Abstract-The goal of reaching a high level of security in wire-less and wired communication networks is continuously proving difficult to achieve. The speed at which both keepers and violators of secure networks are evolving is relatively close. Nowadays, network infrastructures contain a large number of event logs captured by Firewalls and Domain Controllers (DCs). However, these logs are increasingly becoming an obstacle for network administrators in analyzing networks for malicious activities. Forensic investigators mission to detect malicious activities and reconstruct incident scenarios is extremely complex considering the number, as well as the quality of these event logs. This paper presents the building blocks for a model for automated network readiness and awareness. The idea for this model is to utilize the current network security outputs to construct forensically comprehensive evidence. The proposed model covers the three vital phases of the cybercrime management chain, which are: 1) Forensics Readiness, 2) Active Forensics, and 3) Forensics Awareness.
Abstract-Network Forensics is a subtopic of Digital Forensics wherein research on artificat investigations and intrusions evidence acquisition is addressed. Among many challenges in the field, the problem of losing data artifacts in the state of flux, (i.e., live volatile data), when network devices are suddenly non-operational remains a topic of interest to many investigators. The main objective of this article is to simulate an SQL injection attack scenarios in a complex network environment. We designed and simulated a typical demilitarized zone (DMZ) network environment using graphical network simulator (GNS3), Virtual Box and VMware workstation. Using this set-up we are now able to simulate specific network devices configuration, perform SQL injection attacks against victim machines and collect network logs. The main motivation of our work is to finally define an attack pathway prediction methodology that makes it possible to examine the network artifacts collected in case network attacks.
On behalf of nomadic users and through the use of computing pocket devices, agents can efficiently operate in wireless networks, cooperate to resolve complex tasks and negotiate to reach agreements while attempting to maximize their utilities. However, the negotiation protocols intelligent agents use are hardly considering several of the requirements evolved after the increasing reliance on mobility. In this paper we present a new negotiation protocol that avoids the use of mediating agents and applies a voting-like mechanism to handle service requests of nomadic users in wireless networks. We examine our approach in a scenario where it is essential for a multi-agent system to establish a chain of mutually attracted agents seeking to fulfill different bartering desires. We compare the results obtained with those produced after using an adjusted version of the Strategic Negotiation Model.
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In environments where robotic systems are deployed people often have different requirements for the robotic services and human-robot interaction methods. This paper presents a robotic system that exploits the advantages of ubiquitous perception in order to gather knowledge from multiple sensors and various modalities. This ubiquitous human perception will facilitate user profiling in order to support personalised services and individual human-robot interaction. This system combines ubiquitous smart sensing, methods of multi-modal human perception and existing human recognition algorithms from the field of biometrics to collectively work towards a real-time, robust and scalable solution for gender estimation.
Electroencephalograms are brain-computer interfaces that consist of a series of conductors placed on the scalp, using machine-learning techniques, the P300 signal can be classified and used to command ubiquitous robotic systems. For both able-bodied and disabled subjects, the collection of training data can be an exhaustive exercise. It is the goal of this work-in-progress to substitute an extended training phase with a more generalized approach involving electroencephalogram data from multiple subjects, in an attempt to eliminate classification redundancy.
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