Microsoft has promoted ASP.NET’s new web services more than almost any other part of the.NET Framework. But despite their efforts, confusion is still widespread about what a web service is and, more importantly, what it’s meant to accomplish. This chapter introduces web services and explains their role in Microsoft’s vision of the programmable web. Along the way, you’ll learn about the open standards plumbing that allows web services to work and removes some of the confusion surrounding technical terms like WSDL (Web Service Description Language), SOAP, and UDDI (universal description, discovery, and integration).

Web Services Architecture

Internet of Things (IoT) brings the third development wave of the global information industry, which makes users, network, and perception devices cooperate more closely. However, if IoT has security problems, it may cause a variety of damage and even threaten human lives and properties. To improve the abilities of monitoring, providing emergency response, and predicting the development trend of IoT security, a new paradigm called network security situation awareness (NSSA) is proposed. However, it is limited by its ability to mine and evaluate security situation elements from multi-source heterogeneous network security information. To solve this problem, this paper proposes an IoT network security situation awareness model using a situation reasoning method based on semantic ontology and user-defined rules. Ontology technology can provide a unified and formalized description to solve the problem of semantic heterogeneity in the IoT security domain. In this paper, four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability, and network flow. Furthermore, user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of our proposed ontology model. The examples in real IoT scenarios show that the ability of the network security situation awareness that adopts our situation reasoning method is more comprehensive and more powerful reasoning abilities than the traditional NSSA methods.

Network Security Situation Awareness Based on Semantic Ontology and User-Defined Rules for Internet of Things

Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

The current digital world is using the internet almost everywhere. The usage of internet has been increasing, however, threats are also increasing in numbers. One such threat is DoS attack which uses reasonable service requests to gain excessive computing and network resources and results in an inability to access them by legitimate users. The DoS attack can happen at different layers of OSI model such as network, transport and application layers. The aim of this paper is to detect DoS attack effectively using Machine learning (ML) and Neural Network (NN) algorithms. The detection is specifically focused on application layer DoS attack detection rather than at transport and network DoS attack detection. The latest DoS attack dataset CIC IDS 2017 dataset is used in the experiment. The experimentation has divided the dataset into different splits and the best split is found for each algorithm i.e. RF and MLP. Results of RF and MLP are compared and it is shown that RF provides better results than MLP.

DoS Attack Detection Using Machine Learning and Neural Network

The past several years we have witnessed that information has become the most precious asset, while protection and security of information is becoming an ever greater challenge due to the large amount of knowledge necessary for organizations to successfully withstand external threats and attacks. This knowledge collected from the domain of information security can be formally described by security ontologies. A large number of researchers during the last decade have dealt with this issue, and in this paper we have tried to identify, analyze and systematize the relevant papers published in scientific journals indexed in selected scientific databases, in period from 2004 to 2014. This paper gives a review of literature in the field of information security ontology and identifies a total of 52 papers systematized in three groups: general security ontologies (12 papers), specific security ontologies (32 papers) and theoretical works (8 papers). The papers were of different quality and level of detail and varied from presentations of simple conceptual ideas to sophisticated frameworks based on ontology.

/pdf/ontology-in-information-security-2q3hvabws5.pdf

Ontology in Information Security

The hacker attempts distributed denial of service (DDoS) attacks towards network resources to disturb or deny services. The hacker degrades the quality of service to legitimate users by performing reflection and exploitation based DDoS attacks with a trusted third party server that hides information of the attacker. It is, therefore, necessary to propose an intelligent intrusion detection system to detect reflection and exploitation based DDoS attacks efficiently and effectively. The present study proposes a feature reduction method by the combination of information gain (IG) and correlation (CR) feature selection techniques. This study presents a DDoS attack detection framework to detect reflection and exploitation based DDoS attacks in an efficient manner. The framework is tested on the latest DDoS evaluation (CICDDoS2019) dataset with J48 classifier. The feature reduction method obtains minimum and maximum reduction by 56 and 82.92% respectively, of the original features. The experimentation results show that the proposed framework outperforms using a reduced features subset. The validation of the proposed framework on knowledge discovery and data mining (KDD Cup 1999) dataset provides improvement in performance for binary and multi-level classification using feature reduction by 60.97% of the original features. The proposed feature reduction method is also compared to the relevant existing feature selection methods used for intrusion detection on CICDoS 2019 and KDD Cup 1999 datasets.

A feature reduction based reflected and exploited DDoS attacks detection system

Feature selection for intrusion detection system in Internet-of-Things (IoT)

Intrusion Detection System (IDS) must reliably detect malicious activity. The expansion of web application also exponentially increases cyber threats. Current survey shows that application layer is more vulnerable to web attacks. There are more than 75% of attacks are deployed at application layer and out of that 90% are vulnerable to attacks. In this paper, we address issues of existing IDS i.e. low false positive rate, low false negative rate and data overload. We discuss about use of semantic web in the Intrusion Detection Systems. This article presents a proposition of using Semantic Web and Ontology concepts to define an approach to analyze Security logs with the goal to identify possible security issues. It extracts semantic relations between computer attacks and intrusions in an Intrusion Detection System. Ontology provides to enable, reuse of domain knowledge and it is also easier to understand and update legacy data.

Deepak Kshirsagar

Papers

DoS Attack Detection Using Machine Learning and Neural Network

A feature reduction based reflected and exploited DDoS attacks detection system

Feature selection for intrusion detection system in Internet-of-Things (IoT)

Ontology for Detection of Web Attacks

An efficient feature reduction method for the detection of DoS attack