ﻻ يوجد ملخص باللغة العربية
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.
The current paper addresses relevant network security vulnerabilities introduced by network devices within the emerging paradigm of Internet of Things (IoT) as well as the urgent need to mitigate the negative effects of some types of Distributed Deni
Cybersecurity is a domain where the data distribution is constantly changing with attackers exploring newer patterns to attack cyber infrastructure. Intrusion detection system is one of the important layers in cyber safety in todays world. Machine le
A novel approach to analyze statistically the network traffic raw data is proposed. The huge amount of raw data of actual network traffic from the Intrusion Detection System is analyzed to determine if a traffic is a normal or harmful one. Using the
Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catas
Network intrusion is a well-studied area of cyber security. Current machine learning-based network intrusion detection systems (NIDSs) monitor network data and the patterns within those data but at the cost of presenting significant issues in terms o