Cloud Based Intrusion Detection System Research Articles

Improved Rsa Algorithm for Data Security against DDoS Attack in a Cloud-based Intrusion Detection System

Improved Rsa Algorithm for Data Security against DDoS Attack in a Cloud-based Intrusion Detection System

Cyber-Attack Detection for Cloud-Based Intrusion Detection Systems

In this research, we delve into an exhaustive ex- amination of the usage of machine learning (ML) models for the identification of cyber threats within Cloud-Based Intrusion Detection Systems (IDS). With the escalating dependence on cloud services across various industries, the urgency to develop effective and resilient IDS to mitigate burgeoning cyber risks is paramount. Our study makes a significant contribution to this imperative field by analyzing the efficacy of diverse ML models in detecting cyber-attacks within a cloud context. We scrutinized an array of ML models, namely Decision Trees (DT), Random Forest (RF), XGBoost, and Support Vector Machines (SVM), utilizing key performance parameters such as accuracy, recall, precision, F1-s, and confusion matrix to understand their practical application in real-world IDS scenarios. XGBoost stood out as the most proficient model, showcasing not only an impressive accuracy but also a balanced performance in terms of precision and recall. This highlights the considerable potential of ensemble and gradient boosting techniques in optimizing cloud- based IDS detection capabilities. Our findings underscore the significant role of machine learning in fostering more dependable, robust, and efficient IDS in the cloud, thus significantly aiding in securing our digital ecosystems.

Optimization of Deep Generative Intrusion Detection System for Cloud Computing: Challenges and Scope for Improvements

The large amount of data and its exponential increase result in security problems which subsequently cause damage to cloud computing and its environments. The Intrusion detection system (IDS) is among the systems that monitor and analyse data for malicious attacks in the cloud environment. High volume, high redundancy, and high dimensionality of network traffic in cloud computing make it difficult to detect attacks by contemporary techniques. To improve the performance of IDS features selection and data imbalance issues need to be resolved. This paper includes techniques and surveys of cloud-based IDS with ML techniques and IDS performance on the different types of cloud-based datasets. It also analyses the gaps and scope for enhancement of evaluation parameters of IDS. It provides a cloud-based IDS system which will produce a good performance result as compared to the other contemporary system. Moreover, this paper offers a current overview of cloud-based IDS, Data imbalance technique, Dataset and proposed cloud IDS system architecture.

Cloud-Based Smart Contract Analysis in FinTech Using IoT-Integrated Federated Learning in Intrusion Detection

Data sharing is proposed because the issue of data islands hinders advancement of artificial intelligence technology in the 5G era. Sharing high-quality data has a direct impact on how well machine-learning models work, but there will always be misuse and leakage of data. The field of financial technology, or FinTech, has received a lot of attention and is growing quickly. This field has seen the introduction of new terms as a result of its ongoing expansion. One example of such terminology is “FinTech”. This term is used to describe a variety of procedures utilized frequently in the financial technology industry. This study aims to create a cloud-based intrusion detection system based on IoT federated learning architecture as well as smart contract analysis. This study proposes a novel method for detecting intrusions using a cyber-threat federated graphical authentication system and cloud-based smart contracts in FinTech data. Users are required to create a route on a world map as their credentials under this scheme. We had 120 people participate in the evaluation, 60 of whom had a background in finance or FinTech. The simulation was then carried out in Python using a variety of FinTech cyber-attack datasets for accuracy, precision, recall, F-measure, AUC (Area under the ROC Curve), trust value, scalability, and integrity. The proposed technique attained accuracy of 95%, precision of 85%, RMSE of 59%, recall of 68%, F-measure of 83%, AUC of 79%, trust value of 65%, scalability of 91%, and integrity of 83%.

Dual stage ensemble technique for intrusion detection in cloud computing

A capability of cloud-based IDS in identifying complicated and anonymous attacks is rising in the current era. However, unwanted delays hinder the detection rate. A malicious user might utilize vast quantities of computational power. The cloud provides to perform attacks both within and without the cloud. Furthermore, there are major challenges for intrusion detection due to the ease of the cloud and also the continual restructuring and movement of cloud resources. Intruder detection, feature extraction, and data processing are all included in the novel optimization-based Intrusion Detection System (IDS) paradigm that will be presented in this study. Data normalization is used to first pre-process the input data. Then, appropriate feature extraction is carried out, including the extraction of (a) raw features, (b) statistical features, then (c) higher-order statistical features using suggested kurtosis. The detection phase is then applied to the retrieved features. A two-stage ensemble method is suggested for finding intruders in clouds. Random forest (RF), Support Vector Machine (SVM), optimal Neural Network (NN), and RNN make up the suggested ensemble technique. The RF, SVM, and Optimized NN algorithms are directly fed the collected features. The output of these classifiers is then provided to the RNN classifier (i.e.), RF output to RNN1, SVM output to RNN2, and optimized NN output to RNN3. Then, the weighted average of RNN 1, 2, and 3 is considered as the final output. A Self Adaptive Salp Swarm Optimization optimizes the weights of NN for exact detection (SA-SSO). Finally, a test is conducted to confirm the developed model’s superiority.

Hybrid machine learning approach based intrusion detection in cloud: A metaheuristic assisted model

Cloud computing provides various cost-effective on-demand services to the user and so it is rising like a real trend in the IT service model. However, due to its open and distributed architecture, it is highly vulnerable to attackers. The security and privacy of cloud users has become a major hurdle. The most prevalent approach for detecting attacks on the cloud is the Intrusion Detection System (IDS). Scalability and autonomous self-adaptation weren’t features of contemporary IDS deployed in traditional Internet or Intranet contexts. Furthermore, they lack determinism, making them inappropriate for cloud-based settings. This necessitates the development of new cloud-based IDS capable of fulfilling the firm’s security standards. Therefore, in this research work, we have introduced a new IDS model for the cloud environment. Our research work is made up of four major phases: “data pre-processing, optimal clustering, feature selection, and attack detection phase”. Initially, the collected raw data are pre-processed to enhance the quality of the data. Then, these pre-processed data are segmented with the newly introduced K-means clustering model, where we’ve optimally selected the centroids by introducing a new hybrid optimization model referred as Spider Monkey Updated with Sealion Optimization (SMSLO), which is the conceptual hybridization of standard SeaLion Optimization (SLnO) and Spider Monkey Optimization (SMO), respectively. At the end of segmentation, two clusters (attack data and non-attack data) will be formed. The data available in both clusters seems to be huge in dimensions, so we’ve lessened the dimensions of the data in the clusters by applying the “Principal Component Analysis (PCA)” algorithm. Subsequently, these dimensionality-reduced features pass into the attack detection phase. The attack detection phase is modeled with the optimized Deep Belief Network (DBN), which portrays the type of attack (Dos, Botnet, DDoS as well) that intruded into the network. Since the DBN makes the final detections; it is ought to be less prone to errors. We have lessened the detection errors such as the Mean Square Error (MSE) of DBN by fine-tuning its weight using a new hybrid optimization model (SMSLO). Finally, the result acquired from the proposed work (DBN+SMSLO) is validated.

EICIDS-Elastic and Internal Cloud-based Intrusion Detection System

The elasticity and abundant availability of computational resources are attractive to intruders exploit vulnerabilities of the cloud, thus being able to launch attacks against legitimate users to gain access to private and privileged information. The Intrusion Detection Systems are presented as a possible solution for protection; however, to effectively protect cloud users, it must have the ability to expand, rapidly increased or decreased the amount of sensors, according to the provisioning resources, and isolate the access to infrastructure and system levels. Protection against internal threats should also be planned, as most of the protection systems do not identify them properly. In order to solve these problems, we present the EICIDS - Elastic and Internal Cloud-based Intrusion Detection System, which monitors the internal environment of the cloud, entering sensor data capture in the local network of user´s VMs, thus being able to detect suspicious behavior of users. For this, the EICIDS uses the characteristics of virtual machines (such as fast boot, quick recovery, stop, migrate between different hosts and execution across multiple platforms) in order to monitor and protect the environment of cloud computing, with the growth or reduction of the cloud in order to save resources.

Design of an enterprise cloud-based intrusion detection system model, using back propagation network based on particle swarm optimisation algorithm

Design of an enterprise cloud-based intrusion detection system model, using back propagation network based on particle swarm optimisation algorithm

Design of an enterprise cloud-based intrusion detection system model, using back propagation network based on particle swarm optimisation algorithm

Design of an enterprise cloud-based intrusion detection system model, using back propagation network based on particle swarm optimisation algorithm

A Design of an Integrated Cloud-based Intrusion Detection System with Third Party Cloud Service

AbstractAlthough cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.

Multi-cloud cooperative intrusion detection system: trust and fairness assurance

The sophistication of the recent cloud computing systems has made them more vulnerable to intelligent cyber attacks. Moreover, it is becoming very difficult for a single intrusion detection system (IDS) to detect all existing attacks, due to limited knowledge about such attacks’ patterns and implications. Recent works in cloud security have shown that cooperation among cloud-based IDSs can enhance their accuracy. However, there are two main challenges associated with the existing cooperative IDSs, which are related to trust and fairness assurance. To tackle these challenges, we propose in this paper a cooperative cloud-based IDS framework that (1) enables IDSs to distributively form trustworthy IDSs communities by advancing a trust-based hedonic coalitional game, which allows IDSs to increase their individual detection accuracy in the presence of untrusted IDSs and (2) formulates a fairness assurance mechanism as a Stackelberg game between the well-behaving IDSs and the selfish ones that frequently send consultation requests to other IDSs, and at the same do not answer other IDSs’ consultation requests. Experimental results show the effectiveness of the proposed approach in terms of enhancing the accuracy of detection and achieving the fairness among IDSs in terms of benefits obtained through cooperation.

A deep learning approach for proactive multi-cloud cooperative intrusion detection system

Abstract The last few years have witnessed the ability of cooperative cloud-based Intrusion Detection Systems (IDS) in detecting sophisticated and unknown attacks associated with the complex architecture of the Cloud. In a cooperative setting, an IDS can consult other IDSs about suspicious intrusions and make a decision using an aggregation algorithm. However, undesired delays arise from applying aggregation algorithms and also from waiting to receive feedback from consulted IDSs. These limitations render the decisions generated by existing cooperative IDS approaches ineffective in real-time, hence making them unsustainable. To face these challenges, we propose a machine learning-based cooperative IDS that efficiently exploits the historical feedback data to provide the ability of proactive decision making. Specifically, the proposed model is based on a Denoising Autoencoder (DA), which is used as a building block to construct a deep neural network. The power of DA lies in its ability to learn how to reconstruct IDSs’ feedback from partial feedback. This allows us to proactively make decisions about suspicious intrusions even in the absence of complete feedback from the IDSs. The proposed model was implemented in GPU-enabled TensorFlow and evaluated using a real-life dataset. Experimental results show that our model can achieve detection accuracy up to 95%.

Survey of intrusion detection techniques and architectures in cloud computing

Cloud computing yields more utilisation of resources and hence a reasonable service access cost to end users. Cloud computing, virtually and dynamically, provides the data resources to a variety of users. As cloud computing is a shared facility, it is vulnerable to various attacks. The cloud networks have their own features which are the reason for threats to the security in clouds. The best solution to protect the cloud from the attacks is use of intrusion detection systems (IDS). This system is a better solution to achieve higher level of security, and is the most widely used technique where the system consists of IDS connected over the network in combination with various anomaly detection techniques. This paper provides an extensive survey of various cloud-based IDS architectures implemented in cloud environments for dealing with various security issues.