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This paper focuses on the problem of machine learning classifier choice for network intrusion detection, taking into consideration several ensemble classifiers from the supervised learning category. We have evaluated Bagged trees, AdaBoost, RUSBoost, LogitBoost and GentleBoost algorithms, provided an analysis of the performance of the classifiers and compared their learning capabilities, taking for...
As network intrusion data's scale gets larger and larger, designing parallel schemes for intrusion detection have been becoming research focus in the field of information security. In order to solve the problem that the intrusion detection algorithm is high time-consuming, the classification of large amounts of data occupies lots of memory and the efficiency of single detection is low, a parallel...
In the world today, the security of the computer system is of great importance, And in the last few years, there have seen an affected growth in the amount of intrusions that intrusion detection has become the dominant of current information security. Firewalls cannot provide complete protection. Applying on a firewall system alone is not enough to prevent a corporate network from all types of network...
Innovation in the public-sector refers to the development of important improvements in the public administration and their corresponding services. One of such public services is the social security, of which central process has been the information security of their offered services. The aim of the present study has been the analysis of the trends and the discovery of behavioural patterns in the attacks...
The rapid growth in the volume and importance of web communication throughout the Internet has heightened the need for better security protection. Security experts, when protecting systems, maintain a database featuring signatures of a large number of attacks to assist with attack detection. However used in isolation, this can limit the capability of the system as it is only able to recognize known...
The excessive use of the communication networks, rising of Internet of Things leads to increases the vulnerability to the important and secret information. advance attacking techniques and number of attackers are increasing radically. Intrusion is one of the main threats to the internet. Hence security issues had been big problem, so that various techniques and approaches have been presented to address...
Everyday huge amount of information are transferred from one network to another, the information may be exposed to attacks. The information and information system should be protected from unauthorized users. To provide and maintain the Confidentiality and Integrity of the information is a very tedious job so Intrusion Detection plays a very important role. Although various methods are used to protect...
The security related issues in the Internet have drawn a constant attention due to rapid growth of attack and its devastating consequence. The essence of monitoring the networks and analyzing the packets coming from the network is to verify the authenticity of traffics. There have many Intrusion Detection & Prevention System (IDPS) techniques been proposed and accepted in the last few decades...
This paper analyzes the shortcomings of traditional multilayer BP neural network in intrusion detection, and gives an optimized multilayer BP neural network algorithm based on matrix, which has good readability, higher recognition rates and the faster program execution speed, etc. On the basis of this, the improved algorithm, combined with principal component analysis and other methods, is applied...
The presence of intrusion attack traces in network traffic pattern seems to be major threatening to cyber community. During a decade, many preventive and detection measures have had been developed to overcome these illicit activities but the evolution of zero-day exploits which has common behavior as intrusion traces find difficult to resolve the critics presence in network traffic patterns. The other...
With limited resource of nodes in Manets, achieving efficiency of a resilient and a secured network is always a challenge. Implementing any algorithm to enhance performance in such a node may result in lowering the lifetime of the network and affects the efficiency of the nodes adversely. Data regression on an incoming data in a node makes the Algorithm more efficient with respect to time and space...
In recent days it becomes very difficult task to manage security on large network and practically not possible to keep security scanner on every networks for protecting the system from illegal users. This problem can be solved with prevention systems which come in strategic role of Intrusion Prevention Systems that is taken as extended and enhanced version of IDS. By using this system we can upgrade...
Along with the increase of network attacks, network information security has become a globally concerned issue. At present, mainstream intrusion detection systems have the universal problems of massive alarm information and high false alarm rate. Therefore, a data mining technology is proposed in this article in order to reduce the quantity of the false alarms generated by intrusion detection systems...
Intrusion detection is concerned with monitoring and analysing events occurring in a computer system in order to discover potential malicious activity. Data mining, which is part of the procedure of knowledge discovery in databases, is the process of analysing the collected data to find patterns or correlations. As the amount of data collected, store and processed only increases, so does the significance...
As increase in the internet services and usage with open access to sensitive data, necessity of security to these systems had become a need of the hour. Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more crucial issue. To detect the attacks hitting the network it is very obligatory to properly monitor the flow...
Recent emerging growth of data created so many challenges in data mining. Data mining is the process of extracting valid, previously known & comprehensive datasets for the future decision making. As the improved technology by World Wide Web the streaming data come into picture with its challenges. The data which change with time & update its value is known as streaming data. As the...
In today's networked environment, massive volume of data being generated, gathered and stored in databases across the world. This trend is growing very fast, year after year. Today it is normal to find databases with terabytes of data, in which vital information and knowledge is hidden. The unseen information in such databases is not feasible to mine without efficient mining techniques for extracting...
This paper presents the results of the analysis of the network intrusion detection systems using data mining techniques and anomaly detection. Anomaly detection technique is present for a while in the area of data mining. Previous papers that implement data mining techniques to detect anomaly attacks actually use well-known techniques such as classification or clustering. Anomaly detection technique...
The most common method of IDS functioning is based on pattern matching which recognizes the vandalism occurring on the network using particular patterns and rules. In order to do so, normal behaviors of the network are modeled and then used as a base pattern for recognizing abnormal behavior of the network. The article, in hand, tries to recognize and choose the best algorithms for this operation...
Traditional network intrusion detection algorithms are time consuming due to the existence of redundant attributes. In order to improve the efficiency of network intrusion detection, in this paper, we propose a wavelet transform based support vector machine ensemble algorithm. Firstly, we use wavelet transform to remove the redundant attributes from the original dataset. Then we train a support vector...
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