A cognitive based Intrusion detection system

Intrusion detection is one of the primary mechanisms to provide computer networks with security. With an increase in attacks and growing dependence on various fields such as medicine, commercial, and engineering to give services over a network, securing networks have become a significant issue. The purpose of Intrusion Detection Systems (IDS) is to make models which can recognize regular communications from abnormal ones and take necessary actions. Among different methods in this field, Artificial Neural Networks (ANNs) have been widely used. However, ANN-based IDS, has two main disadvantages: 1- Low detection precision. 2- Weak detection stability. To overcome these issues, this paper proposes a new approach based on Deep Neural Network (DNN. The general mechanism of our model is as follows: first, some of the data in dataset is properly ranked, afterwards, dataset is normalized with Min-Max normalizer to fit in the limited domain. Then dimensionality reduction is applied to decrease the amount of both useless dimensions and computational cost. After the preprocessing part, Mean-Shift clustering algorithm is the used to create different subsets and reduce the complexity of dataset. Based on each subset, two models are trained by Support Vector Machine (SVM) and deep learning method. Between two models for each subset, the model with a higher accuracy is chosen. This idea is inspired from philosophy of divide and conquer. Hence, the DNN can learn each subset quickly and robustly. Finally, to reduce the error from the previous step, an ANN model is trained to gain and use the results in order to be able to predict the attacks. We can reach to 95.4 percent of accuracy. Possessing a simple structure and less number of tunable parameters, the proposed model still has a grand generalization with a high level of accuracy in compared to other methods such as SVM, Bayes network, and STL.Comment: 18 pages, 6 figure

Cognitive performance in multiple system atrophy

The cognitive performance of a group of patients with multiple system atrophy (MSA) of striato-nigral predominance was compared with that of age and IQ matched control subjects, using three tests sensitive to frontal lobe dysfunction and a battery sensitive to memory and learning deficits in Parkinson's disease and dementia of the Alzheimer type. The MSA group showed significant deficits in all three of the tests previously shown to be sensitive to frontal lobe dysfunction. Thus, a significant proportion of patients from the MSA group failed an attentional set-shifting test, specifically at the stage when an extra-dimensional shift was required. They were also impaired in a subject-ordered test of spatial working memory. The MSA group showed deficits mostly confined to measures of speed of thinking, rather than accuracy, on the Tower of London task. These deficits were seen in the absence of consistent impairments in language or visual perception. Moreover, the MSA group showed no significant deficits in tests of spatial and pattern recognition previously shown to be sensitive to patients early in the course of probable Alzheimer's disease and only a few patients exhibited impairment on the Warrington Recognition Memory Test. There were impairments on other tests of visual memory and learning relative to matched controls, but these could not easily be related to fundamental deficits of memory or learning. Thus, on a matching-to-sample task the patients were impaired at simultaneous but not delayed matching to sample, whereas difficulties in a pattern-location learning task were more evident at its initial, easier stages. The MSA group showed no consistent evidence of intellectual deterioration as assessed from their performance on subtests of the Wechsler Adult Intelligence Scale (WAIS) and the National Adult Reading Test (NART). Consideration of individual cases showed that there was some heterogeneity in the pattern of deficits in the MSA group, with one patient showing no impairment, even in the face of considerable physical disability. The results show a distinctive pattern of cognitive deficits, unlike those previously seen using the same tests in patients with Parkinson's and Alzheimer's diseases, and suggesting a prominent frontal-lobe-like component. The implications for concepts of 'subcortical' dementia and 'fronto-striatal' cognitive dysfunction are considered

Proposing a new focus for the study of natural and artificial cognitive systems

In the study of systems the function of the system is often a good hint to how it works. In the following paper I would like to suggest that in studying or modeling a cognitive system our pre-knowledge of their functions should be treated carefully. We should focus on the statistical distribution of the system's environment and the ways this distribution affects the behavior and development of the cognitive system. I will show an example of how such a focus changes the view of the immune system. I would also like to show how this new outlook on the study of cognitive systems could affect attempts at creating artifcial cognitive system

Formulating the cognitive design problem of air traffic management

Evolutionary approaches to cognitive design in the air traffic management (ATM) system can be attributed with a history of delayed developments. This issue is well illustrated in the case of the flight progress strip where attempts to design a computer-based system to replace the paper strip have consistently been met with rejection. An alternative approach to cognitive design of air traffic management is needed and this paper proposes an approach centred on the formulation of cognitive design problems. The paper gives an account of how a cognitive design problem was formulated for a simulated ATM task performed by controller subjects in the laboratory. The problem is formulated in terms of two complimentary models. First, a model of the ATM domain describes the cognitive task environment of managing the simulated air traffic. Second, a model of the ATM worksystem describes the abstracted cognitive behaviours of the controllers and their tools in performing the traffic management task. Taken together, the models provide a statement of worksystem performance, and express the cognitive design problem for the simulated system. The use of the problem formulation in supporting cognitive design, including the design of computer-based flight strips, is discussed

Adaptive OFDM System Design For Cognitive Radio

Recently, Cognitive Radio has been proposed as a promising technology to improve spectrum utilization. A highly flexible OFDM system is considered to be a good candidate for the Cognitive Radio baseband processing where individual carriers can be switched off for frequencies occupied by a licensed user. In order to support such an adaptive OFDM system, we propose a Multiprocessor System-on-Chip (MPSoC) architecture which can be dynamically reconfigured. However, the complexity and flexibility of the baseband processing makes the MPSoC design a difficult task. This paper presents a design technology for mapping flexible OFDM baseband for Cognitive Radio on a multiprocessor System-on-Chip (MPSoC)

The immune system and other cognitive systems

In the following pages we propose a theory on cognitive systems and the common strategies of perception, which are at the basis of their function. We demonstrate that these strategies are easily seen to be in place in known cognitive systems such as vision and language. Furthermore we show that taking these strategies into consideration implies a new outlook on immune function calling for a new appraisal of the immune system as a cognitive system

Using Pupil Diameter to Measure Cognitive Load

In this paper, we will present a method for measuring cognitive load and online real-time feedback using the Tobii Pro 2 eye-tracking glasses. The system is envisaged to be capable of estimating high cognitive load states and situations, and adjust human-machine interfaces to the user's needs. The system is using well-known metrics such as average pupillary size over time. Our system can provide cognitive load feedback at 17-18 Hz. We will elaborate on our results of a HRI study using this tool to show it's functionality.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606