16,721 research outputs found
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
Multi-Layer Cyber-Physical Security and Resilience for Smart Grid
The smart grid is a large-scale complex system that integrates communication
technologies with the physical layer operation of the energy systems. Security
and resilience mechanisms by design are important to provide guarantee
operations for the system. This chapter provides a layered perspective of the
smart grid security and discusses game and decision theory as a tool to model
the interactions among system components and the interaction between attackers
and the system. We discuss game-theoretic applications and challenges in the
design of cross-layer robust and resilient controller, secure network routing
protocol at the data communication and networking layers, and the challenges of
the information security at the management layer of the grid. The chapter will
discuss the future directions of using game-theoretic tools in addressing
multi-layer security issues in the smart grid.Comment: 16 page
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