A Neural-Visualization Ids for Honeynet Data

Abstract

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed.

The Association of Parental Interest in Entrepreneurship to the Entrepreneurial Interest of Spanish Youth

“…Decisions oriented towards increasing entrepreneurial intention and its transformation into entrepreneurial action necessarily have to consider the characteristics of the group in line with the suggestions of previous studies [4,[26][27][28]31]. In earlier works, the usefulness of PCA has been shown in multiple fields, ranging from the economic analysis of political risk [76] and in the area of computing [77] to the psychological aspects of entrepreneurship [36]. We contribute by applying this technique to the socio-educational analysis of entrepreneurship.…”

Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids

“…In the case of [24], several projectionist algorithms, such as PCA, CMLHL, CCA, and SOM network, have been applied to monitor the traffic of the Euskalert network (Honeynet data) [25], to discover behaviour and strategies indicative of an attack. In [26], the same techniques have been applied to GICAP-IDS and DARPA datasets [27], and their performance has been measured according to different variables, such as data volume, system dynamics and network traffic diversity, including first-time attacks (0-day).…”

Single-Trial Motor Imagery Classification Using Asymmetry Ratio, Phase Relation, Wavelet-Based Fractal, and Their Selected Combination

“…The active segments are filtered into the respective bands with pass-band spectral range of mu (8)(9)(10)(11)(12) and beta (14-28 Hz) rhythms using a Butterworth band-pass filter. The powers of specific spectral bands are calculated using the variance of filtered data.…”