1,041 research outputs found
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
DDoS Attack Detection Using Cooperative Overlay Networks and Gossip Protocol
DDoS attacks have major impact on the affected networks viz. packet transmission delays, network outage, website sabotage, financial losses, legitimate-user blockage and reputation damage. Existing DDoS detection techniques are either implemented at the victim node (but the damage is already done) or at many intermediate routers which run DDoS detection algorithms, that adds additional delay and more processing. We aim to detect DDoS attacks by using a new technique of cooperative overlay networks which overcomes the above problems by implementing the DDoS detection algorithm at one hop distance nodes (called defense nodes) from the victim.
DOI: 10.17762/ijritcc2321-8169.15062
Toward Network-based DDoS Detection in Software-defined Networks
To combat susceptibility of modern computing systems to cyberattack, identifying and disrupting malicious traffic without human intervention is essential. To accomplish this, three main tasks for an effective intrusion detection system have been identified: monitor network traffic, categorize and identify anomalous behavior in near real time, and take appropriate action against the identified threat. This system leverages distributed SDN architecture and the principles of Artificial Immune Systems and Self-Organizing Maps to build a network-based intrusion detection system capable of detecting and terminating DDoS attacks in progress
Machine Learning DDoS Detection for Consumer Internet of Things Devices
An increasing number of Internet of Things (IoT) devices are connecting to
the Internet, yet many of these devices are fundamentally insecure, exposing
the Internet to a variety of attacks. Botnets such as Mirai have used insecure
consumer IoT devices to conduct distributed denial of service (DDoS) attacks on
critical Internet infrastructure. This motivates the development of new
techniques to automatically detect consumer IoT attack traffic. In this paper,
we demonstrate that using IoT-specific network behaviors (e.g. limited number
of endpoints and regular time intervals between packets) to inform feature
selection can result in high accuracy DDoS detection in IoT network traffic
with a variety of machine learning algorithms, including neural networks. These
results indicate that home gateway routers or other network middleboxes could
automatically detect local IoT device sources of DDoS attacks using low-cost
machine learning algorithms and traffic data that is flow-based and
protocol-agnostic.Comment: 7 pages, 3 figures, 3 tables, appears in the 2018 Workshop on Deep
Learning and Security (DLS '18
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ForChaos: Real Time Application DDoS detection using Forecasting and Chaos Theory in Smart Home IoT Network
Recently, D/DoS attacks have been launched by zombie IoT devices in smart home networks. They pose a great threat to to network systems with Application Layer DDoS attacks being especially hard to detect due to their stealth and seemingly legitimacy. In this paper, we propose we propose ForChaos, a lightweight detection algorithm for IoT devices, that is based on forecasting and chaos theory to identify flooding and DDoS attacks. For every time-series behaviour collected, a forecasting-technique prediction is generated, based on a number of features, and the error between the two values is calcualted. In order to assess the error of the forecasting from the actual value, the lyapunov exponent is used to detect potential malicious behaviour. In NS-3 we evaluate our detection algorithm through a series of experiments in Flooding and Slow-Rate DDoS attacks. The results are presented and discussed in detail and compared with related studies, demonstrating its effectiveness and robustness
Multi-agent-based DDoS detection on big data systems
The Hadoop framework has become the most deployed platform for processing Big Data. Despite its advantages, Hadoop s infrastructure is still deployed within the secured network perimeter because the framework lacks adequate inherent security mechanisms against various security threats. However, this approach is not sufficient for providing adequate security layer against attacks such as Distributed Denial of Service. Furthermore, current work to secure Hadoop s infrastructure against DDoS attacks is unable to provide a distributed node-level detection mechanism. This thesis presents a software agent-based framework that allows distributed, real-time intelligent monitoring and detection of DDoS attack at Hadoop s node-level. The agent s cognitive system is ingrained with cumulative sum statistical technique to analyse network utilisation and average server load and detect attacks from these measurements. The framework is a multi-agent architecture with transducer agents that interface with each Hadoop node to provide real-time detection mechanism. Moreover, the agents contextualise their beliefs by training themselves with the contextual information of each node and monitor the activities of the node to differentiate between normal and anomalous behaviours. In the experiments, the framework was exposed to TCP SYN and UDP flooding attacks during a legitimate MapReduce job on the Hadoop testbed. The experimental results were evaluated regarding performance metrics such as false-positive ratio, false-negative ratio and response time to attack. The results show that UDP and TCP SYN flooding attacks can be detected and confirmed on multiple nodes in nineteen seconds with 5.56% false-positive ration, 7.70% false-negative ratio and 91.5% success rate of detection. The results represent an improvement compare to the state-of the-ar
Real time DDoS detection using fuzzy estimators
We propose a method for DDoS detection by constructing a fuzzy estimator on the mean packet inter arrival times. We divided the problem into two challenges, the first being the actual detection of the DDoS event taking place and the second being the identification of the offending IP addresses. We have imposed strict real time constraints for the first challenge and more relaxed constraints for the identification of addresses. Through empirical evaluation we confirmed that the detection can be completed within improved real time limits and that by using fuzzy estimators instead of crisp statistical descriptors we can avoid the shortcomings posed by assumptions on the model distribution of the traffic. In addition we managed to obtain results under a 3 sec detection window. © 2012 Elsevier Ltd. All rights reserved
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