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This paper designs a high-speed network data acquisition system based on big data platform with good expansibility and high real-time, aiming at the need of enterprises or organizations to acquire high-speed network data efficiently in network intrusion detection. Taking into account the storage capacity and computing power requirements of high-speed network traffic capture and processing, the whole...
Machine learning and data mining techniques have been widely used in order to improve network intrusion detection in recent years. These techniques make it possible to automate anomaly detection in network traffics. One of the major problems that researchers are facing is the lack of published data available for research purposes. The KDD'99 dataset was used by researchers for over a decade even though...
With the rapidly growth of technology, used Internet become an important part in human life it used in many sectors of society, communicating over global network, sending or receiving sensitive data is risk because different techniques are used by attackers to intercept and exposed data. As a result, strong security technique is required to guaranteeing the user data. Many methods proposed to improve...
We propose a traffic jam prediction method based on mining frequent patterns correlated to traffic jams. For traffic jam prediction at a given sensor, first, we apply a one-dimensional clustering scheme to identify automatically which sensors are and in what degree correlated to the given sensor in terms that certain volume values with a compact distribution co-occur frequently with the traffic jams...
The current network anomaly traffic detection technologies usually focus on the rules matching and statistical method which are suitable for the general network environment. For the communication characteristics of the controlled network environment, this paper puts forward a network anomaly traffic detection method based on the flow template, which captures and analyses the real-time network traffic...
Botnet detection plays an important role in network security. Botnet are collection of compromised computers called the bot. For detecting the presence of bots in a network, there are many detection techniques available. Network based detection method is the one of the efficient method in detecting bots. Paper reviews four different botnet detection techniques and a comparison of all these techniques...
Network Intrusion Detection Systems must effectively identify security threats and protect the applications. The focus of the paper is the presence of class imbalance problem in intrusion datasets. An efficient intrusion detection system must accurately identify all threats even if they form a small fraction of the intrusion data. The effect of class imbalance on the benchmark NSL_KDD dataset is evaluated...
Recently, sophisticated attacks are increased against specific business companies, organizations and various facilities and the attackers are trying to remove attack traces such as system logs and related information on the victim systems. Therefore, it is getting more difficult to collect the information for attack analysis. In order to overcome this situations, companies and organizations have started...
Multi-stage attacks can evolve dramatically, causing much loss and damage to organisations. These attacks are frequently instigated by exploiting actions, which in isolation are legal, and are therefore particularly challenging to detect. Much research has been conducted in the multi-stage detection area, in order to build a framework based on an events correlation approach. This paper proposes a...
In order to present the status of the electric power communication network, information collection, presentation and business flow analysis prediction technology are discussed in this project. The realized platform can show the integration of data visualization image rendering and give the business flow prediction.
One of the main challenges in the mobile ad hoc network is to ensure secure communication, because of its dynamic topology and lack of centralised control. In this study, the authors propose a parallel key management scheme which combines the cluster-based key management and the partially distributed key management approach. The network is partitioned into clusters, where the cluster head (CH) plays...
The value of Intrusion Detection System (IDS) traces is based on being able to meaningfully parse the complex data patterns appearing therein as based on the pre-defined intrusion 'detection' rule sets. As IDS traces monitor large groups of servers, large amounts of network data and also spanning a variety of patterns, efficient analytical approaches are needed to address this big heterogeneous data...
User online shopping preference mining is the key point on user found, e-commerce marketing and user personalized recommendation. A method for Online shopping preference analysis based on MapReduce is proposed in this paper. The campus network traffic is analyzed using MapReduce model, in which the features of user online shopping behavior are extracted by four MapReduce jobs using deep packet inspection...
Network traffic prediction for academic organizations is essential to managing and selecting the best routing path. Since overload traffic is a major problem that delays data transmission in network system and causes some data loss, this research demonstrates an approach to predict network traffic on data transmission in the network system by using association rule discovery which is one of the data...
Concept drifting poses a real challenge for network models which depends on statistical heuristics learned from the data stream, for example Anomaly Based Detection/Prevention Systems. These models tend to become inconsistent over a period of time as the underlying data stream like network traffic tends to change and get affected by evolution of concept drift. Change in network traffic pattern is...
Online network anomaly-based intrusion detection systems responsible about monitoring the novel anomalies. Network anomaly detection system architecture with a new outlier detection approach is presented in this paper. A new outlierness measurement is proposed which is based on frequent patterns technique and an approach for detecting outliers is introduced. The proposed approach features main advantages...
This paper introduces a novel approach for anomaly detection. The solution consists of an automatic detection system that operates without the need of network administrator intervention. Network IP flows are modeled by a graph and Tsallis entropy is applied in order to detect anomalies. Furthermore, our solution can extract and present detailed information from the network traffic. It provides to...
In a network environment, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource. Due to increasing incidents of cyber-attacks, building intrusion detection systems (IDSs) remains a priority for protecting information systems security. Intrusion detection does not include prevention of intrusions. IDS should be fast...
Network anomaly detection aims to detect patterns in a given network traffic data that do not conform to an established normal behavior. Distinguishing different anomaly patterns from large amount of data can be a challenge, let alone visualizing them in a comparative perspective. Recently, the unsupervised learning method such as the K-means [3], self-organizing map (SOM) [2], and growing hierarchical...
Reducing latency for accessing web objects is a major challenge in Proxy Server and various techniques such as web caching and Web pre-fetching is used for it. In this paper we have integrated the approach of web caching and pre-fetching using sequential data mining techniques to enhance the proxy server's performance. The web access logs collected at squid proxy servers, can be used derive interesting...
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