scholarly journals Sliding Window: The Impact of Trace Size in Anomaly Detection System for Containers Through Machine Learning

2020 ◽  
Author(s):  
Gabriel Ruschel Castanhel ◽  
Tiago Heinrich ◽  
Fabrício Ceschin ◽  
Carlos A. Maziero
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Normal System ◽  
System Call ◽  
Observation Window ◽  
Sliding Windows ◽  
Behavior System ◽  
Anomaly Intrusion Detection ◽  
The Impact

Anomaly intrusion detection in Host-based Intrusion Detection System (HIDS) is a process intended to monitor operations on a host to identify behaviors that differ from a “normal ” system behavior. System call based HIDS uses traces of calls to represent the behavior of a system. Due to the volume of data generated by applications and the operating system, sliding windows are applied in order to asses an online environment, allowing intrusions to be detected in real time while being still executed. The respective study explores the impact that the size of the observation window has on Machine Learning (ML) one-class algorithms.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

AIWAS

2012 ◽  
Vol 3 (1) ◽  
pp. 73-91
Author(s):  
Toan Huynh ◽  
James Miller
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Web Applications ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Anomaly Intrusion Detection ◽  
Help System

A recent report states that 63 percent of documented vulnerabilities exist in Web applications. Hence, Web applications represent an ideal platform for malicious attackers to target. This paper presents an anomaly intrusion detection system (AIWAS) to help system administrators protect their Web applications from these attacks. AIWAS maps each user’s input into an Instance Model (IM). The IM, which contains attackable features of the input, allows machine learning algorithms to classify the input as either benign or malicious. AIWAS then prevents malicious inputs from reaching the protected Web applications. A case study demonstrates the effectiveness of AIWAS against actual attacks.

scholarly journals Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Basim Mahbooba ◽  
Radhya Sahal ◽  
Wael Alosaimi ◽  
Martin Serrano
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Learning Technologies ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Network Intrusion ◽  
Learning Techniques ◽  
The Impact

To design and develop AI-based cybersecurity systems (e.g., intrusion detection system (IDS)), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree (DT), K nearest neighbour (KNN), random forest (RF), and naïve Bayes (NB). The four deep learning techniques are LSTM (one and two layers) and GRU (one and two layers). Two datasets are used to classify the IDS attack type, including wireless sensor network detection system (WSN-DS) and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS.

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

A MACHINE LEARNING BASED IOT FOR PROVIDING AN INTRUSION DETECTION SYSTEM FOR SECURITY

2021 ◽  
pp. 103741
Author(s):  
Dhanke Jyoti Atul ◽  
Dr. R. Kamalraj ◽  
Dr. G. Ramesh ◽  
K. Sakthidasan Sankaran ◽  
Sudhir Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

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