstrate that IDR is effective in protecting the network from various DDoS attacks. © 2006 IEEE.published_or_final_versio
On the Efficacy of Live DDoS Detection with Hadoop
Distributed Denial of Service flooding attacks are one of the biggest
challenges to the availability of online services today. These DDoS attacks
overwhelm the victim with huge volume of traffic and render it incapable of
performing normal communication or crashes it completely. If there are delays
in detecting the flooding attacks, nothing much can be done except to manually
disconnect the victim and fix the problem. With the rapid increase of DDoS
volume and frequency, the current DDoS detection technologies are challenged to
deal with huge attack volume in reasonable and affordable response time.
In this paper, we propose HADEC, a Hadoop based Live DDoS Detection framework
to tackle efficient analysis of flooding attacks by harnessing MapReduce and
HDFS. We implemented a counter-based DDoS detection algorithm for four major
flooding attacks (TCP-SYN, HTTP GET, UDP and ICMP) in MapReduce, consisting of
map and reduce functions. We deployed a testbed to evaluate the performance of
HADEC framework for live DDoS detection. Based on the experiments we showed
that HADEC is capable of processing and detecting DDoS attacks in affordable
time
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Dynamic Adaptation of Temporal Event Correlation Rules
Temporal event correlation is essential to realizing self-managing distributed systems. Autonomic controllers often require that events be correlated across multiple components using rule patterns with timer-based transitions, e.g., to detect denial of service attacks and to warn of staging problems with business critical applications. This short paper discusses automatic adjustment of timer values for event correlation rules, in particular compensating for the variability of event propagation delays due to factors such as contention for network and server resources. We describe a corresponding Management Station architecture and present experimental studies on a testbed system that suggest that this approach can produce results at least as good as an optimal fixed setting of timer values
Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications
Social Virtual Reality based Learning Environments (VRLEs) such as vSocial
render instructional content in a three-dimensional immersive computer
experience for training youth with learning impediments. There are limited
prior works that explored attack vulnerability in VR technology, and hence
there is a need for systematic frameworks to quantify risks corresponding to
security, privacy, and safety (SPS) threats. The SPS threats can adversely
impact the educational user experience and hinder delivery of VRLE content. In
this paper, we propose a novel risk assessment framework that utilizes attack
trees to calculate a risk score for varied VRLE threats with rate and duration
of threats as inputs. We compare the impact of a well-constructed attack tree
with an adhoc attack tree to study the trade-offs between overheads in managing
attack trees, and the cost of risk mitigation when vulnerabilities are
identified. We use a vSocial VRLE testbed in a case study to showcase the
effectiveness of our framework and demonstrate how a suitable attack tree
formalism can result in a more safer, privacy-preserving and secure VRLE
system.Comment: Tp appear in the CCNC 2019 Conferenc
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