Experimental analysis of application-level intrusion detection algorithms

Dong, Yuhong; Hsu, Sam; Rajput, Saeed; Wu, Bing

doi:10.1504/ijsn.2010.032218

Cited by 16 publications

(4 citation statements)

References 7 publications

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“…In this approach, the standard profile is made over a "session learning" method, which can be used to know an anomaly session. Warusia and Udzir (2014) [12] purpose novel "Signature Based Anomaly Detection Scheme" (SADS) which applied to learn packet headers performance patterns more accurately is proposed. Mixing data mining algorithms like Naive Bayes and Random Forest can be used to make low false alarms and reducing processing time.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Intrusion Detection Method to Increase Anomaly Detection by Using Data Mining Techniques

Borjali¹,

Wang²,

Hassan³

2016

IJDTA

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An Intrusion Detection System is an application which observes movements or action happen on the network and determine it for any kind of harmful activity that can disturb computer security policy. With progress of increase the usage rate of the internet, there is a widely increase in the number of internet attacks as well, so contests arise towards the network security due to the arrival of new approaches of attacks. To classify these attacks, a new hybrid method with the help of data mining based on decision tree C4.5 and Meta algorithm is planned. This method gives a classifier which expands the whole accuracy of detection. Many data mining techniques have been settled for detecting intrusion. For recognition of anomalies a hybrid technique based on decision tree C4.5 with Meta algorithm is offered that provides better accuracy and reduces the problem of high false alarm ratio. The assessment of the given approach is made with other data mining techniques. With this given approach detection rate is improved significantly. KDD Cup 1999 dataset use for experimental work.

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Section: Literature Surveymentioning

confidence: 99%

Hybrid Intrusion Detection Method to Increase Anomaly Detection by Using Data Mining Techniques

Borjali¹,

Wang²,

Hassan³

2016

IJDTA

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“…There are many related research studies in the following topics: various attacks and vulnerabilities , key management , traceback , elliptic curve cryptosystem intrusion detection , radio frequency identification security , security in wireless networks , trust , authentication , security in peer‐to‐peer networks , and other security issues .…”

Section: Introductionmentioning

confidence: 99%

A survey of cyber crimes

Zhang

Xiao

Ghaboosi

et al. 2011

Security Comm Networks

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With the advancement of computer and information technology, cyber crime is now becoming one of the most significant challenges facing law enforcement organizations. Cyber crimes are generally referred as criminal activities that use computers or networks. An understanding of the characteristics and nature of cyber crimes is important in helping research communities find ways to effectively prevent them. Most existing research focuses more on attacks and attack models, including either actual attacks or imaginary/possible attacks over all layers of networks or computers, but there has been less work carried out on a comprehensive survey of cyber crimes. This paper provides a survey of cyber crimes that have actually occurred. First, cyber crimes in the digital world are compared with crimes in the physical world. Then, cyber crimes are categorized according to the roles of computers or networks. Furthermore, we also notice that some cyber crimes are actually traditionally non‐cyber crimes that are facilitated by computers or networks. It is surprising that there are so many recurrent cyber crimes. More efforts are needed to protect people from cyber crimes. Copyright © 2011 John Wiley & Sons, Ltd.

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“…Our other related work includes temporal accountability , accountability for wireless networks , logging , accountability in smart grids , and quantitative study of accountability . There are also many related work in network security .…”

Section: Introductionmentioning

confidence: 99%

Accountability using flow‐net: design, implementation, and performance evaluation

Xiao

Meng

Takahashi

2011

Security Comm Networks

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Accountability is a very important topic for computer and networking systems. It helps to answer questions such as, "What happened?" and, "Who did it?" These two questions are also related to forensics; however, forensics normally tries to answer these questions by adding some human factors (such as a guess or an instinct due to missing evidence, as well as human involvements) under the available system. Accountability, on the other hand, can only be achieved by significantly improving the current system with the result that forensics becomes trivial in an accountable system. Furthermore, each entity in the system must be held responsible for its activities. In order to provide accountability, a better logging system is necessary so that not only their activities but also their relationships may be captured. To this end, our previous work proposed a novel logging mechanism, flow-net methodology, for accountability. In this paper, we extend the flow-net methodology and present its design and implementation in wireless networks. We also evaluate the performance of flownet and compare it with that of audit log files.

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Experimental analysis of application-level intrusion detection algorithms

Cited by 16 publications

References 7 publications

Hybrid Intrusion Detection Method to Increase Anomaly Detection by Using Data Mining Techniques

Hybrid Intrusion Detection Method to Increase Anomaly Detection by Using Data Mining Techniques

A survey of cyber crimes

Accountability using flow‐net: design, implementation, and performance evaluation

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