As an emerging technology, the Internet of Things (IoT) revolutionized the global network comprising of people, smart devices, intelligent objects, data, and information. The development of IoT is still in its infancy and many related issues need to be solved. IoT is a unified concept of embedding everything. IoT has a great chance to make the world a higher level of accessibility, integrity, availability, scalability, confidentiality, and interoperability. However, how to protect IoT is a challenging task. System security is the foundation for the development of IoT. This article systematically reviews IoT cybersecurity. The key considerations are the protection and integration of heterogeneous smart devices and information communication technologies (ICT). This review provides useful information and insights to researchers and practitioners who are interested in cybersecurity of IoT, including the current research of IoT cybersecurity, IoT cybersecurity architecture and taxonomy, key enabling countermeasures and strategies, major applications in industries, research trends and challenges.

Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics

Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across the Internet. Edge computing is an evolving computing paradigm that brings computation and information storage nearer to the end-users to improve response times and spare transmission capacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones. However, CC and edge computing have security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Machine learning (ML) is the investigation of computer algorithms that improve naturally through experience. In this review paper, we present an analysis of CC security threats, issues, and solutions that utilized one or several ML algorithms. We review different ML algorithms that are used to overcome the cloud security issues including supervised, unsupervised, semi-supervised, and reinforcement learning. Then, we compare the performance of each technique based on their features, advantages, and disadvantages. Moreover, we enlist future research directions to secure CC models.

https://www.mdpi.com/2079-9292/9/9/1379/pdf

A Review of Machine Learning Algorithms for Cloud Computing Security

Internet of Things (IoT) has been a massive advancement in the Information and Communication Technology (ICT). It is projected that over 50 billion devices will become part of the IoT in the next few years. Security of the IoT network should be the foremost priority. In this paper, we evaluate the security challenges in the four layers of the IoT architecture and their solutions proposed from 2010 to 2016. Furthermore, important security technologies like encryption are also analyzed in the IoT context. Finally, we discuss countermeasures of the security attacks on different layers of IoT and highlight the future research directions within the IoT architecture.

IoT security: A layered approach for attacks & defenses

Security features embedded in computer aided design (CAD) solid models for additive manufacturing

Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. In the proposed HNIDS, a PSO method improves the vector. GA is enhanced by adding a multi-objective function, which selects the best features and achieves improved fitness outcomes to explore the essential features and helps minimize dimensions, enhance the true positive rate (TPR), and lower the false positive rate (FPR). In the next phase, an IRF eliminates the less significant attributes, incorporates a list of decision trees across each iterative process, supervises the classifier’s performance, and prevents overfitting issues. The performance of the proposed method and existing ML methods are tested using the benchmark datasets NSL-KDD. The experimental findings demonstrated that the proposed HNIDS method achieves an accuracy of 98.979% on BCC and 88.149% on MCC for the NSL-KDD dataset, which is far better than the other ML methods i.e., SVM, RF, LR, NB, LDA, and CART.

/pdf/a-hybrid-intrusion-detection-model-using-ega-pso-and-2o0l7hzf.pdf

A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method

Cloud Computing provides an efficient and flexible way for services to meet escalating business needs. Cloudshared infrastructure and associated services make it cost effective alternative to traditional approaches. However, they may also introduce security breaches and privacy issues. As more cloud based applications keep evolving, the associated security threats are also growing. Many research works on cloud security exist in partial forms of either specifically on cloud issues or virtualization-related security issues. In this paper, an attempt has been made to consolidate the various security threats in a classified manner and to illustrate how cloud and virtualization vulnerabilities affect the different cloud service models.

/pdf/a-survey-on-security-threats-for-cloud-computing-2nem09kvxe.pdf

A Survey on Security Threats for Cloud Computing

Virtualization, which is reflected as the backbone of cloud computing, provides cost-effective resource sharing. Owed to the existence of multiple virtual machines, it passages several challenges. Among the numerous virtualization attacks, the Distributed Denial of Service, widely known as the DDoS attack, is considered to be the momentous. These spells consumes large amount of server resources here after denies access to genuine users. DDoS attacks impact is more in cloud computing as sharing of resources is the innate character of a cloud. At hand are a few kinds of literature dealing with the mitigation of DDoS attacks using single server tactic nonetheless they suffer from poor response time. The proposed model in this paper uses multiple servers to deal with the alleviation of DDoS. An initial test result on the proposed model has provided us with better scalability and protection against further attacks.

A Novel Approach to Protect Cloud Environments Against DDOS Attacks

Amongst different threats relating to cloud environment, the Distributed Denial of Service (DDoS) attacks play a significant role as the attack may affect many distributed systems. In order to mitigate with these types of attacks, the authors have already proposed a novel N-S architecture and its associated algorithms in the form of a suite. This paper deals with the performance analysis of the said suite under different categories such as during non-attacks, attacks and also post-attack periods. The performance has also been compared with a conventional single-server model and results indicate good improvements in terms of response time.

http://ieeexplore.ieee.org/iel7/7589474/7724213/07724908.pdf

Nagaraju Kilari

Papers

A Survey on Security Threats for Cloud Computing

A Novel Approach to Protect Cloud Environments Against DDOS Attacks

The performance analysis of N-S architecture to mitigate DDoS attack in cloud environment