scholarly journals Multi-Layer Hidden Markov Model Based Intrusion Detection System

2018 ◽  
Vol 1 (1) ◽  
pp. 265-286 ◽  
Author(s):  
Wondimu Zegeye ◽  
Richard Dean ◽  
Farzad Moazzami
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Detection System ◽  
Traffic Monitoring ◽  
Machine Learning Algorithms ◽  
Full Potential ◽  
Multi Phase

The all IP nature of the next generation (5G) networks is going to open a lot of doors for new vulnerabilities which are going to be challenging in preventing the risk associated with them. Majority of these vulnerabilities might be impossible to detect with simple networking traffic monitoring tools. Intrusion Detection Systems (IDS) which rely on machine learning and artificial intelligence can significantly improve network defense against intruders. This technology can be trained to learn and identify uncommon patterns in massive volume of traffic and notify, using such as alert flags, system administrators for additional investigation. This paper proposes an IDS design which makes use of machine learning algorithms such as Hidden Markov Model (HMM) using a multi-layer approach. This approach has been developed and verified to resolve the common flaws in the application of HMM to IDS commonly referred as the curse of dimensionality. It factors a huge problem of immense dimensionality to a discrete set of manageable and reliable elements. The multi-layer approach can be expanded beyond 2 layers to capture multi-phase attacks over longer spans of time. A pyramid of HMMs can resolve disparate digital events and signatures across protocols and platforms to actionable information where lower layers identify discrete events (such as network scan) and higher layers new states which are the result of multi-phase events of the lower layers. The concepts of this novel approach have been developed but the full potential has not been demonstrated.

scholarly journals Intrusion Detection Using Hidden Markov Model and XGBoost Algorithm

2020 ◽  
pp. 466-470
Author(s):  
Sanjana Gawali ◽  
Prerana Agale ◽  
Sandhya Ghorpade ◽  
Rutuja Gawade ◽  
Prabodh Nimat
Keyword(s):  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Communication Networks ◽  
Intrusion Detection System ◽  
Hidden Markov ◽  
Detection System ◽  
Critical Issue ◽  
Machine Learning Algorithms ◽  
User Data

Security has been widely concerned and recognized as a critical issue in wireless communication networks recently, because the openness of the wireless medium allows unintended receivers i. e. intruders to potentially eavesdrop on the transmitted messages. Unauthorized access by an intruder can be monitored by Intrusion detection system. Machine learning algorithms such as Hidden Markov Model and Extreme gradient boost algorithm can be used for intrusion detection based on CICIDS dataset. Based on dataset, algorithms create classifiers of signatures of particular attack. These trained classifiers are tested against user data for intrusion detection. System reports attack in network. Here, XGBoost classifier gives higher accuracy compared to HMM classifier.

scholarly journals Intrusion Detection System using Bayesian Network and Hidden Markov Model

2012 ◽  
Vol 4 ◽  
pp. 506-514 ◽  
Author(s):  
Nagaraju Devarakonda ◽  
Srinivasulu Pamidi ◽  
V. Valli Kumari ◽  
A. Govardhan
Keyword(s):  
Intrusion Detection ◽  
Markov Model ◽  
Bayesian Network ◽  
Hidden Markov Model ◽  
Intrusion Detection System ◽  
Hidden Markov ◽  
Detection System

scholarly journals Identifying Intrusion Behaviour using Enhanced Hidden Markov Model

2020 ◽  
Vol 9 (4) ◽  
pp. 1618-1623
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
Hidden Markov ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Constant Attention ◽  
Network Intrusion

Data Mining is a method for detecting network intrusion detection in networks. It brings ideas from variety of areas including statistics, machine learning and database processes. Decreasing price of digital networking is now economically viable for network intrusion detection. This analysis chiefly examines the system intrusion detection with machine learning and DM methods. To improve the accuracy and efficiency of SHMM, we are collecting multiple observation in SHMM that will be called as Multiple Hidden Markov Model (MHMM). It is used to improve better Detection accuracy compare with SHMM. In the standard Hidden Markov Model, we have observed three fundamental problems are Evaluation and decoding another one is learning problem. The Evaluation problem can be used for word recognition. And the Decoding problem is related to constant attention and also the segmentation. In this Proposed Research, the primary purpose is to model the sequence of observation in Network log and credit card log transactions process using Enhanced Hidden Markov Model (EHMM). And show how it can be used for intrusion detection in Network. In this procedure, an EHMM is primarily trained with the conventional manners of a intruders. If the trained EHMM does not recognize an incoming Intruder transaction with adequately high probability, it is thought to be fraudulent.

Research of IOT Intrusion Detection System Based on Hidden Markov Model

2012 ◽  
Vol 263-266 ◽  
pp. 2949-2952
Author(s):  
Xiu Mei Wei ◽  
Xue Song Jiang ◽  
Xin Gang Wang
Keyword(s):  
Time Delay ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Intrusion Detection System ◽  
Detection Method ◽  
Hidden Markov ◽  
Detection System ◽  
Markov State

Along with the development of Internet of Things (IOT), there are a lot of increasingly serious security problems. The traditional intrusion detection method cannot adapt to the requirement of IOT. In this paper we advance a new intrusion detection method which can adapt to IOT. It is based on Hidden Markov Model (HMM), which is named as Hidden Markov state time delay sequence embedding (HMMSTdse) method.

scholarly journals Anomaly Detection in Control Data of Programming Execution by Context Sensitive Hidden Markov Model

2020 ◽  
Vol 9 (4) ◽  
pp. 1866-1871
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Detection System ◽  
Control Data ◽  
Context Sensitive ◽  
Wide Range ◽  
Artificial Learning

The inconsistency is a major problem in security of information in computer is two ways: data inconsistency and application inconsistency. These two problems are raised due to bad structure of design in programming and create security breaches, vulnerable entries by exploiting application codes. So we can discover these anomalies by design of anomaly detection system (ADS) models at system programming (coding) levels with the help of machine learning. The security vulnerabilities (anomalies) are frequently occurred at potential code execution by exploitation or manipulation of instructions. So, in this paper we have specified various forms of extensions to our work to detect wide range of anomalies at coding exploits and use of a machine learning technique called Context Sensitive-Hidden Markov Model (CS-HMM) will improve the overall performance of ADS by discovering the correlations between control data instances. In this paper we are going to use Linux OS tracing kits to collect the necessary information such as control data instances (return addresses) collected from system as part of artificial learning. The results evaluated through practice on various programs developed for work and also uses of some Linux commands for tracing, finally compared performance of all those input datasets generated live (artificially). After that, the CS-HMM is applying to datasets to scrutinize the anomalies with similarity-search and correlation of function control data of program and classification process determines the anomalous outcomes.

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