The rise in popularity of Internet of Things (IoT) devices has opened doors for privacy and security breaches in Cyber-Physical systems like smart homes, smart vehicles, and smart grids that affect our daily existence. IoT systems are also a source of big data that gets shared via the cloud. IoT systems in a smart home environment have sensitive access control issues since they are deployed in a personal space. The collected data can also be of a highly personal nature. Therefore, it is critical to building access control models that govern who, under what circumstances, can access which sensed data or actuate a physical system. Traditional access control mechanisms are not expressive enough to handle such complex access control needs, warranting the incorporation of new methodologies for privacy and security. In this paper, we propose the creation of the PALS system, that builds upon existing work in an attribute-based access control model, captures physical context collected from sensed data (attributes) and performs dynamic reasoning over these attributes and context-driven policies using Semantic Web technologies to execute access control decisions. Reasoning over user context, details of the information collected by the cloud service provider, and device type our mechanism generates as a consequent access control decisions. Our system’s access control decisions are supplemented by another sub-system that detects intrusions into smart home systems based on both network and behavioral data. The combined approach serves to determine indicators that a smart home system is under attack, as well as limit what data breach such attacks can achieve.
Automating GDPR Compliance using Policy Integrated Blockchain
In my knowledge graph class yesterday we talked about the SPARQL query language and I illustrated it with DBpedia queries, including an example getting data about the movie Double Indemnity. I had brought a google assistant device and used it to compare its answers to those from DBpedia. When I asked the Google assistant “Who starred in the film Double Indemnity”, the first person it mentioned was Raymond Chandler. I knew this was wrong, since he was one of its screenwriters, not an actor, and shared an Academy Award for the screenplay. DBpedia’s data was correct and did not list Chandler as one of the actors.
I did not feel too bad about this — we shouldn’t expect perfect accuracy in these huge, general purpose knowledge graphs and at least Chandler played an important role in making the film.
After class I looked at the Wikidata page for Double Indemnity (Q478209) and saw that it did list Chandler as an actor. I take this as evidence that Google’s knowledge Graph got this incorrect fact from Wikidata, or perhaps from a precursor, Freebase.
The good news 🙂 is that Wikidata had flagged the fact that Chandler (Q180377) was a cast member in Double Indemnity with a “potential Issue“. Clicking on this revealed that the issue was that Chandler was not known to have an occupation property that a “cast member” property (P161) expects, which includes twelve types, such as actor, opera singer, comedian, and ballet dancer. Wikidata lists chandler’s occupations as screenwriter, novelist, write and poet.
More good news 😀 is that the Wikidata fact had provenance information in the form of a reference stating that it came from CSFD (Q3561957), a “Czech and Slovak web project providing a movie database”. Following the link Wikidata provided led me eventually to the resource, which allowed my to search for and find its Double Indemnity entry. Indeed, it lists Raymond Chandler as one of the movie’s Hrají. All that was left to do was to ask for a translation, which confirmed that Hrají means “starring”.
Case closed? Well, not quite. What remains is fixing the problem.
The final good news 🙂 is that it’s easy to edit or delete an incorrect fact in Wikidata. I plan to delete the incorrect fact in class next Monday. I’ll look into possible options to add an annotation in some way to ignore the incorrect ?SFD source for Chander being a cast member over the weekend.
Some possible bad news 🙁 that public knowledge graphs like Wikidata might be exploited by unscrupulous groups or individuals in the future to promote false or biased information. Wikipedia is reasonably resilient to this, but the problem may be harder to manage for public knowledge graphs, which get much their data from other sources that could be manipulated.
Early Detection of Cybersecurity Threats Using Collaborative Cognition
The early detection of cybersecurity events such as attacks is challenging given the constantly evolving threat landscape. Even with advanced monitoring, sophisticated attackers can spend more than 100 days in a system before being detected. This paper describes a novel, collaborative framework that assists a security analyst by exploiting the power of semantically rich knowledge representation and reasoning integrated with different machine learning techniques. Our Cognitive Cybersecurity System ingests information from various textual sources and stores them in a common knowledge graph using terms from an extended version of the Unified Cybersecurity Ontology. The system then reasons over the knowledge graph that combines a variety of collaborative agents representing host and network-based sensors to derive improved actionable intelligence for security administrators, decreasing their cognitive load and increasing their confidence in the result. We describe a proof of concept framework for our approach and demonstrate its capabilities by testing it against a custom-built ransomware similar to WannaCry.
Design and Implementation of an Attribute Based Access Controller using OpenStack Services
Sharad Dixit, Graduate Student, UMBC
10:30am Monday, 24 September 2018, ITE346
With the advent of cloud computing, industries began a paradigm shift from the traditional way of computing towards cloud computing as it fulfilled organizations present requirements such as on-demand resource allocation, lower capital expenditure, scalability and flexibility but with that it brought a variety of security and user data breach issues. To solve the issues of user data and security breach, organizations have started to implement hybrid cloud where underlying cloud infrastructure is set by the organization and is accessible from anywhere around the world because of the distinguishable security edges provided by it. However, most of the cloud platforms provide a Role Based Access Controller which does not adequate for complex organizational structures. A novel mechanism is proposed using OpenStack services and semantic web technologies to develop a module which evaluates user’s and project’s multi-varied attributes and run them against access policy rules defined by an organization before granting the access to the user. Henceforth, an organization can deploy our module to obtain a robust and trustworthy access control based on multiple attributes of a user and the project the user has requested in a hybrid cloud platform like OpenStack.
Attribute Based Encryption for Secure Access to Cloud Based EHR Systems
Medical organizations find it challenging to adopt cloud-based electronic medical records services, due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient centric approach for EHR management where the responsibility of authorizing data access is handled at the patients’ end. This however creates a significant overhead for the patient who has to authorize every access of their health record. This is not practical given the multiple personnel involved in providing care and that at times the patient may not be in a state to provide this authorization. Hence there is a need of developing a proper authorization delegation mechanism for safe, secure and easy cloud-based EHR management. We have developed a novel, centralized, attribute based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHR’s access authority to the medical providers. In this paper, we describe this novel ABE approach as well as the prototype system that we have created to illustrate it.
Automated Knowledge Extraction from the Federal Acquisition Regulations System (FARS)
With increasing regulation of Big Data, it is becoming essential for organizations to ensure compliance with various data protection standards. The Federal Acquisition Regulations System (FARS) within the Code of Federal Regulations (CFR) includes facts and rules for individuals and organizations seeking to do business with the US Federal government. Parsing and gathering knowledge from such lengthy regulation documents is currently done manually and is time and human intensive.Hence, developing a cognitive assistant for automated analysis of such legal documents has become a necessity. We have developed semantically rich approach to automate the analysis of legal documents and have implemented a system to capture various facts and rules contributing towards building an ef?cient legal knowledge base that contains details of the relationships between various legal elements, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules. In this paper, we describe our framework along with the results of automating knowledge extraction from the FARS document (Title48, CFR). Our approach can be used by Big Data Users to automate knowledge extraction from Large Legal documents.
W3C Recommendation: Time Ontology in OWL
The Spatial Data on the Web Working Group has published a W3C Recommendation of the Time Ontology in OWL specification. The ontology provides a vocabulary for expressing facts about relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional Gregorian calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars.
Link before you Share: Managing Privacy Policies through Blockchain
11:00am Monday, 16 October 2017
In this week’s meeting, Srishty Saha, Michael Aebig and Jiayong Lin will talk about their work on extracting knowledge from the US FAR System.
Automated Knowledge Extraction from the Federal Acquisition Regulations System
Srishty Saha, Michael Aebig and Jiayong Lin
11am-12pm Monday, 25 September 2017, ITE346, UMBC
The Federal Acquisition Regulations System (FARS) within the Code of Federal Regulations (CFR) includes facts and rules for individuals and organizations seeking to do business with the US Federal government. Parsing and extracting knowledge from such lengthy regulation documents is currently done manually and is time and human intensive. Hence, developing a cognitive assistant for automated analysis of such legal documents has become a necessity. We are developing a semantically rich legal knowledge base representing legal entities and their relationships, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules.
2018 Ontology Summit: Ontologies in Context
The OntologySummit is an annual series of online and in-person events that involves the ontology community and communities related to each year’s topic. The topic chosen for the 2018 Ontology Summit will be Ontologies in Context, which the summit describes as follows.
“In general, a context is defined to be the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed. Some examples of synonyms include circumstances, conditions, factors, state of affairs, situation, background, scene, setting, and frame of reference. There are many meanings of “context” in general, and also for ontologies in particular. The summit this year will survey these meanings and identify the research problems that must be solved so that contexts can succeed in achieving the full understanding and assessment of an ontology.”
Each year’s Summit comprises of a series of both online and face-to-face events that span about three months. These include a vigorous three-month online discourse on the theme, and online panel discussions, research activities which will culminate in a two-day face-to-face workshop and symposium.
Over the next two months, there will be a sequence of weekly online meetings to discuss, plan and develop the 2018 topic. The summit itself will start in January with weekly online sessions of invited speakers. Visit the the 2018 Ontology Summit site for more information and to see how you can participate in the planning sessions.