No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
NSNN Algorithm Performance with Different Neural Network Architectures
The Internet of Things (IoT) has rapidly changed information systems and networks in a significant way. Traditional networks are experiencing exponential increases in data volume, velocity, and variety. With the intermingling of IoT devices and legacy systems, new security threats are becoming more prevalent and severe. Successful attacks can cause significant damage or disruption to critical infrastructures or theft of private data through previously nonexistent attack vectors.
In response, existing security solutions must be adapted, or new countermeasures must be created to address the unique threats presented by IoT. Therefore, we propose the Blacksite framework for a novel adaptive real-time intrusion detection in IoT networks using human intelligence integrated into an Artificial Immune System with Deep Neural Network-based validation model. We recommend a solution that can address unique challenges of IoT
networks and present implementation strategies as well as a pilot implementation of the core component (deep neural network model for malicious traffic classification) of Blacksite. The proposed framework is designed to rapidly respond to attacks and adapt to changing network environments. Blacksite serves as a foundation for further development of holistic IoT intrusion detection solutions, wherein, each node contributes to the security of the network.
The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.
Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algorithm in general, when applied to anomaly detection. A straightforward self detector classification principle is proposed and its classification performance is compared to a real-valued negative selection algorithm and to a one-class support vector machine. Earlier work suggests that real-value negative selection requires a single class to learn from. The investigations presented in this paper reveal, however, that when applied to anomaly detection, the real-valued negative selection and self detector classification techniques require positive and negative examples to achieve a high classification accuracy. Whereas, one-class SVMs only require examples from a single class.
The book is a collection of invited papers on Constructive methods for Neural networks. Most of the chapters are extended versions of works presented on the special session on constructive neural network algorithms of the 18th International Conference on Artificial Neural Networks (ICANN 2008) held September 3-6, 2008 in Prague, Czech Republic. The book is devoted to constructive neural networks and other incremental learning algorithms that constitute an alternative to standard trial and error methods for searching adequate architectures. It is made of 15 articles which provide an overview of the most recent advances on the techniques being developed for constructive neural networks and their applications. It will be of interest to researchers in industry and academics and to post-graduate students interested in the latest advances and developments in the field of artificial neural networks.
This paper researches on problems of improving the stability of feature selection algorithm. A bagging-based selective results ensemble method is proposed. First use a feature selection algorithm and different training subsets to select several feature subsets. Then compute weights of each selected feature subset by mutual information and classifying accuracy. At last use a bagging-based method to assemble the selective subsets. Experiments in intrusion detection data of KDD cup'99 show that this algorithm could obtain better results.
The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. We describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses. 1 Introduction The problem of ensuring the security of computer systems includes such activities as detecting unauthorized use of computer facilities, guaranteeing the integrity of data files, and preventing the spread of computer viruses. In this paper, we view these protection problems as instances of the more general problem of distinguishing self (legitimate users, corrupted data, etc.) from other (unauthorized users, viruses, etc.). We introduce a change-detection algorithm that is based on the way that natural immune systems distinguish self from other. Mathematical analysis ...
An immunology approach to change detection: Algorithm, analysis and implications
- P Haeseleer
- S Forrest
- P Helman
Constructive neural networks
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System
- E Hodo
- X Bellekens
- A Hamilton
- P Duboulih
- E E Iorkyase
Evolving Neural Network Intrusion Detection System for MCPS
- N Molwa
- I Doh
- K J Chae