PDIS: A Service Layer for Privacy and Detecting Intrusions in Cloud Computing

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient’s information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker’s attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

Cloud computing has paved an excellent platform for the emergence of cost-effective technological solutions. However, security and privacy issues still remain as a stringent challenge during service catering. Explicitly, the service utility anomalies are liable to cause severe privacy and security issues in cloud service delivery. So, the overall performance of cloud service consumption and end-user applications’ service levels utility is degraded. The open access and distributed nature of the cloud computing is the major reason for its vulnerability to intruders. The security and privacy in cloud services have many challenges and problems still open for research. This paper proposes an intrusion detection method capable of detecting nine categories of attacks in two stages. This paper focuses on establishing a network-based intrusion detection mechanism using machine learning techniques. A model will be constructed with a supervised learning methodology using historical network flowdata and flowdata collected from the Internet.

In the rapidly evolving landscape of digital services, cloud computing has become a cornerstone for data storage, processing, and transfer, significantly amplifying security challenges. This environment faces numerous threats and security issues, including cyberattacks, data breaches, and malware, in addition to challenges related to ensuring confidentiality, integrity, and availability. This study aims to highlight the effective role of machine learning techniques in enhancing cloud security by detecting cyber threats, protecting data privacy, and preventing attacks. It underscores the pressing need to adopt intelligent solutions, given the limitations of traditional systems in addressing the complex and evolving threats within cloud environments. Through a critical review of recent research on this topic, the study examines various machine learning methods proven effective in tackling key cloud security issues, such as network intrusion detection, financial fraud detection, and malware identification. The findings confirm that machine learning techniques significantly improve the efficiency of security systems. However, challenges remain, including adapting to continuously evolving threats, ensuring the quality of available training data, and addressing the high computational costs of real-time model operation. The study recommends further research to enhance cloud security, improve adaptability to emerging threats, and establish a theoretical foundation to guide future efforts toward strengthening security in cloud environments.

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

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

The IT Industry and the wider media are abuzz with the phrase “cloud computing” and most of us have no idea what this latest terminology means. This paper starts by addressing the question – “what is cloud computing, and provides an understanding of the technology?” Cloud computing is related to technologies that has been around for ages, however, what can be done differently with the same technology when integrated with Internet is cloud computing.Individuals who might be considering cloud computing services for personal use will also benefit by the information provided. Web sites like Flickr, FaceBook, YouTube and others are used to store and share personal photos, music and videos for free. Easy as: 1). Register, 2). Upload, 3). Share. Free and easy, there has to be a downside, and there is awareness regarding this.There is information regarding cloud computing for businesses! Businesses, who wish to get on the “flight to the clouds” like their peers to keep their heads above the “cloud” and competition. With cloud service providers, businesses can tap into the IT services that they need, when they need, for as long as they need; without investing in any IT infrastructure. The result is a far more agile and cost-effective IT services and this paper looks at how and why.After looking at the good of cloud computing, the bad, and the ugly is revealed. Not all our experiences on the Internet have been totally positive. A range of security and trust issues exist. Individuals and businesses ask is “how secured are their information once it is with the cloud service providers?” There is analysis on the ownership of data and information stored with the cloud service providers and taxonomy of data that you post on social networking web sites.Finally, anecdotes about cloud computing that support our endeavour to compute in the cloud. Cloud computing is not a hype, it is a reality. Cloud computing is not a fad, it is here to stay. The question is no longer “will cloud computing happen?” but “how you are going to exploit this technology?”Keywordscloud computingvirtual computingcluster computingdistributed computingpervasive computing

Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future.

With the rapid growth of digitalization and the increasing volume of data, the cybersecurity threat landscape is expanding at an alarming rate. Intrusion Detection Systems (IDS) have become crucial in conjunction with firewalls to safeguard networks from malicious activities. In this work, four well-known cybersecurity datasets—CIC IDS 2017, NSL KDD, KDD Cup, and CIC IDS 2018—are employed to evaluate the effectiveness of various techniques for intrusion detection. Feature selection is performed using Mutual Information to enhance the relevance of selected features. Data sampling techniques are also explored, including Original Data, Random Under Sampling, Random Over Sampling, and a combination of both under and over-sampling to address data imbalance. To further improve the detection performance, a refined approach utilizing a Stacking Classifier combining Random Forest (RF) and Decision Tree (DT) with a Bagging Classifier is implemented. The results show that this approach achieves high performance across all datasets and sampling techniques, demonstrating its effectiveness in accurately detecting network intrusions in dynamic cybersecurity environments. “Index Terms – Incremental learning, network intrusion detection, machine learning, majority voting classifier, random sampling, Stacking classifier, Cyber Security”.

As the reliance on digital networks grows, the need for robust security measures becomes paramount. It offers a thorough analysis of creating and assessing a Network Intrusion Detection System (NIDS) incorporating various machine learning algorithms. The selected algorithms consist of K Nearest Neighbour, Naïve Bayes, Decision Tree, Support Vector Machine, and Logistic Regression. Moreover, widely recognised ensemble learning models like CatBoost, AdaBoost, Gradient Boost, Random Forest, and Extreme Gradient Boost. The core objective is to improve the efficiency of intrusion detection by evaluating various machine-learning models. Utilizes feature selection techniques based on chi-square to enhance the relevance and effectiveness of the selected features. The evaluation metrics, particularly Matthews’ Correlation Coefficient (MCC) and ROC Curves, are used in assessing the models’ efficacy. They offer valuable insights into the models’ capability to differentiate between normal and malicious network activities. This study aims to advance cybersecurity by comparing classical machine learning and ensemble models for network intrusion detection. Significantly, our results underscore XGBoost’s effectiveness with 0.996 accuracy and 0.993 MCC, in enhancing digital network security.

One of the problems in computer network security is Intrusion Detection. To detect it, we need a NIDS (Network Intrusion Detection). One example of NIDS is a honeypot. In this research, we use a kippo honeypot as a tool as an IDS. In this paper, honeypot logs are used for classifying SSH attacks using machine learning. The research used the original dataset from Kippo's log to classify the ssh attack as the primary source. We implement four algorithm classifications: Decision Tree, Naive Bayes, SVM, and Random Forest. Our research found that Decision Tree (DT) and random forest have the same score and better accuracy than naive Bayes and SVM. The accuracy and F1 score of RF and DT are 0.92 and 0.92, respectively.

Intrusion detection is an important issue for network security. Many machine learning based detection technologies have been put forward and obtained rather good results, such as Bayesian classifiers, Extreme learning machine. However, the performance of these methods is dependent on parameters to a great extent. Aiming at learning the optimal parameters of extreme learning machine for network intrusion detection, the paper proposes a new network intrusion detection based on hybrid rice algorithm optimized extreme learning machine (HRO-ELM). First, the extreme learning machine parameters are encoded as rice gene location, and the accuracy of test results represents the fitness value of the algorithm. Then the optimal parameters of the extreme learning machine are found by simulating rice breeding behavior, and a network intrusion detection classifier is established. Finally, the KDD99 data set is used and simulation results show that the HRO-ELM improves the accuracy of network intrusion detection and can meet the requirements of network intrusion online detection.

— In the modern computer world, use of the internet is increasing day by day. However, the increasing use of the internet creates some security issues. These days, such new type of security attacks occurs every day and it is not easy to detect and prevent those attacks effectively. One common method of attack involves sending large amount of request to site or server and server will be unable to handle such huge requests and site will be offline for many days. This type of attack is called distributed denial of service (DDOS) attack, which act as a major security threat to internet services and most critical attack for cyber security world. Detection and prevention of Distributed Denial of Service Attack (DDoS) becomes a crucial process for the commercial organizations that uses the internet. Different approaches have been adapted to process traffic information collected by monitoring stations (Routers and Servers) to distinguish malicious traffic such as DDoS attack from normal traffic in Intrusion Detection Systems (IDS). In general, Machine learning techniques can be designed and implemented with the intrusion systems to protect the organizations from malicious traffic. Specifically, supervised clustering techniques allow to effectively distinguishing the normal traffic from malicious traffic with good accuracy. In this paper, machine learning algorithms are used to detect DDoS attacks collected from “KDD cup 99 Dataset”, pre- processing and feature selection technique is used on the dataset to enhance the performance of the classifiers and reduce the detection time. The classification algorithms such as C4.5 decision tree and Navie Bayes is applied on the training dataset and the implementation of the algorithm is done using spyder tool. The performance comparison of algorithms is shown using confusion matrix and it is found that C4.5 decision is more efficient in detection of DDOS attack. The proposed method can be used as DDoS defense system.

Cloud computing is a new computing model which uses virtualization technology, distributed computing, parallel computing and other existing technologies to achieve cloud service virtualization and economies of scale, whilst increasingly overwhelming cloud security issues has brought great challenges and concerns to the cloud services providers and cloud users, especially trust and privacy issues with regard to cloud computing and cloud shared storage associated security issues. In the paper, we expound the basic concepts of cloud computing, deployment models, service models and key features, analyze and outline the currently highlighted cloud security issues, report the status quo of cloud computing security, investigate the prevalent and typical cloud computing security problem key solving techniques, and thus render a comprehensive cloud computing security technical reference model, which is composed of associated cloud security solving techniques that result from inevitably multi-faceted cloud security issues. The model is expected to alleviate prominent cloud security issues. This paper generalizes cloud security technology research directions and further development space of cloud security technology and standardization.KeywordsCloud computingSecurity issuesTrusted cloud computing solving techniquesShared storage security solving techniquesCloud security technical reference model

One of the key challenges of the machine learning (ML) based intrusion detection system (IDS) is the expensive computation time which is largely caused by the redundant, incomplete, and unrelated features contain in the IDS datasets. To overcome such challenges and ensure building efficient and more accurate IDS models, many researchers utilize preprocessing techniques such as normalization and feature selection, and a hybrid modeling approach is typically used. In this work, we propose a hybrid IDS modeling approach with an algorithm for feature selection (FS) and another for building the IDS. The FS method is a wrapper-based FS with a decision tree as the feature evaluator. Five selected ML algorithms are individually used in combination with the proposed FS method to build five IDS models using the UNSW-NB15 dataset. As a baseline, five more IDS models are built, in a single modeling approach, using the full features of the datasets. We evaluate the effectiveness of our proposed method by comparing it with the baseline models and also with state-of-the-art works. Our method achieves the best DR of 97.95% and proved to be quite effective in comparison to state-of-the-art works. We, therefore, recommend its usage especially in IDS modeling with the UNSW-NB15 dataset.