Predicting Network Attacks Using Ontology-Driven Inference

Graph knowledge models and ontologies are very powerful modeling and re asoning tools. We propose an effective approach to model network attacks and attack prediction which plays important roles in security management. The goals of this study are: First we model network attacks, their prerequisites and consequences using knowledge representation methods in order to provide description logic reasoning and inference over attack domain concepts. And secondly, we propose an ontology-based system which predicts potential attacks using inference and observing information which provided by sensory inputs. We generate our ontology and evaluate corresponding methods using CAPEC, CWE, and CVE hierarchical datasets. Results from experiments show significant capability improvements comparing to traditional hierarchical and relational models. Proposed method also reduces false alarms and improves intrusion detection effectiveness.Comment: 9 page

Towards a Computational Model of General Cognitive Control Using Artificial Intelligence, Experimental Psychology and Cognitive Neuroscience

Cognitive control is essential to human cognitive functioning as it allows us to adapt and respond to a wide range of situations and environments. The possibility to enhance cognitive control in a way that transfers to real life situations could greatly benefit individuals and society. However, the lack of a formal, quantitative definition of cognitive control has limited progress in developing effective cognitive control training programs. To address this issue, the first part of the thesis focuses on gaining clarity on what cognitive control is and how to measure it. This is accomplished through a large-scale text analysis that integrates cognitive control tasks and related constructs into a cohesive knowledge graph. This knowledge graph provides a more quantitative definition of cognitive control based on previous research, which can be used to guide future research. The second part of the thesis aims at furthering a computational understanding of cognitive control, in particular to study what features of the task (i.e., the environment) and what features of the cognitive system (i.e., the agent) determine cognitive control, its functioning, and generalization. The thesis first presents CogEnv, a virtual cognitive assessment environment where artificial agents (e.g., reinforcement learning agents) can be directly compared to humans in a variety of cognitive tests. It then presents CogPonder, a novel computational method for general cognitive control that is relevant for research on both humans and artificial agents. The proposed framework is a flexible, differentiable end-to-end deep learning model that separates the act of control from the controlled act, and can be trained to perform the same cognitive tests that are used in cognitive psychology to assess humans. Together, the proposed cognitive environment and agent architecture offer unique new opportunities to enable and accelerate the study of human and artificial agents in an interoperable framework. Research on training cognition with complex tasks, such as video games, may benefit from and contribute to the broad view of cognitive control. The final part of the thesis presents a profile of cognitive control and its generalization based on cognitive training studies, in particular how it may be improved by using action video game training. More specifically, we contrasted the brain connectivity profiles of people that are either habitual action video game players or do not play video games at all. We focused in particular on brain networks that have been associated with cognitive control. Our results show that cognitive control emerges from a distributed set of brain networks rather than individual specialized brain networks, supporting the view that action video gaming may have a broad, general impact of cognitive control. These results also have practical value for cognitive scientists studying cognitive control, as they imply that action video game training may offer new ways to test cognitive control theories in a causal way. Taken together, the current work explores a variety of approaches from within cognitive science disciplines to contribute in novel ways to the fascinating and long tradition of research on cognitive control. In the age of ubiquitous computing and large datasets, bridging the gap between behavior, brain, and computation has the potential to fundamentally transform our understanding of the human mind and inspire the development of intelligent artificial agents

CogPonder: Towards a Computational Framework of General Cognitive Control

Current computational models of cognitive control exhibit notable limitations. In machine learning, artificial agents are now capable of performing complex tasks but often ignore critical constraints such as resource limitations and how long it takes for the agent to make decisions and act. Conversely, cognitive control models in psychology are limited in their ability to tackle complex tasks (e.g., play video games) or generalize across a battery of simple cognitive tests. Here we introduce CogPonder, a flexible, differentiable, cognitive control framework that is inspired by the Test-Operate-Test-Exit (TOTE) architecture in psychology and the PonderNet framework in machine learning. CogPonder functionally decouples the act of control from the controlled processes by introducing a controller that acts as a wrapper around any end-to-end deep learning model and decides when to terminate processing and output a response, thus producing both a response and response time. Our experiments show that CogPonder effectively learns from data to generate behavior that closely resembles human responses and response times in two classic cognitive tasks. This work demonstrates the value of this new computational framework and offers promising new research prospects for both psychological and computer sciences

Temporary Self-Deprivation Can Impair Cognitive Control: Evidence From the Ramadan Fast

During Ramadan, people of Muslim faith fast by not eating or drinking between sunrise and sunset. This is likely to have physiological and psychological consequences for fasters, and societal and economic impacts on the wider population. We investigate whether, during this voluntary and temporally limited fast, reminders of food can impair the fasters' reaction time and accuracy on a non-food-related test of cognitive control. Using a repeated measures design in a sample of Ramadan fasters (N = 190), we find that when food is made salient, fasters are slower and less accurate during Ramadan compared with after Ramadan. Control participants perform similarly across time. Furthermore, during Ramadan performances vary by how recently people had their last meal. Potential mechanisms are suggested, grounded in research on resource scarcity, commitment, and thought suppression, as well as the psychology of rituals and self-regulation, and implications for people who fast for religious or health reasons are discussed

Decoding Hypnotic Experience from Raw EEG Data using a Multi-Output Auto-Encoder

In this study, we propose a novel approach for quantifying brain-to-brain coupling during a hypnosis induction. Our approach uses a multi-output sequence-to-sequence deep neural network applied to raw EEG data recorded from 51 participants using 59 electrodes. Specifically, we use a long short-term memory (LSTM) encoder to extract an embedding, which is then utilized for two downstream heads: one head to predict the hypnotist's brain activity, and the other head to classify the level of hypnotic depth. We found that removing the head that predicted the hypnotist's brain activity substantially decreased the accuracy of the classification head, indicating that this head plays a critical role in achieving better classification performance. These results highlight the importance of shared representations in shaping social interactions. Ultimately, this work can help us better understand the dynamics of verbal communication

Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control

Traditionally, theory and practice of Cognitive Control are linked via literature reviews by human domain experts. This approach, however, is inadequate to track the ever-growing literature. It may also be biased, and yield redundancies and confusion. Here we present an alternative approach. We performed automated text analyses on a large body of scientific texts to create a joint representation of tasks and constructs. More specifically, 385,705 scientific abstracts were first mapped into an embedding space using a transformers-based language model. Document embeddings were then used to identify a task-construct graph embedding that grounds constructs on tasks and supports nuanced meaning of the constructs by taking advantage of constrained random walks in the graph. This joint task-construct graph embedding, can be queried to generate task batteries targeting specific constructs, may reveal knowledge gaps in the literature, and inspire new tasks and novel hypotheses.Comment: 7 pages, 4 figures,CogSci2022 camera read

Training Cognition with Video Games

This chapter reviews the behavioral and neuroimaging scientific literature on the cognitive consequences of playing various genres of video games. The available research highlights that not all video games have similar cognitive impact; action video games as defined by first- and third-person shooter games have been associated with greater cognitive enhancement, especially when it comes to top-down attention, than puzzle or life-simulation games. This state of affairs suggests specific game mechanics need to be embodied in a video game for it to enhance cognition. These hypothesized game mechanics are reviewed; yet, the authors note that the advent of more complex, hybrid, video games poses new research challenges and call for a more systematic assessment of how specific video game mechanics relate to cognitive enhancement

Ontology-based modeling of DDoS attacks for attack plan detection

This paper proposes an effective approach to model DDoS attacks, and its application to recognize attack plans prior to the actual incident. The goals of this study are, firstly model DDoS attacks, their prerequisites and consequences using semantic representation in order to provide description logic of DDoS attacks; and secondly, propose an ontology-based solution which detects potential DDoS attacks using inference over observing knowledge provided by sensory inputs. Unlike other ontologies in network attack domains, proposed ontology is generated automatically using well-known taxonomies like CAPEC, CWE, and CVE datasets. Proposed method not only introduces semantic to exchange knowledge between machines, but also provides a framework by which machine can detect intrusions