Analyzing Small Businesses\u27 Adoption of Big Data Security Analytics

Despite the increased cost of data breaches due to advanced, persistent threats from malicious sources, the adoption of big data security analytics among U.S. small businesses has been slow. Anchored in a diffusion of innovation theory, the purpose of this correlational study was to examine ways to increase the adoption of big data security analytics among small businesses in the United States by examining the relationship between small business leaders\u27 perceptions of big data security analytics and their adoption. The research questions were developed to determine how to increase the adoption of big data security analytics, which can be measured as a function of the user\u27s perceived attributes of innovation represented by the independent variables: relative advantage, compatibility, complexity, observability, and trialability. The study included a cross-sectional survey distributed online to a convenience sample of 165 small businesses. Pearson correlations and multiple linear regression were used to statistically understand relationships between variables. There were no significant positive correlations between relative advantage, compatibility, and the dependent variable adoption; however, there were significant negative correlations between complexity, trialability, and the adoption. There was also a significant positive correlation between observability and the adoption. The implications for positive social change include an increase in knowledge, skill sets, and jobs for employees and increased confidentiality, integrity, and availability of systems and data for small businesses. Social benefits include improved decision making for small businesses and increased secure transactions between systems by detecting and eliminating advanced, persistent threats

Integrated survey for the reconstruction of the Papal Basilica and the Sacred Convent of St. Francis in Assisi, Italy

The Papal Basilica and the Sacred Convent of Saint Francis in Assisi in Italy are characterized by unique and composite particularities that need an exhaustive knowledge of the sites themselves to guarantee visitor's security and safety, considering all the people and personnel normally present in the site, visitors with disabilities and finally the needs for cultural heritage preservation and protection. This aim can be reached using integrated systems and innovative technologies, such as Internet of Everything (IoE), which can connect people, things (smart sensors, devices and actuators; mobile terminals; wearable devices; etc.), data/information/knowledge and processes to reach the wanted objectives. The IoE system must implement and support an Integrated Multidisciplinary Model for Security and Safety Management (IMMSSM) for the specific context, using a multidisciplinary approach. The purpose of the paper is to illustrate the integrated survey for the reconstruction of the considered site that was necessary to obtain all the necessary information to start to set up the considered IMMSSM and the related IoE based technological system

Unknown Threat Detection With Honeypot Ensemble Analsyis Using Big Datasecurity Architecture

The amount of data that is being generated continues to rapidly grow in size and complexity. Frameworks such as Apache Hadoop and Apache Spark are evolving at a rapid rate as organizations are building data driven applications to gain competitive advantages. Data analytics frameworks decomposes our problems to build applications that are more than just inference and can help make predictions as well as prescriptions to problems in real time instead of batch processes. Information Security is becoming more important to organizations as the Internet and cloud technologies become more integrated with their internal processes. The number of attacks and attack vectors has been increasing steadily over the years. Border defense measures (e.g. Intrusion Detection Systems) are no longer enough to identify and stop attackers. Data driven information security is not a new approach to solving information security; however there is an increased emphasis on combining heterogeneous sources to gain a broader view of the problem instead of isolated systems. Stitching together multiple alerts into a cohesive system can increase the number of True Positives. With the increased concern of unknown insider threats and zero-day attacks, identifying unknown attack vectors becomes more difficult. Previous research has shown that with as little as 10 commands it is possible to identify a masquerade attack against a user\u27s profile. This thesis is going to look at a data driven information security architecture that relies on both behavioral analysis of SSH profiles and bad actor data collected from an SSH honeypot to identify bad actor attack vectors. Honeypots should collect only data from bad actors; therefore have a high True Positive rate. Using Apache Spark and Apache Hadoop we can create a real time data driven architecture that can collect and analyze new bad actor behaviors from honeypot data and monitor legitimate user accounts to create predictive and prescriptive models. Previously unidentified attack vectors can be cataloged for review

Security & Privacy Issues of Big Data in IOT based Healthcare System using Cloud Computing

The increasing popularity of IOT based technology in healthcare arena using cloud computing immensely emphasizes on using big data as a service for facilitating a proper structure for collected healthcare data and accommodating such ample number of diverse data for better diagnosis, medication and prediction of human health. The recent revolution brought in healthcare by internet of things allows us to experience the gigantic figure of data with more complexity, diversity and timeliness. Hence, the question rises in the researcher’s den about the security and privacy of such enormous data. Therefore, nowadays the limelight has been shifted questioning on how much secure and private those data which are generated from IOT devices and being stored in cloud environment? In this paper we have drafted a survey on most probable security as well as privacy problems related to healthcare which needs to grab the attention for enabling the healthcare system more reliable, more effective in terms of advancement of medical science and curing more patients at a time predicting the possible diseases

Feature Extraction and Feature Selection: Reducing Data Complexity with Apache Spark

Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cyber security threats and attacks while utilizing machine learning. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark

Revisión sistemática para la construcción de una arquitectura con tecnologías emergentes IoT, técnicas de inteligencia artificial, monitoreo y almacenamiento de tráfico malicioso

This article presents a systematic review to determine the guidelines that allow the construction of an architecture based on emerging IoT technologies, artificial intelligence techniques, monitoring and storage of malicious traffic, in order to safeguard information, given that there are security flaws in IoT devices, which are intercepted by malicious systems that perform unwanted actions without the consent of the user, causing damage and theft of data, that is why three phases were established to carry out: in the first phase an exhaustive search of information was carried out in specialized databases, where they are selected and classified for the development of the guidelines, in the second phase the information collected was identified and analyzed to define an appropriate algorithm for the study, emerging technologies and key components of the cybersecurity system and finally in the third phase defined the necessary and pertinent guidelines for the struction of an architecture based on emerging technologies

Modern SIEM Analysis and Critical Requirements Definition in the Context of Information Warfare

Today Security Information and Event Management (SIEM) systems are used to prevent information loss in computer systems and networks. There are many approaches to SIEM realization. This paper is devoted to the analysis of existing SIEM and their characteristics in accordance with international standards and specifications, as well as a comparative description of their capabilities and differences, advantages and disadvantages. These results will be used in research project realization devoted to open source SIEM development and implementation in critical infrastructure to improve the cybersecurity level in the context of information warfare and cyber threats realization