ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

Toward Network-based DDoS Detection in Software-defined Networks

To combat susceptibility of modern computing systems to cyberattack, identifying and disrupting malicious traffic without human intervention is essential. To accomplish this, three main tasks for an effective intrusion detection system have been identified: monitor network traffic, categorize and identify anomalous behavior in near real time, and take appropriate action against the identified threat. This system leverages distributed SDN architecture and the principles of Artificial Immune Systems and Self-Organizing Maps to build a network-based intrusion detection system capable of detecting and terminating DDoS attacks in progress

An Evolutionary Algorithm to Generate Ellipsoid Detectors for Negative Selection

Negative selection is a process from the biological immune system that can be applied to two-class (self and nonself) classification problems. Negative selection uses only one class (self) for training, which results in detectors for the other class (nonself). This paradigm is especially useful for problems in which only one class is available for training, such as network intrusion detection. Previous work has investigated hyper-rectangles and hyper-spheres as geometric detectors. This work proposes ellipsoids as geometric detectors. First, the author establishes a mathematical model for ellipsoids. He develops an algorithm to generate ellipsoids by training on only one class of data. Ellipsoid mutation operators, an objective function, and a convergence technique are described for the evolutionary algorithm that generates ellipsoid detectors. Testing on several data sets validates this approach by showing that the algorithm generates good ellipsoid detectors. Against artificial data sets, the detectors generated by the algorithm match more than 90% of nonself data with no false alarms. Against a subset of data from the 1999 DARPA MIT intrusion detection data, the ellipsoids generated by the algorithm detected approximately 98% of nonself (intrusions) with an approximate 0% false alarm rate

Emergence of Major Pandemics: Examining the Use of AI for the Fight Against Covid-19

Covid-19 is an infectious disease caused by the SARS-CoV-2 virus and which is considered today as a global health emergency. Long before this pandemic, several others such as the plague of Athens, the plague of Antonine, the black plague, the Spanish flu, cholera, the Asian flu, AIDS raged, with consequences as fatal, even more serious than covid-19. The emergence of AI over the past ten years has brought it to the forefront of the response to this disease. The objective of this work is to present the significant contribution of AI in the fight against the new coronavirus, comparing it to previous large pandemics. A preliminary search of information related to past pandemics and covid-19 has been carried out. Next, the contribution of AI following the WHO framework for combating pandemics was presented. Finally, the discussion part resulted in the conclusion that if AI had already been fundamentally implemented during the time of the other major pandemics, the damage to human losses would have been less

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 349)

This bibliography lists 149 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April, 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment