An Adaptive Intrusion Detection Scheme for Cloud Computing

2019 ◽  
Vol 10 (4) ◽  
pp. 53-70
Author(s):  
Nurudeen Mahmud Ibrahim ◽  
Anazida Zainal
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Virtual Machine ◽  
Detection System ◽  
Performance Comparison ◽  
Change Point Detection ◽  
Virtual Machine Migration ◽  
Detection Scheme ◽  
Adaptive Scheme

To provide dynamic resource management, live virtual machine migration is used to move a virtual machine from one host to another. However, virtual machine migration poses challenges to cloud intrusion detection systems because movement of VMs from one host to another makes it difficult to create a consistent normal profile for anomaly detection. Hence, there is a need to provide an adaptive anomaly detection system capable of adapting to changes that occur in the cloud data during VM migration. To achieve this, the authors proposed a scheme for adaptive IDS for Cloud computing. The proposed adaptive scheme is comprised of four components: an ant colony optimization-based feature selection component, a statistical time series change point detection component, adaptive classification, and model update component, and a detection component. The proposed adaptive scheme was evaluated using simulated datasets collected from vSphere and performance comparison shows improved performance over existing techniques.

scholarly journals Implementasi Network Intrusion Detection System (NIDS) Dalam Sistem Keamanan Open Cloud Computing

2019 ◽  
Vol 17 (2) ◽  
pp. 1-9
Author(s):  
Muqorobin Muqorobin ◽  
Zul Hisyam ◽  
Moch Mashuri ◽  
Hanafi Hanafi ◽  
Yudhi Setiyantara
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Virtual Machine ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Keamanan merupakan bagian terpenting dalam sistem teknologi jaringan komputer. Diantara teknologi yang memanfaatkan jaringan adalah cloud computing. Salah satu penyedia cloud computing seperti eucalyptus menggunakan firewall untuk keamanan sistem. Penggunaan firewall pada sistem tidak dapat memantau dan menganalisa traffic yang berada di dalam server cloud dan tidak memberikan peringatan ketika terjadi sebuah serangan. Tujuan dari penelitian ini adalah peneliti akan menerapkan sebuah network intrusion detection system (NIDS) dilingkungan cloud computing dan mirroring traffic pada switch. Intrusion detection system (IDS) merupakan suatu teknologi keamanan yang mampu menganalisis traffic jaringan dan mendeteksi traffic apabila terindikasi serangan. NIDS ditempatkan dihost yang berbeda dengan server cloud computing. Dengan metode mirroring traffic pada switch, traffic akan diarahkan ke NIDS sehingga NIDS mampu merekam semua traffic jaringan yang berasal dari luar server cloud ataupun traffic antar virtual machine di dalam server cloud. Hasil pengujian serangan dengan 2 skenario yaitu penyerangan dari luar dan dari dalam sistem cloud, maka NIDS mampu memberikan respon alert untuk traffic serangan.

scholarly journals On the proper choice of datasets and traffic features for real-time anomaly detection

2021 ◽  
Vol 2091 (1) ◽  
pp. 012001
Author(s):  
C Callegari ◽  
S Giordano ◽  
M Pagano
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection System ◽  
Detection Algorithm ◽  
Performance Comparison ◽  
Critical Element ◽  
Correct Identification ◽  
Data Set ◽  
On Line

Abstract Thanks to its ability to face unknown attacks, Anomaly-based Intrusion Detection is a key research topic in network security and different statistical methods, fed by suitable traffic features, have been proposed in the literature. The choice of a proper dataset is a critical element not only for performance comparison, but also for the correct identification of the normal traffic behaviour. In this paper we address the general problem of selecting traffic features from recent real traffic traces (MAWI data set) and verify how the real-time constraint impacts on the general performance. Although a state-of-the-art IDS (Intrusion Detection System) based on deep neural networks is considered, our conclusions can be extended to any anomaly detection algorithm and advocate for a fair comparison of IDSs using representative datasets and traffic features that can be extracted on-line (and do not depend on the entire dataset).

A Distributed Intrusion Detection Scheme for Cloud Computing

2020 ◽  
Vol 11 (1) ◽  
pp. 68-82
Author(s):  
Nurudeen Mahmud Ibrahim ◽  
Anazida Zainal
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Scheme ◽  
Distributed Intrusion Detection ◽  
Binary Segmentation

Intrusion detection systems (IDS) is an important security measure used to secure cloud resources, however, IDS often suffer from poor detection accuracy due to coordinated attacks such as a DDoS. Various research on distributed IDSs have been proposed to detect DDoS however, the limitations of these works the lack of technique to determine an appropriate period to share attack information among nodes in the distributed IDS. Therefore, this article proposes a distributed IDS that uses a binary segmentation change point detection algorithm to address the appropriate period to send attack information to nodes in distributed IDS and using parallel Stochastic Gradient Descent with Support Vector Machine (SGD-SVM) to achieve the distributed detection. The result of the proposed scheme was implemented in Apache Spark using NSL-KDD benchmark intrusion detection dataset. Experimental results show that the proposed distributed intrusion detection scheme outperforms existing distributed IDS for cloud computing.

A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

2017 ◽  
Vol 7 (9) ◽  
pp. 30 ◽  
Author(s):  
Ramandeep Kaur
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Heuristic Algorithms ◽  
Hybrid Approach ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Bee Colony ◽  
Effective Manner ◽  
Effective Performance

A lot of research has been done in the field of cloud computing in computing domain.  For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.

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).

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