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Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect the intrusion in Tor...
Nowadays, as information systems are more open to the Internet, the importance of secure networks is tremendously increased. Interconnected systems such as web server data servers are now under the threats of network attackers. Intrusion Detection System (IDS) is the most powerful system that can handle the intrusions of the computer environments by triggering alerts to make the analysts take actions...
At present, the issue of intrusion detection must be a hot point to all over the computer security area. In this paper, two novel intrusion detection techniques have been proposed. First, unlike the current existent detection methods, this paper combines the theories of both intuitionistic fuzzy sets (IFS) and artificial neural networks (ANN) together, which lead to much fewer iteration numbers, higher...
Millions of computers are infected with bot malware, form botnets and enable botmaster to perform malicious and criminal activities. Intrusion Detection Systems are deployed to detect infections, but they raise many correlated alerts for each infection, requiring a large manual investigation effort. This paper presents a novel method with a goal of determining which alerts are correlated, by applying...
Recently, anomaly-based intrusion detection system (IDS) is valuable methodology to protect target systems and networks against attacks. Modeling normal system/network behaviors enables anomaly-based IDS (Intrusion Detection System) to be extremely effective methodology to detect both known as well as unknown/new attacks. However, current software-based methods are difficult to process a large amount...
In this paper, we propose a hybrid method for intrusion detection which is based on k-means, naive-bayes and back propagation neural network (KBB). Initially we apply k-means which is partition-based, unsupervised cluster analysis method. In the form of clusters, we attain the gathered data which can be easily processed and learned by any machine learning algorithm. These outcomes are provided to...
Anomalous traffic detection on internet is a major issue of security as per the growth of smart devices and this technology. Several attacks are affecting the systems and deteriorate its computing performance. Intrusion detection system is one of the techniques, which helps to determine the system security, by alarming when intrusion is detected. In this paper performance of NSL-KDD dataset is evaluated...
Software-Defined Networking (SDN) is an emerging concept that intends to replace traditional networks by breaking vertical integration. It does so by separating the control logic of network from the underlying switches and routers, suggesting logical centralization of network control, and allowing to program the network. Although SDN promises more flexible network management, there are numerous security...
This paper focuses on an important research problem of Big Data classification in intrusion detection system. Deep Belief Networks is introduced to the field of intrusion detection, and an intrusion detection model based on Deep Belief Networks is proposed to apply in intrusion recognition domain. The deep hierarchical model is a deep neural network classifier of a combination of multilayer unsupervised...
The problem of intrusion is gradually becoming nightmare for several organizations. To protect the valuable data of their clients, organizations implement security systems to detect and prevent security breaches. But since the intruders are using sophisticated techniques to penetrate the systems, even the highly reputed secured systems have become vulnerable now. To deal with the current scenario,...
Computer Security has been discussed and improvised in many forms and using different techniques as well as technologies. The enhancements keep on adding as the security remains the fastest updating unit in a computer system. In this paper we propose a model for securing the system along with the network and enhance it more by applying machine learning techniques SVM (support vector machine) and ANN...
Security system is the immune system for computers which is similar to the immune system in the human body. This includes all operations required to protect computer and systems from intruders. The aim of this work is to develop an anomaly-based intrusion detection system (IDS) that can promptly detect and classify various attacks. Anomaly-based IDSs need to be able to learn the dynamically changing...
Intrusion detection and monitoring systems produce hundreds or even thousands of events every day. Unfortunately, most of these events are false positives, or irrelevant and can be considered as background noise, which makes their correlation, analysis and investigation very complicated and resource consuming. This paper presents modeling of background noise using the Non-Stationary time series analysis...
In recent years, the security has become a critical part of any organizational information systems. The intrusion detection system is an effective approach to deal with the problems of networks using various neural network classifiers. In this paper, the performance of intrusion detection with various neural network classifiers is compared. In the proposed research the three types of classifiers used...
Automatic detection of network intrusion is a challenging task because of increasing types of attacks. Many of the existing approaches either are rigid, inflexible designs tailored to a specific situation or require manual setting of design parameters such as the initial number of clusters. In this paper we allow the design parameters to be determined dynamically by adopting a layered hybrid architecture,...
Based on the advantages and disadvantages of the improved GA and LM algorithm, in this paper, the Hybrid Neural Network Algorithm (HNNA) is presented. Firstly, the algorithms use the advantage of the improved GA with strong whole searching capacity to search global optimal point in the whole question domain. Then, it adopts the strongpoint of the LM algorithm with fast local searching to fine search...
Nowadays with the dramatic growth in communication and computer networks, security has become a critical subject for computer systems. A good way to detect the illegal users is to monitoring these user's packets. Different algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize...
An Intrusion detection system is designed to classify the system activities into normal and abnormal. We use a combination of machine learning approaches as to detect the system attacks. The experimental results of the study show that increasing the number of classifiers has a threshold limit and the system accuracy will remain constant if the number of classifiers goes beyond this limit. The determination...
With fast growing cyber activities everyday, cyber attack has become a critical issue over the last decade. A number of cyber attack detection algorithms have been developed and applied in this field of study with different levels of success. In this paper, a new distributed cyber attack detection algorithm based on the decision cost minimization strategy is introduced. The proposed algorithm employs...
In this paper, an intrusion detection model is proposed based on multilayer perceptrons neural network . In this model, HISTORY is used to collect data. Then, the data stream is converted its' data structure for preprocessing. We use pattern matching module to filter out some of the known intrusions, in oder to reduce the load of the next step on intrusion detection, and the efficiency and accuracy...
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