Abstract:In the past few decades, the rise in attacks on communication devices in networks has resulted in a reduction of network functionality, throughput, and performance. To detect and mitigate these network attacks, researchers, academicians, and practitioners developed Intrusion Detection Systems (IDSs) with automatic response systems. The response system is considered an important component of IDS, since without a timely response IDSs may not function properly in countering various attacks, especially on a real-time basis. To respond appropriately, IDSs should select the optimal response option according to the type of network attack. This research study provides a complete survey of IDSs and Intrusion Response Systems (IRSs) on the basis of our indepth understanding of the response option for different types of network attacks. Knowledge of the path from IDS to IRS can assist network administrators and network staffs in understanding how to tackle different attacks with state-of-the-art technologies.

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Internet of things (IoT) is a contemporary technology, which links a tremendous number of devices with each other to ease the life via many useful services such as information exchange, monitoring, and control. IoT comprises different types of entities such as sensors and RFID tags, which mostly deployed in unattended, sensitive, and hostile territories. Due to rapid scalability and high heterogeneity, traditional security approaches fails to provide adequate security mechanisms for the current IoT infrastructure. The possibility of insecure and unattended deployment make some of IoT's entities subject to be captured physically by the attackers. As a result, the victim device can be exploited as a gateway to compromise the entire network. Furthermore, an entity may not work correctly because of resources constraints or instability of network's link. Recently, trust and reputation (TR) extended in IoT to monitor the behaviors deviation of IoT entities. Many TR models introduced, to incorporate the trust concepts in IoT as a new security paradigm. In this study, we provide thematic taxonomy for trust in IoT, considering several issues such as understanding of trust entity roles, trust properties, trust applications, levels of trust management, trust metrics, trust computation schemes and attacks on TR. Finally, the survey presents advances and open research challenges in the IoT's trust.

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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