State of the Art Intrusion Detection System for Cloud Computing

The term Cloud computing is not new anymore in computing technology. This form of computing technology previously considered only as marketing term, but today Cloud computing not only provides innovative improvements in resource utilisation but it also creates a new opportunities in data protection mechanisms where the advancement of intrusion detection technologies  are blooming rapidly. From the perspective of security, Cloud computing also introduces concerns about data protection and intrusion detection mechanism. This paper surveys, explores and informs researchers about the latest developed Cloud Intrusion Detection Systems by providing a comprehensive taxonomy and investigating possible solutions to detect intrusions in cloud computing systems. As a result, we provide a comprehensive review of Cloud Intrusion Detection System research, while highlighting the specific properties of Cloud Intrusion Detection System. We also present taxonomy on the key issues in Cloud Intrusion Detection System area and discuss the different approaches taken to solve the issues. We conclude the paper with a critical analysis of challenges that have not fully solved

Shallow and deep networks intrusion detection system : a taxonomy and survey

Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems

Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed

Intrusion Detection Systems in Cloud Computing: A Contemporary Review of Techniques and Solutions

Rapid growth of resources and escalating cost of infrastructure is leading organizations to adopt cloud computing. Cloud computing provides high performance, efficient utilization, and on-demand availability of resources. However, the cloud environment is vulnerable to different kinds of intrusion attacks which involve installing malicious software and creating backdoors. In a cloud environment, where businesses have hosted important and critical data, the security of underlying technologies becomes crucial. To mitigate the threat to cloud environments, Intrusion Detection Systems (IDS) are a layer of defense. The aim of this survey paper is to review IDS techniques proposed for the cloud. To achieve this objective, the first step is defining the limitations and unique characteristics of each technique. The second step is establishing the criteria to evaluate IDS architectures. In this paper, the criteria used is derived from basic characteristics of cloud. Next step is a comparative analysis of various existing intrusion detection techniques against the criteria. The last step is on the discussion of drawbacks and open issues, comprehended from the evaluation, due to which implementation of IDS in cloud environment face hurdles

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) network is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using an IoT Data set, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks

Intelligent and Improved Self-Adaptive Anomaly based Intrusion Detection System for Networks

With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Intrusions or attacks on computers and networks are activities or attempts to jeopardize main system security objectives, which called as confidentiality, integrity and availability. They lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. There is a great need for an effective Network Intrusion Detection System (NIDS), which are security tools designed to interpret the intrusion attempts in incoming network traffic, thereby achieving a solid line of protection against inside and outside intruders. In this work, we propose to optimize a very popular soft computing tool prevalently used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel machine learning framework called “ISAGASAA”, based on Improved Self-Adaptive Genetic Algorithm (ISAGA) and Simulated Annealing Algorithm (SAA). ISAGA is our variant of standard Genetic Algorithm (GA), which is developed based on GA improved through an Adaptive Mutation Algorithm (AMA) and optimization strategies. The optimization strategies carried out are Parallel Processing (PP) and Fitness Value Hashing (FVH) that reduce execution time, convergence time and save processing power. While, SAA was incorporated to ISAGA in order to optimize its heuristic search. Experimental results based on Kyoto University benchmark dataset version 2015 demonstrate that our optimized NIDS based BPNN called “ANID BPNN-ISAGASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. Moreover, improvement of GA through FVH and PP saves processing power and execution time. Thus, our model is very much convenient for network anomaly detection.

Federated learning for distributed intrusion detection systems in public networks

Abstract. The rapid integration of technologies such as IoT devices, cloud, and edge computing has led to a progressively interconnected network of intelligent environments, services, and public infrastructures. This evolution highlights the critical need for sophisticated and self-governing Intrusion Detection Systems (IDS) to enhance trust and ensure the security and integrity of these interconnected environments. Furthermore, the advancement of AI-based Intrusion Detection Systems hinges on the effective utilization of high-quality data for model training. A considerable number of datasets created in controlled lab environments have recently been released, which has significantly facilitated researchers in developing and evaluating resilient Machine Learning models. However, a substantial portion of the architectures and datasets available are now considered outdated. As a result, the principal aim of this thesis is to contribute to the enhancement of knowledge concerning the creation of contemporary testbed architectures specifically designed for defense systems. The main objective of this study is to propose an innovative testbed infrastructure design, capitalizing on the broad connectivity panOULU public network, to facilitate the analysis and evaluation of AI-based security applications within a public network setting. The testbed incorporates a variety of distributed computing paradigms including edge, fog, and cloud computing. It simplifies the adoption of technologies like Software-Defined Networking, Network Function Virtualization, and Service Orchestration by leveraging the capabilities of the VMware vSphere platform. In the learning phase, a custom-developed application uses information from the attackers to automatically classify incoming data as either normal or malicious. This labeled data is then used for training machine learning models within a federated learning framework (FED-ML). The trained models are validated using previously unseen network data (test data). The entire procedure, from collecting network traffic to labeling data, and from training models within the federated architecture, operates autonomously, removing the necessity for human involvement. The development and implementation of FED-ML models in this thesis may contribute towards laying the groundwork for future-forward, AI-oriented cybersecurity measures. The dataset and testbed configuration showcased in this research could improve our understanding of the challenges associated with safeguarding public networks, especially those with heterogeneous environments comprising various technologies

Network Intrusion Detection System:A systematic study of Machine Learning and Deep Learning approaches

The rapid advances in the internet and communication fields have resulted in ahuge increase in the network size and the corresponding data. As a result, manynovel attacks are being generated and have posed challenges for network secu-rity to accurately detect intrusions. Furthermore, the presence of the intruderswiththeaimtolaunchvariousattackswithinthenetworkcannotbeignored.Anintrusion detection system (IDS) is one such tool that prevents the network frompossible intrusions by inspecting the network traffic, to ensure its confidential-ity, integrity, and availability. Despite enormous efforts by the researchers, IDSstillfaceschallengesinimprovingdetectionaccuracywhilereducingfalsealarmrates and in detecting novel intrusions. Recently, machine learning (ML) anddeep learning (DL)-based IDS systems are being deployed as potential solutionsto detect intrusions across the network in an efficient manner. This article firstclarifiestheconceptofIDSandthenprovidesthetaxonomybasedonthenotableML and DL techniques adopted in designing network-based IDS (NIDS) sys-tems. A comprehensive review of the recent NIDS-based articles is provided bydiscussing the strengths and limitations of the proposed solutions. Then, recenttrends and advancements of ML and DL-based NIDS are provided in terms ofthe proposed methodology, evaluation metrics, and dataset selection. Using theshortcomings of the proposed methods, we highlighted various research chal-lenges and provided the future scope for the research in improving ML andDL-based NIDS