Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

An Empirical Analysis of Vulnerabilities in Python Packages for Web Applications

This paper examines software vulnerabilities in common Python packages used particularly for web development. The empirical dataset is based on the PyPI package repository and the so-called Safety DB used to track vulnerabilities in selected packages within the repository. The methodological approach builds on a release-based time series analysis of the conditional probabilities for the releases of the packages to be vulnerable. According to the results, many of the Python vulnerabilities observed seem to be only modestly severe; input validation and cross-site scripting have been the most typical vulnerabilities. In terms of the time series analysis based on the release histories, only the recent past is observed to be relevant for statistical predictions; the classical Markov property holds.Comment: Forthcoming in: Proceedings of the 9th International Workshop on Empirical Software Engineering in Practice (IWESEP 2018), Nara, IEE

DroughtScape- Summer 2010

Upcoming Workshops Mild Drought Season Likely to Persist Drought Impacts Intensify in Upper Midwest Visiting Scientists North Carolina Takes Drought Monitor Seriously International Work- Murcia, Spain, Jun

Active security vulnerability notification and resolution

The early version of the Internet was designed for connectivity only, without the consideration of security, and the Internet is consequently an open structure. Networked systems are vulnerable for a number of reasons; design error, implementation, and management. A vulnerability is a hole or weak point that can be exploited to compromise the security of the system. Operating systems and applications are often vulnerable because of design errors. Software vendors release patches for discovered vulnerabilities, and rely upon system administrators to accept and install patches on their systems. Many system administrators fail to install patches on time, and consequently leave their systems vulnerable to exploitation by hackers. This exploitation can result in various security breaches, including website defacement, denial of service, or malware attacks. The overall problem is significant with an average of 115 vulnerabilities per week being documented during 2005. This thesis considers the problem of vulnerabilities in IT networked systems, and maps the vulnerability types into a technical taxonomy. The thesis presents a thorough analysis of the existing methods of vulnerability management which determine that these methods have failed to mange the problem in a comprehensive way, and show the need for a comprehensive management system, capable of addressing the awareness and patch deploymentp roblems. A critical examination of vulnerability databasess tatistics over the past few years is provided, together with a benchmarking of the problem in a reference environment with a discussion of why a new approach is needed. The research examined and compared different vulnerability advisories, and proposed a generic vulnerability format towards automating the notification process. The thesis identifies the standard process of addressing vulnerabilities and the over reliance upon the manual method. An automated management system must take into account new vulnerabilities and patch deploymentt o provide a comprehensives olution. The overall aim of the research has therefore been to design a new framework to address these flaws in the networked systems harmonised with the standard system administrator process. The approach, known as AVMS (Automated Vulnerability Management System), is capable of filtering and prioritising the relevant messages, and then downloading the associated patches and deploying them to the required machines. The framework is validated through a proof-of-concept prototype system. A series of tests involving different advisories are used to illustrate how AVMS would behave. This helped to prove that the automated vulnerability management system prototype is indeed viable, and that the research has provided a suitable contribution to knowledge in this important domain.The Saudi Government and the Network Research Group at the University of Plymouth

Assessing the effectiveness of defensive cyber operations

Enormous amounts of resources are being allocated for defensive cyber programs. The White House’s Cyber Security National Action Plan proposes a 35% increase in federal spending on cyber security during Fiscal Year 2017. Without an appropriate understanding of how well the people, processes, defenses, and risk are measured, there will naturally be unproductive tasking, inefficient spending and ineffective reporting. In 2016, the White House established the Commission on enhancing National Cybersecurity to assess the state of our nation’s cybersecurity posture. The report recognized both the difficulty and the need to develop meaningful metrics for cybersecurity in order to better secure the cyber landscape as it pertained to the broader digital ecosystem and its connection to our economy, government, and defense. The commission focused on both the private sector as well as the government and suggested the need to perfect policies, practices and technologies. Additionally, the Marine Corps University recently released research topics addressing some of the most important concerns affecting warfighters. One of the concerns was the lack of a methodology for determining the performance of Defensive Cyber Operations (DCO). Specifically addressed was a need to better understand how actions taken by network defenders facilitate network protection. Previous analysis of this topic led to a reactive and un-actionable approach which was tied to negative events such as the quantity and category of incident reports. As there is currently no framework or scorecard built to evaluate DCO as a whole effort, a methodical approach was taken to scope the problem, compare existing frameworks, develop a framework, and present a scorecard. The first phase of research required scoping exactly what is involved in DCO at the most basic level and understanding how the DoD evaluates performance. This resulted in an understanding of the actionability of metrics, the levels of warfare, and the counterbalance of cyber asymmetry. Also identified was the military doctrine for assessments, which frames evaluations in terms of Measures of Effectiveness and Measures of Performance and supports continuous assessments that provide actionable information to decision makers. The second phase required a detailed analysis of existing frameworks that measured related functions of cybersecurity. Specifically utilized were industry accepted compliance, incident handling, governance, and risk management frameworks. The outcome identified four functional areas common to most frameworks; people, processes, defenses, and risk. The third phase involved developing a framework that evaluated the four functional areas of DCO identified in the problem-framing phase, utilizing the most appropriate features of the already established frameworks. A key facet of this evaluation was that assessments should be weighed over time to demonstrate progress but also be measured against standards, peers, and the adversary. The final phase identified the continuous reporting criteria and the tangible mechanism for evaluating an organization in terms of a scorecard. The framework is not a static list of measurements but rather supports tailoring metrics to the organization’s specific requirements. The fundamentals of the framework are organized into elements, levels, categories, ends/ways, and measures. These metrics should be documented utilizing a standardized rubric that assesses the capability and performance of the metrics. The results should be reviewed and analyzed to determine trends, areas for improvement or investment and actionable information to support decision making. Additionally, a modified Delphi analysis with expert consensus validated the major concepts put forward in this paper. Overall, this research provides a comprehensive framework to evaluate the performance of Defensive Cyber Operations in terms of people, processes, defenses, and risk, filling a knowledge gap that is increasingly vital

A Framework of DevSecOps for Software Development Teams

This master's thesis explores a broad evaluation of automated security testing in the context of DevOps practices. The primary objective of this study is to propose a framework that facilitates the seamless integration of security scanning tools within DevOps practices. The thesis will focus on examining the existing set of tools and their effective integration into fully automated DevOps CI/CD pipelines. The thesis starts by examining the theoretical concepts of DevOps and provides guidelines for integrating security within DevOps methodologies. Furthermore, it assesses the current state of security by analysing the OWASP Web API top 10 security vulnerability list and evaluating existing security automation tools. Additionally, the research investigates the performance and efficacy of these tools across various stages of the SDLC and investigates ongoing research and development activities. A fully automated DevOps CI/CD pipeline is implemented to integrate security scanning tools, enforcing complete security checks throughout the SDLC. Azure DevOps build and release pipelines, along with Snyk, were used to create a comprehensive automated security scanning framework. The study considerably investigates the integration of these security scanning tools and assesses their influence on the overall security posture of the developed applications. The finding of the study reveals that security scanning tools can be efficiently integrated into fully automated DevOps practices. Based on the results, recommendations are provided for the selection of suitable tools and techniques to achieve a DevSecOps practice. In conclusion, this thesis provides valuable insights into security integration in DevOps practices, highlighting the effectiveness of security automation tools. The research also recommends areas for further improvements to meet the industry's evolving requirements

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity