A Multifractal Analysis and Machine Learning Based Intrusion Detection System with an Application in a UAS/RADAR System

The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it vaster, faster, and safer. To support the ongoing fight against the malicious misuse of networks, in this paper we propose a novel algorithm called AMDES (unmanned aerial system multifractal analysis intrusion detection system) for spoofing attack detection. This novel algorithm is based on both wavelet leader multifractal analysis (WLM) and machine learning (ML) principles. In earlier research on unmanned aerial systems (UAS), intrusion detection systems (IDS) based on multifractal (MF) spectral analysis have been used to provide accurate MF spectrum estimations of network traffic. Such an estimation is then used to detect and characterize flooding anomalies that can be observed in an unmanned aerial vehicle (UAV) network. However, the previous contributions have lacked the consideration of other types of network intrusions commonly observed in UAS networks, such as the man in the middle attack (MITM). In this work, this promising methodology has been accommodated to detect a spoofing attack within a UAS. This methodology highlights a robust approach in terms of false positive performance in detecting intrusions in a UAS location reporting system.

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A robust intrusion detection system using machine learning techniques for MANET

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-200047 ◽

2020 ◽

Vol 24 (3) ◽

pp. 253-260

Author(s):

N. Ravi ◽

G. Ramachandran

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Mobile Computing ◽

Mobile Networks ◽

Intrusion Detection System ◽

Detection System ◽

Mobile Network ◽

Machine Learning Techniques ◽

Test Bed ◽

Learning Techniques

Recent advancement in technologies such as Cloud, Internet of Things etc., leads to the increase usage of mobile computing. Present day mobile computing are too sophisticated and advancement are reaching great heights. Moreover, the present day mobile network suffers due to external and internal intrusions within and outside networks. The existing security systems to protect the mobile networks are incapable to detect the recent attacks. Further, the existing security system completely depends on the traditional signature and rule based approaches. Recent attacks have the property of not fluctuating its behaviour during attack. Hence, a robust Intrusion Detection System (IDS) is desirable. In order to address the above mentioned issue, this paper proposed a robust IDS using Machine Learning Techniques (MLT). The key of using MLT is to utilize the power of ensembles. The ensembles of classifier used in this paper are Random Forest (RF), KNN, Naïve Bayes (NB), etc. The proposed IDS is experimentally tested and validated using a secure test bed. The experimental results also confirms that the proposed IDS is robust enough to withstand and detect any form of intrusions and it is also noted that the proposed IDS outperforms the state of the art IDS with more than 95% accuracy.

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Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset

Electronics ◽

10.3390/electronics9111771 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1771

Author(s):

Muhammad Ashfaq Khan ◽

Juntae Kim

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Performance Metrics ◽

Detection System ◽

Rapid Development ◽

Research Problem ◽

Vital Role ◽

Attack Detection ◽

Detection Methods ◽

Malicious Attacks

Recently, due to the rapid development and remarkable result of deep learning (DL) and machine learning (ML) approaches in various domains for several long-standing artificial intelligence (AI) tasks, there has an extreme interest in applying toward network security too. Nowadays, in the information communication technology (ICT) era, the intrusion detection (ID) system has the great potential to be the frontier of security against cyberattacks and plays a vital role in achieving network infrastructure and resources. Conventional ID systems are not strong enough to detect advanced malicious threats. Heterogeneity is one of the important features of big data. Thus, designing an efficient ID system using a heterogeneous dataset is a massive research problem. There are several ID datasets openly existing for more research by the cybersecurity researcher community. However, no existing research has shown a detailed performance evaluation of several ML methods on various publicly available ID datasets. Due to the dynamic nature of malicious attacks with continuously changing attack detection methods, ID datasets are available publicly and are updated systematically. In this research, spark MLlib (machine learning library)-based robust classical ML classifiers for anomaly detection and state of the art DL, such as the convolutional-auto encoder (Conv-AE) for misuse attack, is used to develop an efficient and intelligent ID system to detect and classify unpredictable malicious attacks. To measure the effectiveness of our proposed ID system, we have used several important performance metrics, such as FAR, DR, and accuracy, while experiments are conducted on the publicly existing dataset, specifically the contemporary heterogeneous CSE-CIC-IDS2018 dataset.

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A Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System

Journal of Computational Science and Intelligent Technologies ◽

10.53409/mnaa.jcsit20201203 ◽

2020 ◽

Vol 1 (2) ◽

pp. 15-25

Author(s):

Suresh Adithya Nallamuthu ◽

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Performance Analysis ◽

Intrusion Detection ◽

Cyber Security ◽

Intrusion Detection System ◽

Detection Rate ◽

Detection System ◽

Attack Detection ◽

Network Systems

The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) is used for attack detection and classification. The Canadian Institute for Cyber-security CIC-IDS 2017 dataset is used for the evaluation of performance analysis. Initially, from the dataset, the data are preprocessed, and by using the genetic algorithm, the attack was detected. The detected attacks are classified using the BPNN classifier for identifying the types of attacks. The performance analysis was executed, and the results are obtained and compared with the existing machine learning-based classifiers like FC-ANN, NB-RF, KDBN, and FCM-SVM techniques. The proposed GA-BPNN model outperforms all these classifying techniques in every performance metric, like accuracy, precision, recall, and detection rate. Overall, from the performance analysis, the best classification accuracy is achieved for Web attack detection with 97.90%, and the best detection rate is achieved for Brute force attack detection with 97.89%.

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Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

International Journal of Artificial Intelligence Research ◽

10.29099/ijair.v4i1.156 ◽

2020 ◽

Vol 4 (1) ◽

Author(s):

Ahmad Azhari ◽

Arif Wirawan Muhammad ◽

Cik Feresa Mohd Foozy

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Policy Development ◽

Detection System ◽

Denial Of Service ◽

Attack Detection ◽

Machine Learning Techniques ◽

Ddos Attacks ◽

Detection Techniques

Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity. DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development. According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%.

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A Survey on Intrusion Detection System using Machine Learning and Deep Learning

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit195264 ◽

2019 ◽

pp. 264-270

Author(s):

Er. Hemavati ◽

Aparna R

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Performance Metrics ◽

Detection System ◽

Attack Detection ◽

Machine Learning Techniques ◽

Network Applications ◽

Important Challenge ◽

Iot Devices

As we know internet of Things (IoT) is one of the fastest growing paradigm which is composed of Internet and different physical devices with different domains or the smart applications like home automation, business automation applications, health and environmental monitoring applications. The dependency on IOT devices is increasing day by day with our daily activities, which leads to most important challenge for security. Since having a better monitoring system for better security is a need. From more than two decades the concept or the frame work called IDS (Intrusion detection system) is playing important role for detecting the attacks in the network. Since the network attacks are not fixed in nature, a new type of attacks are happening on the network applications. There are many traditional IDS techniques are available but they are complex to apply. Since machine learning is one of the important area which is achieving good results in many applications. In this paper we study about the different machine learning techniques used till now and the methodology for the attack detection and the validation strategy. We will also discuss about the performance metrics.

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