Biblio

The Semantic Web today is a web that allows for intelligent knowledge retrieval by means of semantically annotated tags. This web also known as Intelligent web aims to provide meaningful information to man and machines equally. However, the information thus provided lacks the component of trust. Therefore we propose a method to embed trust in semantic web documents by the concept of provenance which provides answers to who, when, where and by whom the documents were created or modified. This paper demonstrates the same using the Manchester approach of provenance implemented in a University Ontology.

In recent years, the usage of unmanned aircraft systems (UAS) for security-related purposes has increased, ranging from military applications to different areas of civil protection. The deployment of UAS can support security forces in achieving an enhanced situational awareness. However, in order to provide useful input to a situational picture, sensor data provided by UAS has to be integrated with information about the area and objects of interest from other sources. The aim of this study is to design a high-level data fusion component combining probabilistic information processing with logical and probabilistic reasoning, to support human operators in their situational awareness and improving their capabilities for making efficient and effective decisions. To this end, a fusion component based on the ISR (Intelligence, Surveillance and Reconnaissance) Analytics Architecture (ISR-AA) [1] is presented, incorporating an object-oriented world model (OOWM) for information integration, an expressive knowledge model and a reasoning component for detection of critical events. Approaches for translating the information contained in the OOWM into either an ontology for logical reasoning or a Markov logic network for probabilistic reasoning are presented.

In this paper, we present a security and privacy enhancement (SPE) framework for unmodified mobile operating systems. SPE introduces a new layer between the application and the operating system and does not require a device be jailbroken or utilize a custom operating system. We utilize an existing ontology designed for enforcing security and privacy policies on mobile devices to build a policy that is customizable. Based on this policy, SPE provides enhancements to native controls that currently exist on the platform for privacy and security sensitive components. SPE allows access to these components in a way that allows the framework to ensure the application is truthful in its declared intent and ensure that the user's policy is enforced. In our evaluation we verify the correctness of the framework and the computing impact on the device. Additionally, we discovered security and privacy issues in several open source applications by utilizing the SPE Framework. From our findings, if SPE is adopted by mobile operating systems producers, it would provide consumers and businesses the additional privacy and security controls they demand and allow users to be more aware of security and privacy issues with applications on their devices.

In this paper, we analyze the security of cyber-physical systems using the ADversary VIew Security Evaluation (ADVISE) meta modeling approach, taking into consideration the efects of physical attacks. To build our model of the system, we construct an ontology that describes the system components and the relationships among them. The ontology also deines attack steps that represent cyber and physical actions that afect the system entities. We apply the ADVISE meta modeling approach, which admits as input our deined ontology, to a railway system use case to obtain insights regarding the system’s security. The ADVISE Meta tool takes in a system model of a railway station and generates an attack execution graph that shows the actions that adversaries may take to reach their goal. We consider several adversary proiles, ranging from outsiders to insider staf members, and compare their attack paths in terms of targeted assets, time to achieve the goal, and probability of detection. The generated results show that even adversaries with access to noncritical assets can afect system service by intelligently crafting their attacks to trigger a physical sequence of efects. We also identify the physical devices and user actions that require more in-depth monitoring to reinforce the system’s security.

Software engineering is a complex filed with diverse specialties. By the growth of Internet based applications, information security plays an important role in software development process. Finding expert software engineers who have expertise in information security requires too much effort. Stack Overflow is the largest social Q&A Website in the field of software engineering. Stack Overflow contains developers' posts and answers in different software engineering areas including information security. Security related posts are asked in conjunction with various technologies, programming languages, tools and frameworks. In this paper, the content and metadata of Stack Overflow is analysed to find experts in diverse software engineering security related concepts using information security ontology.

Cloud computing is one of the happening technologies in these years and gives scope to lot of research ideas. Banks are likely to enter the cloud computing field because of abundant advantages offered by cloud like reduced IT costs, pay-per-use modeling, and business agility and green IT. Main challenges to be addressed while moving bank to cloud are security breach, governance, and Service Level Agreements (SLA). Banks should not give prospect for security breaches at any cost. Access control and authorization are vivacious solutions to security risks. Thus we are proposing a knowledge based security model addressing the present issue. Separate ontologies for subject, object, and action elements are created and an authorization rule is framed by considering the inter linkage between those elements to ensure data security with restricted access. Moreover banks are now using Software as a Service (SaaS), which is managed by Cloud Service Providers (CSPs). Banks rely upon the security measures provided by CSPs. If CSPs follow traditional security model, then the data security will be a big question. Our work facilitates the bank to pose some security measures on their side along with the security provided by the CSPs. Banks can add and delete rules according to their needs and can have control over the data in addition to CSPs. We also showed the performance analysis of our model and proved that our model provides secure access to bank data.

Targeted attacks on IT systems are a rising threat against the confidentiality of sensitive data and the availability of systems and infrastructures. Planning for the eventuality of a data breach or sabotage attack has become an increasingly difficult task with the emergence of advanced persistent threats (APTs), a class of highly sophisticated cyber-attacks that are nigh impossible to detect using conventional signature-based systems. Understanding, interpreting, and correlating the particulars of such advanced targeted attacks is a major research challenge that needs to be tackled before behavior-based approaches can evolve from their current state to truly semantics-aware solutions. Ontologies offer a versatile foundation well suited for depicting the complex connections between such behavioral data and the diverse technical and organizational properties of an IT system. In order to facilitate the development of novel behavior-based detection systems, we present TAON, an OWL-based ontology offering a holistic view on actors, assets, and threat details, which are mapped to individual abstracted events and anomalies that can be detected by today's monitoring data providers. TOAN offers a straightforward means to plan an organization's defense against APTs and helps to understand how, why, and by whom certain resources are targeted. Populated by concrete data, the proposed ontology becomes a smart correlation framework able to combine several data sources into a semantic assessment of any targeted attack.

Requirements analysts can model regulated data practices to identify and reason about risks of noncompliance. If terminology is inconsistent or ambiguous, however, these models and their conclusions will be unreliable. To study this problem, we investigated an approach to automatically construct an information type ontology by identifying information type hyponymy in privacy policies using Tregex patterns. Tregex is a utility to match regular expressions against constituency parse trees, which are hierarchical expressions of natural language clauses, including noun and verb phrases. We discovered the Tregex patterns by applying content analysis to 15 privacy policies from three domains (shopping, telecommunication and social networks) to identify all instances of information type hyponymy. From this dataset, three semantic and four syntactic categories of hyponymy emerged based on category completeness and word-order. Among these, we identified and empirically evaluated 26 Tregex patterns to automate the extraction of hyponyms from privacy policies. The patterns identify information type hypernym-hyponym pairs with an average precision of 0.83 and recall of 0.52 across our dataset of 15 policies. 

Internet of Things (IoT) is to connect objects of different application fields, functionality and technology. These objects are entirely addressable and use standard communication protocol. Intelligent agents are used to integrate Internet of Things with heterogeneous low-power embedded resource-constrained networked devices. This paper discusses with the implemented real world scenario of smart autonomous patient management with the assistance of semantic technology in IoT. It uses the Smart Semantic framework using domain ontologies to encapsulate the processed information from sensor networks. This embedded Agent based Semantic Internet of Things in healthcare (ASIOTH) system is having semantic logic and semantic value based Information to make the system as smart and intelligent. This paper aims at explaining in detail the technology drivers behind the IoT and health care with the information on data modeling, data mapping of existing IoT data into different other associated system data, workflow or the process flow behind the technical operations of the remote device coordination, the architecture of network, middleware, databases, application services. The challenges and the associated solution in this field are discussed with the use case.

Dealing with increasing amounts of data creates the need to deal with redundant, inconsistent and/or complementary repositories which may be different in their data models and/or in their schema. Current data cleaning techniques developed to tackle data quality problems are just suitable for scenarios were all repositories share the same model and schema. Recently, an ontology-based methodology was proposed to overcome this limitation. In this paper, this methodology is briefly described and applied to a real scenario in the health domain with data quality problems.

Recently personal information due to the APT attack, the economic damage and leakage of confidential information is a serious social problem, a great deal of research has been done to solve this problem. APT attacks are threatening traditional hacking techniques as well as to increase the success rate of attacks using sophisticated attack techniques such attacks Zero-Day vulnerability in order to avoid detection techniques and state-of-the-art security because it uses a combination of intelligence. In this paper, the malicious code is designed to detect APT attack based on APT attack behavior ontology that occur during the operation on the target system, it uses intelligent APT attack than to define inference rules can be inferred about malicious attack behavior to propose a method that can be detected.

Effective Personalized Mobile Search Using KNN, implements an architecture to improve user's personalization effectiveness over large set of data maintaining security of the data. User preferences are gathered through clickthrough data. Clickthrough data obtained is sent to the server in encrypted form. Clickthrough data obtained is classified into content concepts and location concepts. To improve classification and minimize processing time, KNN(K Nearest Neighborhood) algorithm is used. Preferences identified(location and content) are merged to provide effective preferences to the user. System make use of four entropies to balance weight between content concepts and location concepts. System implements client server architecture. Role of client is to collect user queries and to maintain them in files for future reference. User preference privacy is ensured through privacy parameters and also through encryption techniques. Server is responsible to carry out the tasks like training, reranking of the search results obtained and the concept extraction. Experiments are carried out on Android based mobile. Results obtained through experiments show that system significantly gives improved results over previous algorithm for the large set of data maintaining security.

The objective of this paper is to explore the current notions of systems and “System of Systems” and establish the case for quantitative characterization of their structural, behavioural and contextual facets that will pave the way for further formal development (mathematical formulation). This is partly driven by stakeholder needs and perspectives and also in response to the necessity to attribute and communicate the properties of a system more succinctly, meaningfully and efficiently. The systematic quantitative characterization framework proposed will endeavor to extend the notion of emergence that allows the definition of appropriate metrics in the context of a number of systems ontologies. The general characteristic and information content of the ontologies relevant to system and system of system will be specified but not developed at this stage. The current supra-system, system and sub-system hierarchy is also explored for the formalisation of a standard notation in order to depict a relative scale and order and avoid the seemingly arbitrary attributions.
 

Many surveillance cameras are using everywhere, the videos or images captured by these cameras are still dumped but they are not processed. Many methods are proposed for tracking and detecting the objects in the videos but we need the meaningful content called semantic content from these videos. Detecting Human activity recognition is quite complex. The proposed method called Semantic Content Extraction (SCE) from videos is used to identify the objects and the events present in the video. This model provides useful methodology for intruder detecting systems which provides the behavior and the activities performed by the intruder. Construction of ontology enhances the spatial and temporal relations between the objects or features extracted. Thus proposed system provides a best way for detecting the intruders, thieves and malpractices happening around us.

This paper presents an ontological approach to perceive the current security status of the network. Computer network is a dynamic entity whose state changes with the introduction of new services, installation of new network operating system, and addition of new hardware components, creation of new user roles and by attacks from various actors instigated by aggressors. Various security mechanisms employed in the network does not give the complete picture of security of complete network. In this paper we have proposed taxonomy and ontology which may be used to infer impact of various events happening in the network on security status of the network. Vulnerability, Network and Attack are the main taxonomy classes in the ontology. Vulnerability class describes various types of vulnerabilities in the network which may in hardware components like storage devices, computing devices or networks devices. Attack class has many subclasses like Actor class which is entity executing the attack, Goal class describes goal of the attack, Attack mechanism class defines attack methodology, Scope class describes size and utility of the target, Automation level describes the automation level of the attack Evaluation of security status of the network is required for network security situational awareness. Network class has network operating system, users, roles, hardware components and services as its subclasses. Based on this taxonomy ontology has been developed to perceive network security status. Finally a framework, which uses this ontology as knowledgebase has been proposed.
 

The lack of standardization of the terms insider and insider threat has been a noted problem for researchers in the insider threat field. This paper describes the investigation of 42 different definitions of the terms insider and insider threat, with the goal of better understanding the current conceptual model of insider threat and facilitating communication in the research community.

Cyber-has often been considered as a coordination and control, as opposed to collaborative influence, media. This conceptual-design paper, uniquely, builds upon a number of entangled, cross disciplinary research strands – integrating engineering and conflict studies – and a detailed literature review to propose a new paradigm of assurance and deterrence models. We consider an ontology for Cyber-sûréte, which combines both the social trusts necessary for [knowledge &, information] assurance such as collaboration by social influence (CSI) and the technological controls and rules for secure information management referred as coordination by rule and control (CRC). We posit Cyber-sûréte as enabling both a 'safe-to-fail' ecology (in which learning, testing and adaptation can take place) within a fail-safe supervisory control and data acquisition (SCADA type) system, e.g. in a nuclear power plant. Building upon traditional state-based threat analysis, we consider Warning Time and the Threat equation with relation to policies for managing Cyber-Deterrence. We examine how the goods of Cyber-might be galvanised so as to encourage virtuous behaviour and deter and / or dissuade ne'er-do-wells through multiple transparencies. We consider how the Deterrence-escalator may be managed by identifying both weak influence and strong control signals so as to create a more benign and responsive cyber-ecology, in which strengths can be exploited and weaknesses identified. Finally, we consider declaratory / mutual transparencies as opposed to legalistic / controlled transparency.