Cloud Computing Security aspects: Threats, Countermeasures and Intrusion Detection using Support Vector Machine

Cloud computing provides a range of services over the Internet using a pay-per-use model. Consequently, several firms have already used this system to entice people with its appealing characteristics. However, its architecture renders it susceptible to malicious assaults. This necessitates the implementation of an Intrusion Detection System (IDS) that can identify such assaults in a cloud environment accurately. This study presents a Machine Learning Efficient Intrusion Detection System (ML-EIDS) that integrates a Fuzzy C Means (FCM) clustering method with a Support Vector Machine (SVM) to enhance the precision of the detecting method in a cloud computing scenario. The suggested ML-EIDS system has been executed and contrasted with preexisting methods. The NSL-KDD dataset is used for conducting experiments. Through performance assessment and comparative analysis, the findings achieved by implementing the ML-EIDS hybrid method demonstrate that the suggested system can accurately detect abnormalities with high precision and a low occurrence of false alarms, surpassing current methodologies.

Denial-of-Service (DoS) and probe attacks are growing more modern and sophisticated in order to evade detection by Intrusion Detection Systems (IDSs) and to increase the potent threat to the availability of network services. Detecting these attacks is quite tough for network operators using misuse-based IDSs because they need to see through attackers and upgrade their IDSs by adding new accurate attack signatures. In this paper, we proposed a novel signal and image processing-based method for detecting network probe and DoS attacks in which prior knowledge of attacks is not required. The method uses a time-frequency representation technique called S-transform, which is an extension of Wavelet Transform, to reveal abnormal frequency components caused by attacks in a traffic signal (e.g., a time-series of the number of packets). Firstly, S-Transform converts the traffic signal to a two-dimensional image which describes time-frequency behavior of the traffic signal. The frequencies that behave abnormally are discovered as abnormal regions in the image. Secondly, Otsu's method is used to detect the abnormal regions and identify time that attacks occur. We evaluated the effectiveness of the proposed method with several network probe and DoS attacks such as port scans, packet flooding attacks, and a low-intensity DoS attack. The results clearly indicated that the method is effective for detecting the probe and DoS attack streams which were generated to real-world Internet.

Existing misuse-based intrusion detection methods are often not sufficient in detecting zero-days attacks. As a result, anomaly or learning based intrusion detection mechanisms have been developed to cope with such attacks. Among variety of anomaly detection approaches, Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) are known to be two of the best machine learning algorithms to classify normal from abnormal behaviors. In this paper a hybrid anomaly-based intrusion detection method is proposed that is based on these two methods. These methods are trained in supervised way. We use the following additional techniques to improve the performance of proposed approach: First, a feature selection technique using the entropy of features is used for extracting optimized information from KDD data set and second, a novel method is proposed to combine the results of these two learning based methods. Lastly, we demonstrate the effectiveness of proposed hybrid approach by using the KDD dataset. The simulation results show that which features of KDD are better to distinguish the normal from abnormal traffics. Also these results show the detection precision of the DoS, Probe, U2R and R2L attacks by using our method are 99%, 100%, 100% and 100% respectively.

ERP management information systems are widely used in the process of enterprise operation and development. By optimizing the allocation of management resources such as enterprise customers, project operations, material procurement, supply, and production, the utility of various resources in the enterprise is maximized, with integrated, systematic, and organizational management characteristics. Based on this, this article discusses cloud computing and ERP systems, analyzes the positive effects of cloud computing ERP systems on enterprises, and studies the specific applications of ERP systems in enterprises under cloud computing mode. At present, many large and medium-sized enterprises in China have established a complete ERP enterprise management information system. Considering the expansion of enterprise scale and the multiplication of enterprise management data, the existing ERP management information system has shown some limitations. With the rapid development of computer technology, the concept of cloud computing has been gradually promoted and applied. Based on the characteristics of cloud computing, ERP enterprise management information system began to consciously combine with the concept of cloud computing. This paper mainly introduces the characteristics and advantages of ERP enterprise management information system based on cloud computing mode, and analyzes its application effect in depth.

Support Vector Machine (SVM) is a relatively novel classification technology, which has shown higher performance than traditional learning methods in many applications. Therefore, some security researchers have proposed an intrusion detection method based on SVM. However, the SVM algorithm is very sensitive to the choice of kernel function and parameter adjustment. Once the parameter selection is unscientific, it will lead to poor classification accuracy. To solve this problem, this paper presents a Grey Wolf Optimizer Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm to improve the Intrusion Detection System (IDS) based on SVM. This method uses PSOGWO algorithm to optimize the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The "optimal detection model" of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments based on NSL-KDD dataset show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, and has improved significantly in terms of detection rate, convergence speed and model balance. This shows that the method has better performance for network intrusion detection.

Intrusion Detection System (IDS) can be defined as a group of tools, methods and resources that help us to predict or identify any unauthorized activity in a network. Current IDSs are mainly based on techniques constructed on heuristic rules, named signatures, in order to detect intrusions in a network environment. The drawbacks of these approach is that it could only detect a known attacks and referenced above. Contrastively, Intrusion Detection behavioral, or anomaly, assume that attacks causes an abnormal use of resources or manifest a strange behavior on the part of the user, by studying the behavior of the different types of network traffic it can identify the known and unknown attacks using the artificial learning algorithm. This study proposes a new behavioral approach of intrusion detection based on combination APSO (Accelerated Particle Swarm Optimization)-SVM (Support Vector Machine) to develop a model for IDS. The simulation results show a significant amelioration in performances, all tests were realized with the NSL-KDD data set. In comparison with other methods based on the same dataset, the proposed model shows a high detection performance.

Uses of Artificial Intelligent Techniques to Build Accurate Models for Intrusion Detection System

Intrusion detection has been a vast-surveyed topic for many decades as network attacks are tremendously growing. This has heightened the need for security in networks as web-based communication systems are advanced nowadays. The proposed work introduces an intelligent semi-supervised intrusion detection system based on different algorithms to classify the network attacks accurately. Initially, the pre-processing is accomplished using null value dropping and standard scaler normalization. After pre-processing, an enhanced Deep Reinforcement Learning (EDRL) model is employed to extract high-level representations and learn complex patterns from data by means of interaction with the environment. The enhancement of deep reinforcement learning is made by associating a deep autoencoder (AE) and an improved flamingo search algorithm (IFSA) to approximate the Q-function and optimal policy selection. After feature representations, a support vector machine (SVM) classifier, which discriminates the input into normal and attack instances, is employed for classification. The presented model is simulated in the Python platform and evaluated using the UNSW-NB15, CICIDS2017, and NSL-KDD datasets. The overall classification accuracy is 99.6%, 99.93%, and 99.42% using UNSW-NB15, CICIDS2017, and NSL-KDD datasets, which is higher than the existing detection frameworks.

Cloud computing is one of the most rapidly evolving technologies today; it provides numerous advantages that increase its affordability and dependability for use in the company. This paper goes over the concepts of cloud computing, such as its characteristics, deployment model, and service model, discusses the various benefits of cloud computing, and highlights the most pressing issues and security concerns in cloud storage. As a consequence, that leads to a review of distinctive cryptography algorithms that meet the security requirements (CIA: confidentiality, integrity, and availability) that are used to secure communications in cloud computing situations. It also displays many algorithms depending on the previous studies, such as Blowfish, RSA, DES, AES, MD5, Feistel, SP, HIGHT, LED, Cybpher, PRESENT, RC6, Diamond2, mCrypton, SLIM, Klein, PUFFIN-2, SEA, CLEFIA, LBlock, TWINE, 2, ANU, ANU-II, NLBSIT, Piccolo, BORON, RECTANGLE, LICI, QTL, LOGIC, TRIVIUM, Fruit-v2, Fruit-80, A4, the Enocoro family, and Grain family, to make a comparison among them using many measurements. It found that modern and lightweight algorithms are more suitable for use in this field. Furthermore, the purpose of this paper is to make some suggestions for improving the safety and security of cloud computing technologies.

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

The rapid growth of cloud computing has led to an increased demand for security mechanisms to safeguard sensitive data and resources from cyber threats. Intrusion detection systems (IDS) play a crucial role in identifying unauthorized access or malicious activities within cloud environments. This paper presents a comparative study of machine learning (ML) techniques used in intrusion detection for cloud computing platforms. Various ML algorithms, including decision trees, support vector machines, k-nearest neighbors, and neural networks, are evaluated based on their performance in detecting different types of attacks. The study assesses the accuracy, efficiency, and scalability of these techniques in cloud environments, highlighting their strengths and limitations. The findings provide valuable insights into the selection of appropriate machine learning models for effective intrusion detection in dynamic and scalable cloud systems.

Optimized machine learning enabled intrusion detection 2 system for internet of medical things

The ubiquity and pay-for-use services offered by cloud computing draw more customers to use it as a result of these facilities. Even if cloud computing has many benefits, there are also some drawbacks. Cloud security features including confidentiality, availability, and integrity are susceptible to assaults. Therefore, security solutions are necessary for both cloud service providers and consumers in order to identify assaults and enhance cloud security. The biggest problem with cloud computing is security. The cloud has a variety of incursions. People are becoming increasingly conscious of the significance of network security as computer network technologies advance swiftly and internet technology develops more quickly. The greatest problem in computing is network security because assaults are happening more frequently. Due to these factors, a series of techniques known as intrusion detection systems (IDSs) has developed to prevent unauthorized use of a network’s resources. In this study, we will explore convolutional deep learning method along with supervised methods likes support vector machines and KNN models, and we will propose a hybrid solution for intrusion detection using CNN, SVM and KNN models.

Through continuous observation and modeling of normal behavior in networks, Anomaly-based Network Intrusion Detection System (A-NIDS) offers a way to find possible threats via deviation from the normal model. The analysis of network traffic based on the time series model has the advantage of exploiting the relationship between packages within network traffic and observing trends of behaviors over a period of time. It will generate new sequences with good features that support anomaly detection in network traffic and provide the ability to detect new attacks. Besides, an anomaly detection technique, which focuses on the normal data and aims to build a description of it, will be an effective technique for anomaly detection in imbalanced data. In this paper, we propose a combination model of Long Short Term Memory (LSTM) architecture for processing time series and a data description Support Vector Data Description (SVDD) for anomaly detection in A-NIDS to obtain the advantages of them. This model helps parameters in LSTM and SVDD are jointly trained with the joint optimization method. Our experimental results with KDD99 dataset show that the proposed combined model obtains high performance in intrusion detection, especially DoS and Probe attacks with 98.0% and 99.8%, respectively.

Cloud computing helps users to store and retrieve their data in the cloud online on an as-per-pay basis anytime, anywhere in the world. As a consequence, the security of data stored in the cloud serves as a key concern for cloud consumers due to ongoing hacking incidents in the cloud. Both cloud service providers (CSPs) and consumers face various attacks that pose a serious threat to both the organization and the user. Therefore, a robust intrusion detection system (IDS) needs to be configured to identify and avoid potential attacks in the cloud at an earlier stage. The state-of-the-art IDS system which used shallow machine learning techniques suffered from poor accuracy with a higher false alarm rate and higher response time. To overcome this problem, a hybrid semantic deep learning (HSDL) architecture is developed in this paper by integrating the long short-term memory (LSTM), convolutional neural network (CNN), and support vector machine (SVM) architectures. The semantic information present in the network traffic is identified using a semantic layer known as the Word2Vec embedding layer. The HSDL model classifies the intrusion present in the text along with its corresponding attack class. The normal text without any trace of intrusion is encrypted using an advanced encryption standard (AES) algorithm to enhance cloud storage security, and the optimal key with the largest key breaking time for the AES algorithm is selected using the crossover-based mine blast optimization algorithm (CMBA). The proposed HSDL scheme is evaluated and tested using two real-time intrusion detection benchmark datasets, namely NSL-KDD and UNSW-NB15. The proposed model achieves an accuracy of 99.98% for the NSL-KDD and 98.47% for the UNSW-NB15 dataset, respectively. The experimental and security analysis conducted proves the efficiency and robustness of the proposed scheme.