5,682 research outputs found
Review of Detection Denial of Service Attacks using Machine Learning through Ensemble Learning
Today's network hacking is more resource-intensive because the goal is to prohibit the user from using the network's resources when the target is either offensive or for financial gain, especially in businesses and organizations. That relies on the Internet like Amazon Due to this, several techniques, such as artificial intelligence algorithms like machine learning (ML) and deep learning (DL), have been developed to identify intrusion and network infiltration and discriminate between legitimate and unauthorized users. Application of machine learning and ensemble learning algorithms to various datasets, consideration of homogeneous ensembles using a single algorithm type or heterogeneous ensembles using several algorithm types, and evaluation of the discovery outcomes in terms of accuracy or discovery error for detecting attacks. The survey literature provides an overview of the many approaches and approaches of one or more machine-learning algorithms used in various datasets to identify denial of service attacks. It has also been shown that employing the hybrid approach is the most common and produces better attack detection outcomes than using the sole approaches. Numerous machine learning techniques, including support vector machines (SVM), K-Nearest Neighbors (KNN), and ensemble learning like random forest (RF), bagging, and boosting, are illustrated in this work (DT). That is employed in several articles to identify different denial of service (DoS) assaults, including the trojan horse, teardrop, land, smurf, flooding, and worm. That attacks network traffic and resources to deny users access to the resources or to steal confidential information from the company without damaging the system and employs several algorithms to obtain high attack detection accuracy and low false alarm rates
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A survey of intrusion detection techniques in Cloud
Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks
An Efficient Analytical Solution to Thwart DDoS Attacks in Public Domain
In this paper, an analytical model for DDoS attacks detection is proposed, in
which propagation of abrupt traffic changes inside public domain is monitored
to detect a wide range of DDoS attacks. Although, various statistical measures
can be used to construct profile of the traffic normally seen in the network to
identify anomalies whenever traffic goes out of profile, we have selected
volume and flow measure. Consideration of varying tolerance factors make
proposed detection system scalable to the varying network conditions and attack
loads in real time. NS-2 network simulator on Linux platform is used as
simulation testbed. Simulation results show that our proposed solution gives a
drastic improvement in terms of detection rate and false positive rate.
However, the mammoth volume generated by DDoS attacks pose the biggest
challenge in terms of memory and computational overheads as far as monitoring
and analysis of traffic at single point connecting victim is concerned. To
address this problem, a distributed cooperative technique is proposed that
distributes memory and computational overheads to all edge routers for
detecting a wide range of DDoS attacks at early stage.Comment: arXiv admin note: substantial text overlap with arXiv:1203.240
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