Cloud Base Intrusion Detection System using Convolutional and Supervised Machine Learning

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.

The swift growth of cloud computing has heightened cybersecurity vulnerabilities, demanding robust intrusion detection systems (IDS). Conventional IDS models face challenges, such as excessive false positives and limited flexibility. This study introduces Spinal Stacked AutoEncoder Net (SpinalSAENet), an innovative hybrid deep-learning-based IDS that merges SpinalNet and Deep Stacked AutoEncoders (DSAE) to enhance anomaly detection and data integrity verification. The system employs feature extraction and Chebyshev distance-based fusion to improve classification, while Principal Component Analysis (PCA) is utilised to reduce dimensionality, thereby increasing computational efficiency. When tested on the Bot-IoT dataset, SpinalSAENet demonstrated superior performance with 96.87% accuracy, 95.4% recall, 96.1% precision, and a 95.7% F1-score, surpassing Decision Trees, Random Forests, and Support Vector Machines. The incorporation of SHA-256 hashing and Merkle tree proofs ensures data integrity, offering a multitiered security approach. Its streamlined architecture and cloud-native scalability (Docker and Kubernetes) facilitate real-time deployment in cloud environments. This paper presents a highly precise and scalable IDS framework capable of real-time intrusion detection and data integrity verification. Subsequent research will investigate the resistance to adversarial attacks, explainable AI, and serverless deployment to further enhance cloud security.

The network security is getting more important due to the wide-spread computer viruses and increasing network attacks. Nowadays, more and more security mechanisms, such as firewalls and intrusion detection systems (IDS), are introduced to protect the network from malicious attacks. This paper proposes an agent and service based intrusion detection and response system for active network. In contrast to a traditional passive network, an active network gives the nodes programmable ability to exercise various active network technologies. The intrusion response, service deployment, and service update mechanisms are centered on this technology. The proposed model of intrusion detection and response system (IDRS) catches network attacks and responses to stop the attacks at the first time to reduce the damage. Detecting, reporting, and responding capabilities are all embedded and integrated in the proposed system. A prototype system is developed using a novel data mining technology (the support vector machine) to enhance the detection function. In addition, several experiments were conducted to verify the system and results showed that the system was able to effectively identify the intrusions and respond promptly. Experiments also showed that the support vector machine outperforms the competitive neural networks in identifying the intrusions.

Cloud computing is currently the most hyped and popular paradigm in the domain of distributed computing. Multiple vendors are providing infrastructure, platform, and software-related cloud products or solutions, and the number of such vendors is growing rapidly. But the reality is that there is very little in the form of standards in general and interoperability standards in particular. This leads to a challenging situation for cloud service consumers as they need to use solutions provided by multiple cloud vendors in order to fulfill their business requirements, and the lack of standardization leads to lack of interoperability among the solutions provided by varied vendors. Service brokers act as intermediaries between the cloud service providers and consumers. The role of service brokers becomes critical in situations that need integration among multiple systems as they bridge the interoperability gaps among multiple cloud solutions and thus compensate for the lack of standards. They do so by acting as agents adding value on behalf of the service consumers. The usage of such cloud brokers also helps to future-proof the solution as service brokers add a layer of indirection, thus enabling loose coupling among the cloud service consumers and providers. The aim of this chapter is to review the role of cloud service brokers in cloud computing. The review includes consolidation of the definitions and characteristics of cloud service brokers, review of the current brokerage solutions, and interfacing requirements. Also presented are a high-level cloud service broker functional architecture, criteria for selection of a cloud service broker, future trends and review of benefits, and challenges of using cloud service brokers.

IT sector is growing day by day, its data and energy costs are also increasing, which needs to access the high-end computing capabilities. To fulfill these requirements, there is a need to shift from traditional computing practices to new computing practices that give access to a broader network, unlimited resources, and enables self-service on demand at a reasonable cost using pay per use method. All these requirements can be fulfilled with cloud computing. Cloud Computing has a number of advantages as well as disadvantages. Security is a major threat to the cloud computing because the data placed on cloud servers is always vulnerable as there are lots of attackers on the internet who tries to compromise the security of the cloud. To overcome this issue, we use security appliances in the form of physical machines or by virtualization. A significant amount of power is consumed by security appliances which not only leads to the high energy costs but also a major threat to the environment as carbon-dioxide is released. So there is a need to find optimal solutions for providing security in cloud computing environment in an efficient manner. In this paper, a comprehensive survey on existing cloud security frameworks has been done. Based upon the limitations on existing frameworks a new framework has been proposed to provide security in virtual networks that is based on Intrusion Detection and Prevention System (IDPS) and its prototype implementation has also been done.

IDS (Intrusion Detection System) plays a vital role in network security in that it monitors system activities to identity unauthorized use, misuse or abuse of computer and network system. For the simulation of IDS a model has been constructed based on the DEVS (Discrete EVent system Specification) formalism. With this model we can simulate whether the intrusion detection, which is a core function of IDS, is effectively done under various different conditions. As intrusions become more sophisticated, it is beyond the scope of any one IDS to deal with them. Thus we placed multiple IDS agents in the network where the information helpful for detecting the intrusions is shared among these agents to cope effectively with attackers. Each agent cooperates through the BBA (Black Board Architecture) for detecting intrusions. If an agent detects intrusions, it transfers attacker's information to a Firewall. Using this mechanism attacker's packets detected by IDS can be prevented from damaging the network.

Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks. The design of IDS using a machine learning (ML) technique comprises a series of methods that can learn patterns from data and forecast the outcomes consequently. In this background, the current study designs a novel multi-objective seagull optimization algorithm with a deep learning-enabled vulnerability detection (MOSOA-DLVD) technique to secure the cloud platform. The MOSOA-DLVD technique uses the feature selection (FS) method and hyperparameter tuning strategy to identify the presence of vulnerabilities or attacks in the cloud infrastructure. Primarily, the FS method is implemented using the MOSOA technique. Furthermore, the MOSOA-DLVD technique uses a deep belief network (DBN) method for intrusion detection and its classification. In order to improve the detection outcomes of the DBN algorithm, the sooty tern optimization algorithm (STOA) is applied for the hyperparameter tuning process. The performance of the proposed MOSOA-DLVD system was validated with extensive simulations upon a benchmark IDS dataset. The improved intrusion detection results of the MOSOA-DLVD approach with a maximum accuracy of 99.34% establish the proficiency of the model compared with recent methods.

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

Cloud computing not only provides convenience for users, but also brings certain information security threats to users. How to effectively protect the user’s information security has become one of the core issues of network security. This paper analyzes the problems and reasons of network security in cloud computing environment, and points out the main ways to improve network security. This paper analyzes the network security problems in the cloud computing environment, and discusses the information security technology in the network. This paper studies the definition, characteristics, classification and core technology of cloud computing security, analyzes the security risk of cloud computing network, and puts forward the cloud computing network security model and security level evaluation method from different angles. Level experiment results show that the network security model in cloud environment is feasible, computing can fully meet the development requirements of the current network information age, and effectively standardize the network security operation environment. The results show that: in 2016, the number of articles related to cloud computing is at least 2653, and in 2020, the number of articles related to cloud computing is at most 6438.

Nowadays, Network Security is growing rapidly because no user specifically wants his/her computer system to be intruded by a malicious user or an attacker. The growing usage of cloud computing provides a different type of services, which leads users to face security issues. There are different types of security issues such as hacking intrusions worms and viruses, DoS etc. Since the entire needed resources are associated with everyone and are centrally monitored by main controller in cloud computing area it creates a simplest way for intruders. In addition, an experienced or knowledgeable attacker can get to make out the system’s weakness and can hack the sensible information or any resource and so, it is essential to provide protection against attack or intrusion. Additionally, to handle poor performance or low latency for the clients, filtering malicious accesses becomes the main concern of an administrator. Some of the traditional Intrusion Detection and Prevention Systems fail to overcome the abovementioned problems. As a result, this research proposes a novel approach named Enhanced Intrusion Detection Prevention System to prevent, protect and respond the various network intrusions in the internet. Our proposed system use client-server architecture, which contains main server and several clients. Clients record the doubtful actions taking place in the Internet and record the suspicious information. Then, this recorded information is forwarded to the main server for further usage. After that, the main server analyses the received data and then make decision whether to provide a security alert or not; then the received information is displayed via an interface. In addition, server verifies the data packets using IDPS and classifies the attacks using Support Vector Machine. Finally, as per the attack type Honeypot system sends irrelevant data to the attacker. The experimentally deployed proposed system results are shown in our framework which validates the authorized users and prevents the intrusions effectively rather than other frameworks or tools.

Enhancement of Security in Cloud Maas and JUJU Using Foxiproxy

Machine learning (ML) and deep learning (DL) are used in numerous fields, particularly to develop effective intrusion detection systems (IDS). Existing wireless network IDS, which rely on a single ML algorithm and have limitations. These include a high rate of false positives, difficulties in recognizing distinct attack patterns, and a high acquisition cost for annotated training datasets. However, hostile threats are always evolving, networks need a smart security solution. In comparison to other ML approaches, DL algorithms are more successful in intrusion detection. This paper presents a DL based ensemble model that combines Multi-verse through Chaotic Atom Search Optimization (MCA) for preprocessing, which eliminates unsolicited/recurrent information in the dataset. The process of optimized feature selection uses Principal Component Analysis (PCA), Chaotic Manta-ray Foraging Optimizations (CMFO), and a grounded grouping method to partition the optimized feature dataset into k-diverse clusters. The recommended model then stacks Support Vector Machine (SVM) as the ensemble model’s meta-learner classifier, pre-training the hybrid DL prototypes using the optimized feature dataset cluster. The CNN-LSTM and CNN-GRU models, which integrate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), are the hybrid DL prototype’s key components. The suggested model’s performance has been enhanced and compared to six ML techniques: NB, SVM, J48, RF, MLP, and kNN models, utilizing measures such as accuracy, precision, recall, and F-measure. The public can access the Aegean Wi-Fi Intrusion Dataset (AWID) which is used for evaluating the recommended model and is outperformed the contemporary models in the literature.

Chapter 5 - Intrusion Prevention and Detection Systems

With the development of cloud computing, cloud security issues have become increasingly prominent. As an important part of cloud computing network security, intrusion detection system has attracted people's attention. How to make the system's response decision has become the focus of the research. For the characteristics of cloud computing attackdefence behaviour, a security intrusion detection of cloud computing based on game theory is proposed. By modelling the behaviours of cloud service provider and cloud malicious user, the best strategies were analysed respectively. According to the sensitivity analysis, the interaction between the parameters in the model was analysed, and it can help to make the cloud computing intrusion detection strategy.

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective