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 t
heir 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.
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.
The traditional worms such as Blaster, Code Red, Slammer and Sasser, are still infecting vulnerable machines on the internet. They will remain as significant threats due to their fast spreading nature on the internet. Various traditional worms attack
pattern has been analyzed from various logs at different OSI layers such as victim logs, attacker logs and IDS alert log. These worms attack pattern can be abstracted to form worms attack model which describes the process of worms infection. For the purpose of this paper, only Blaster variants were used during the experiment. This paper proposes a multi-step worm attack model which can be extended into research areas in alert correlation and computer forensic investigation.
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.
