With air transportation growing and current civil aeronautical communication systems reaching their capacity limit in high density areas, the need for new aeronautical communication technologies becomes apparent. The biggest challenge in recent years is the transition from analogue voice to digital data communication and the related trend towards an increased autonomous data processing. A promising candidate for the digital future communication infrastructure in continental areas is the terrestrial long-range L-band Digital Aeronautical Communications System (LDACS), which is currently in the process of being standardized by the International Civil Aviation Organization (ICAO). As safety and security are strongly intertwined in civil aviation, every installation of LDACS requires protection against cyber-attacks. This paper introduces a cybersecurity architecture for LDACS and proposes suitable security algorithm, which can achieve the security objectives on top of the architecture. Therefore we integrate new security functions within the existing protocol stack of LDACS. We provide an architecture for user data encryption, data integrity, authenticated key agreement, entity authentication, broadcast channel protection, and key and access management.

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A Cybersecurity Architecture for the L-band Digital Aeronautical Communications System (LDACS)

The article discusses the problem of risk management in the context of safety of an organization's information assets. Assuming system of information risk management as a basic element of organization management in the aspect of information safety of modern organizations, this document focuses on methods and techniques of qualitative risk estimates. Basic standards and good practice from areas of risk management and ensuring information safety in the organization were recalled.

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Evaluation of information safety as an element of improving the organization’s safety management

Framework for Calculating Return on Security Investment (ROSI) for Security-Oriented Organizations

With the continuously increasing number of data leakage security incidents caused by organization insiders, current security activities cannot predict a data leakage. Because such security incidents are extremely harmful and difficult to detect, predicting security incidents would be the most effective preventative method. However, current insider security controls and systems detect and identify unusual behaviors to prevent security incidents but produce many false-positives. To solve these problems, the present study collects and analyzes data leaks by insiders in advance, analyzes information leaks that can predict security incidents, and evaluates risk based on behavior. To this end, data leakage behaviors by insiders are analyzed through an analysis of previous studies and the implementation of an in-depth interview method. Statistical verification of the analyzed data leakage behavior is performed to determine the validity and derive the levels of leakage risk for each behavior. In addition, by applying the N-gram analysis method to derive a data leakage scenario, the levels of risk are clarified to reduce false-positives and over detection (i.e., the limitations of existing data leakage prevention systems) and make preemptive security activities possible.

Research on Behavior-Based Data Leakage Incidents for the Sustainable Growth of an Organization

The current work aims at probing the performance of real-time forecasting of endemic infectious diseases by means of machine learning and deep learning techniques. An LSTM-based time series forecasting framework and machine learning-based framework are proposed for forecasting the endemic infectious diseases in real time. With recent outbreaks of Ebola, Zika, cholera, and COVID 2019, a question is being raised on our alertness as well as preparedness toward controlling the spread of these pandemics. Accurate and reliable prediction occurrences of these diseases are compulsory for the health personals to enable timely response in handling these outbreaks. The diversities of the communities make it more complex along with the humongous data generated due to the convergence of SMAC technologies. The data generated due to this complex network is nonlinear and non-stationary. Processing of this data requires an effort from a multidimensional perspective. The current work proposed the utilization of machine learning and deep learning-based long short-term memory (LSTM) techniques for the assessment of time series forecasting of casualties in case of cholera outbreak that happened recently in Yemen. The feasibility of these two techniques is probed using performance evaluation metrics. The core objective of using these two techniques is in considering nonlinear and non-stationary behavior.

A probe into performance analysis of real-time forecasting of endemic infectious diseases using machine learning and deep learning algorithms

1. ENISA (European Network and Information Security Agency), “Risk Management /Risk Assessment “ (available on-line at http://www.enisa.europa.eu/rmra) 2. Walid Al-Ahmad and Bassil Mohammad. Addressing information security risks by adopting standards. International Journal of Information Security Science, 2(2):28_43, 2013. 3. Tom Carlson, HF Tipton, and M Krause. Understanding Information Security Management Systems. Auerbach Publications Boca Raton, FL, 2008.

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Sahar Al-Dhahri

Papers

Information Security Management System