Biblio
Lately mining of information from online life is pulling in more consideration because of the blast in the development of Big Data. In security, Big Data manages an assortment of immense advanced data for investigating, envisioning and to draw the bits of knowledge for the expectation and anticipation of digital assaults. Big Data Analytics (BDA) is the term composed by experts to portray the art of dealing with, taking care of and gathering a great deal of data for future evaluation. Data is being made at an upsetting rate. The quick improvement of the Internet, Internet of Things (IoT) and other creative advances are the rule liable gatherings behind this proceeded with advancement. The data made is an impression of the earth, it is conveyed out of, along these lines can use the data got away from structures to understand the internal exercises of that system. This has become a significant element in cyber security where the objective is to secure resources. Moreover, the developing estimation of information has made large information a high worth objective. Right now, investigate ongoing exploration works in cyber security comparable to huge information and feature how Big information is secured and how huge information can likewise be utilized as a device for cyber security. Simultaneously, a Big Data based concentrated log investigation framework is actualized to distinguish the system traffic happened with assailants through DDOS, SQL Injection and Bruce Force assault. The log record is naturally transmitted to the brought together cloud server and big information is started in the investigation process.
Smart technologies at hand have facilitated generation and collection of huge volumes of data, on daily basis. It involves highly sensitive and diverse data like personal, organisational, environment, energy, transport and economic data. Data Analytics provide solution for various issues being faced by smart cities like crisis response, disaster resilience, emergence management, smart traffic management system etc.; it requires distribution of sensitive data among various entities within or outside the smart city,. Sharing of sensitive data creates a need for efficient usage of smart city data to provide smart applications and utility to the end users in a trustworthy and safe mode. This shared sensitive data if get leaked as a consequence can cause damage and severe risk to the city's resources. Fortification of critical data from unofficial disclosure is biggest issue for success of any project. Data Leakage Detection provides a set of tools and technology that can efficiently resolves the concerns related to smart city critical data. The paper, showcase an approach to detect the leakage which is caused intentionally or unintentionally. The model represents allotment of data objects between diverse agents using Bigraph. The objective is to make critical data secure by revealing the guilty agent who caused the data leakage.
Data analytics and telemetry have become paramount to monitoring and maintaining quality-of-service in addition to business analytics. Stream processing-a model where a network of operators receives and processes continuously arriving discrete elements-is well-suited for these needs. Current and previous studies and frameworks have focused on continuity of operations and aggregate performance metrics. However, real-time performance and tail latency are also important. Timing errors caused by either performance or failed communication faults also affect real-time performance more drastically than aggregate metrics. In this paper, we introduce redundancy in the stream data to improve the real-time performance and resiliency to timing errors caused by either performance or failed communication faults. We also address limitations in previous solutions using a fine-grained acknowledgment tracking scheme to both increase the effectiveness for resiliency to performance faults and enable effectiveness for failed communication faults. Our results show that fine-grained acknowledgment schemes can improve the tail and mean latencies by approximately 30%. We also show that these schemes can improve resiliency to performance faults compared to existing work. Our improvements result in 47.4% to 92.9% fewer missed deadlines compared to 17.3% to 50.6% for comparable topologies and redundancy levels in the state of the art. Finally, we show that redundancies of 25% to 100% can reduce the number of data elements that miss their deadline constraints by 0.76% to 14.04% for applications with high fan-out and by 7.45% up to 50% for applications with no fan-out.
This paper presents PSO, an ontological framework and a methodology for improving physical security and insider threat detection. PSO can facilitate forensic data analysis and proactively mitigate insider threats by leveraging rule-based anomaly detection. In all too many cases, rule-based anomaly detection can detect employee deviations from organizational security policies. In addition, PSO can be considered a security provenance solution because of its ability to fully reconstruct attack patterns. Provenance graphs can be further analyzed to identify deceptive actions and overcome analytical mistakes that can result in bad decision-making, such as false attribution. Moreover, the information can be used to enrich the available intelligence (about intrusion attempts) that can form use cases to detect and remediate limitations in the system, such as loosely-coupled provenance graphs that in many cases indicate weaknesses in the physical security architecture. Ultimately, validation of the framework through use cases demonstrates and proves that PS0 can improve an organization's security posture in terms of physical security and insider threat detection.
Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources.
The mitigation of insider threats against databases is a challenging problem as insiders often have legitimate access privileges to sensitive data. Therefore, conventional security mechanisms, such as authentication and access control, may be insufficient for the protection of databases against insider threats and need to be complemented with techniques that support real-time detection of access anomalies. The existing real-time anomaly detection techniques consider anomalies in references to the database entities and the amounts of accessed data. However, they are unable to track the access frequencies. According to recent security reports, an increase in the access frequency by an insider is an indicator of a potential data misuse and may be the result of malicious intents for stealing or corrupting the data. In this paper, we propose techniques for tracking users' access frequencies and detecting anomalous related activities in real-time. We present detailed algorithms for constructing accurate profiles that describe the access patterns of the database users and for matching subsequent accesses by these users to the profiles. Our methods report and log mismatches as anomalies that may need further investigation. We evaluated our techniques on the OLTP-Benchmark. The results of the evaluation indicate that our techniques are very effective in the detection of anomalies.