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تسجيل مستخدم جديدAdvanced Trace Pattern For Computer Intrusion Discovery
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The number of crime committed based on the malware intrusion is never ending as the number of malware variants is growing tremendously and the usage of internet is expanding globally. Malicious codes easily obtained and use as one of weapon to gain their objective illegally. Hence, in this research, diverse logs from different OSI layer are explored to identify the traces left on the attacker and victim logs in order to establish worm trace pattern to defending against the attack and help revealing true attacker or victim. For the purpose of this paper, it focused on malware intrusion and traditional worm namely sasser worm variants. The concept of trace pattern is created by fusing the attackers and victims perspective. Therefore, the objective of this paper is to propose a general worm trace pattern for attackers, victims and multi-step (attacker/victim)s by combining both perspectives. These three proposed worm trace patterns can be extended into research areas in alert correlation and computer forensic investigation.
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The number of malware variants is growing tremendously and the study of malware attacks on the Internet is still a demanding research domain. In this research, various logs from different OSI layer are explore to identify the traces leave on the atta
cker and victim logs, and the attack worm trace pattern are establish in order to reveal true attacker or victim. For the purpose of this paper, it will only concentrate on cybercrime that caused by malware network intrusion and used the traditional worm namely blaster worm variants. This research creates the concept of trace pattern by fusing the attackers and victims perspective. Therefore, the objective of this paper is to propose on attackers, victims and multistep, attacker or victim, trace patterns by combining both perspectives. These three proposed worm trace patterns can be extended into research areas in alert correlation and computer forensic investigation.
As one of the solutions to intrusion detection problems, Artificial Immune Systems (AIS) have shown their advantages. Unlike genetic algorithms, there is no one archetypal AIS, instead there are four major paradigms. Among them, the Dendritic Cell Al
gorithm (DCA) has produced promising results in various applications. The aim of this chapter is to demonstrate the potential for the DCA as a suitable candidate for intrusion detection problems. We review some of the commonly used AIS paradigms for intrusion detection problems and demonstrate the advantages of one particular algorithm, the DCA. In order to clearly describe the algorithm, the background to its development and a formal definition are given. In addition, improvements to the original DCA are presented and their implications are discussed, including previous work done on an online analysis component with segmentation and ongoing work on automated data preprocessing. Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection.
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques
. In this paper, we present a simple technique based on incremental learning of support vector machines in order to rank the features in real time within a streaming model for network data. Some illustrative numerical experiments with two popular benchmark datasets show that our approach allows to adapt to the changes in normal network behaviour and novel attack patterns which have not been experienced before.
Internet has played a vital role in this modern world, the possibilities and opportunities offered are limitless. Despite all the hype, Internet services are liable to intrusion attack that could tamper the confidentiality and integrity of important
information. An attack started with gathering the information of the attack target, this gathering of information activity can be done as either fast or slow attack. The defensive measure network administrator can take to overcome this liability is by introducing Intrusion Detection Systems (IDSs) in their network. IDS have the capabilities to analyze the network traffic and recognize incoming and on-going intrusion. Unfortunately the combination of both modules in real time network traffic slowed down the detection process. In real time network, early detection of fast attack can prevent any further attack and reduce the unauthorized access on the targeted machine. The suitable set of feature selection and the correct threshold value, add an extra advantage for IDS to detect anomalies in the network. Therefore this paper discusses a new technique for selecting static threshold value from a minimum standard features in detecting fast attack from the victim perspective. In order to increase the confidence of the threshold value the result is verified using Statistical Process Control (SPC). The implementation of this approach shows that the threshold selected is suitable for identifying the fast attack in real time.
This paper introduces a new similarity measure, the covering similarity, that we formally define for evaluating the similarity between a symbolic sequence and a set of symbolic sequences. A pair-wise similarity can also be directly derived from the c
overing similarity to compare two symbolic sequences. An efficient implementation to compute the covering similarity is proposed that uses a suffix tree data-structure, but other implementations, based on suffix array for instance, are possible and possibly necessary for handling large scale problems. We have used this similarity to isolate attack sequences from normal sequences in the scope of Host-based Intrusion Detection. We have assessed the covering similarity on two well-known benchmarks in the field. In view of the results reported on these two datasets for the state of the art methods, and according to the comparative study we have carried out based on three challenging similarity measures commonly used for string processing or in bioinformatics, we show that the covering similarity is particularly relevant to address the detection of anomalies in sequences of system calls
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