Intrusion detection system (IDS) alerts the network administrators against intrusive attempts. The anomalies are detected using machine learning techniques such as supervised and unsupervised learning algorithms. Repetitive and irrelevant features in data have posed a long due to speed bump in efficient network traffic classification. This issue could be resolved by reducing the dimensionality of feature space using feature selection method wherein it identifies the important features and eliminates irrelevant ones. An intrusion detection system named Least Square Support Vector Machine (LSSVM-IDS) is built using this feature selection algorithm. It is tested on intrusion detection data set like KDD Cup 99, NSK-KDD and Kyoto 2006+ data set. This LSSVM machine has accuracy of 95%, i.e. it has predicted correct output with 95% of time. To avoid the intrusion detection system from getting obsolete, to adapt it with newer attack resistance feature and also to make it less expensive, we applied ensemble learning algorithm on UNSW-NB15 data set, using a stacking classifier method. We have combined random forest, support vector machine and Naive Bayes methods using logistic regression as meta-classifier and have achieved 95% accuracy.

Building an Ensemble Learning Based Algorithm for Improving Intrusion Detection System

Lightweight virtualization represented by container technology has made continuous progress in the field of virtualization as an effective substitute for Virtual Machine(VM). However, its flexibility also makes the cloud system more dynamic and complex, which intensifies the unpredictable security problems. In this case, the defense mechanisms which use changeless implementation can hardly protect the attack surface in real time in the dynamic cloud system. To solve this problem, we present AHDS, an Automated Honeynet Deployment Strategy for active defense in the container-based cloud, to assess the variation of system structure and optimize the honeynet deployment strategy automatically. In the design of AHDS, we (1) establish an attack graph for formalizing various possible attack events in the container-based cloud system; (2) combine the concept of degree centrality with the attack graph for evaluating and optimizing the process to deploy honeynet; (3) design a framework to automatically generate, deploy and adjust honeynet deployment strategy. The results show that AHDS could reduce the success rate of attacks by 83% in container-based clouds and is flexible and scalable for large-scale implementation.

Automated Honeynet Deployment Strategy for Active Defense in Container-based Cloud

—The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5G era, providing multiple benefits in the smart economies. However, despite the advantages, security issues still remain. In particular, SDN/NFV and cloud/edge computing are related to various security issues. Moreover, due to the wireless nature of the entities, they are prone to a wide range of cyberthreats. Therefore, the presence of appropriate intrusion detection mechanisms is critical. Although both Machine Learning (ML) and Deep Learning (DL) have optimised the typical rule-based detection systems, the use of ML and DL requires labelled pre-existing datasets. However, this kind of data varies based on the nature of the respective environment. Another smart solution for detecting intrusions is to use honeypots. A honeypot acts as a decoy with the goal to mislead the cyberatatcker and protect the real assets. In this paper, we focus on Wireless Honeypots (WHs) in ultra-dense networks. In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a) e − Greedy and (b) Q − Learning . Both methods aim to identify the optimal number of honeypots that can be deployed for protecting the actual entities. The experimental results demonstrate the efficacy of both methods.

https://ieeexplore.ieee.org/ielx7/6245516/6558478/09805462.pdf

Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

Honeypot is a proactive defence technology introduced by the defence to change the asymmetry of the network offensive and defence game. Through deploying security resources without any production purpose, the defenders can deceive attackers to illegally use them to capture and analyse the attack behaviour to understand the attack tools, methods, speculate the attack intentions and motivations. This paper introduces the development process and the latest development direction of honeypot technology, comprehensively analyses the application situation of artificial intelligence technology in the key mechanisms of honeypot technology, and summarises the latest application of artificial intelligence technology in the deception environment construction, threat data capture and threat data analysis. Finally, the application of artificial intelligence technology in honeypot technology is discussed.

Application of Artificial Intelligence Technology in Honeypot Technology

Software Effort Estimation (SEE) plays an instrumental role in estimating the effort required to complete a software development project. It involves the process of estimating the effort required in order to meet project delivery deadlines by appropriately scheduling and allocating resources during the Software Development Life Cycle (SDLC). In this paper, we have proposed a feature selection technique with regression models using learning automata to estimate software development effort. We have validated our approach in six data sets for software effort estimation. Further, we have discretized the predicted effort into three different classes: High (H), Moderate (M) and Low (L). We have used various regression models including Linear, LASSO, ElasticNet and Ridge regression to obtain the estimated effort and use quartiles for discretizing them. We have compared the result of our proposed approach with some existing models of feature selection and regression techniques and found that our approach is able to provide better prediction performance than some existing-models.

Learning Automata based Feature Selection for Duplex Output Software Effort Estimation Model

The honeypot is a kind of proactive defense technology against malicious attacks in the field of information security. Successful and timely detection of network attacks highly depends on efficient honeypot deployment. This paper proposes an adaptive honeypot deployment algorithm based on learning automata (LA) called LA-AHD for improving the network security. The whole network suffers from external attacks in an attack-defense scenario based on a mathematical model. The proposed algorithm considers the entirety of candidate nodes in the network as a LA and models each candidate node as an action of the LA. Through the interactions with attacks and the evolutions of the LA, a certain number of honeypots can be adaptively deployed at the proper place in the network. The computational experiments verify the effectiveness and efficiency of the proposed LA-AHD algorithm. The results show the algorithm can improve the speed of selecting the honeypots and increase the honeypot capture rate substantially.

An Adaptive Honeypot Deployment Algorithm Based on Learning Automata

According to an industrial control network flow abnormality identification method with a reference model constructed based on multiple RBM networks, features are extracted from the industrial controlnetwork and a training dataset is generated, the reference model is trained and an industrial control network normal reference model containing a plurality of RBM models and an abnormal data cluster in the training dataset, and the industrial control network normal reference model is utilized to perform real-time network message assessment, thereby realizing flow abnormality detection. According to the industrial control network flow abnormality identification method provided by the invention can complete whether to perform dimensionality reduction and which dimensionality is to be reduced tothrough setting of parameters inside and has better robustness, does not need to set the required clustering number in advance, completes through the correlation degree of the model, and thus better satisfies the situation of practical application.

Industrial control network flow abnormality identification method with reference model constructed based on multiple RBM networks

The invention discloses a feature screening system and method for network traffic attack detection based on a learning automaton. The system comprises a data preprocessing module, a setting module, alearning automaton module, a random environment module, a feature screening module and an attack detection module. According to the invention, by interacting with the classifer and learning the evolution of the automaton is learned, redundant features are removed one by one, finally, the optimal features are screened out to form an optimal feature subset, the problems that the network traffic datasize is large, and the dimension is high are effectively solved, the network traffic attack detection efficiency can be effectively improved through the screened features, and the method can be applied to environments of large-scale networks such as a power grid industrial control network and the like.

Feature screening system and method for network traffic attack detection based on learning automaton

The power grid is an important facility, which is essential to the national economy and people's livelihood. With the development of the power grid, the security problems of it become more and more urgent. Among these problems, the traffic anomaly of the power grid is one of the most important in the power grid security field currently. It is obvious that the grid data are quite regular, massive, unsupervised and real-time. In addition, the existing methods of this problem are relatively few and the application of those in the specific grid environment is unsatisfactory. Given those factors, this paper proposed a new method of the network anomaly traffic detection, which is based on a benchmark model built by the multi-RBM network. This method is as follows: first, divide the data onto clusters according to the time. Then, determine the RBM model of the initial clusters. Next, obtain the RBM model of the entire data by merging the similar clusters. Finally, detect anomalies according to the similarity between the real-time data and the normal benchmark model. The results of the experiment indicate that the method proposed in this paper is more accurate and responsive in the anomaly detection of power grid flow in comparison with the previous unsupervised algorithms.

Anomaly Detection for Power Grid Flow Patterns Based on the Multi-restricted Boltzmann Machines

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An Adaptive Honeypot Deployment Algorithm Based on Learning Automata

Industrial control network flow abnormality identification method with reference model constructed based on multiple RBM networks

Feature screening system and method for network traffic attack detection based on learning automaton

Anomaly Detection for Power Grid Flow Patterns Based on the Multi-restricted Boltzmann Machines