As manmade systems become increasingly complex and evolve into Systems of Systems of various kinds, a multitude of tasks needs to be performed in coordination throughout their development. In this paper, we briefly introduce a new modeling framework that was designed following systems engineering principles and based on extensive research of development efforts characteristics. We focus on the framework's ability to support the design of development processes while addressing Systems of Systems‐related challenges. We then describe a case study of using this framework to introduce a new capability into a large enterprise, which specializes in systems and System of Systems development. The example demonstrates the utility of using this framework for planning complex development efforts as well as improving the coordination of multiple development efforts in System of Systems development scenarios.

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Model-based Systems Engineering (MBSE) approaches are a step forward in the evolution of computer-aided engineering, and yet, they often incorporate deficiencies that may jeopardize their practical utility and usability, as well as the validity of the resulting models. We demonstrate how a domain-specific modeling approach can relieve some hurdles in adopting MBSE, and how it can be used in tandem with a general-purpose modeling approach to augment and introduce rigor to models. Specifically, we demonstrate the consequences of theoretical issues that were previously identified in Object Process Methodology and suggest an approach to solve them. We use a generalized case-study—derived from extensive process modeling in both academia and industry—to show that a domain-specific model can significantly relax the user’s modeling effort. This demonstration is based on two quantitative metrics: the number of representational elements and available modeling tactics. We discuss the contribution of our approach to model quality, particularly with respect to its rigor and communicability.

Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by a radar system is sent to, and used by, a peripheral consuming system, such as a missile system or a graphical user interface used by an operator. Those systems process the data stream and make real-time operational decisions based on the data received. Given this, the reliability and availability of information provided by radar systems have grown in importance. Although the field of cyber security has been continuously evolving, no prior research has focused on anomaly detection in radar systems. In this paper, we present an unsupervised deep-learning-based method for detecting anomalies in radar system data streams; we take into consideration the fact that a data stream created by a radar system is heterogeneous, i.e., it contains both numerical and categorical features with non-linear and complex relationships. We propose a novel technique that learns the correlation between numerical features and an embedding representation of categorical features in an unsupervised manner. The proposed technique, which allows for the detection of the malicious manipulation of critical fields in a data stream, is complemented by a timing-interval anomaly-detection mechanism proposed for the detection of message-dropping attempts. Real radar system data were used to evaluate the proposed method. Our experiments demonstrated the method’s high detection accuracy on a variety of data-stream manipulation attacks (an average detection rate of 88% with a false -alarm rate of 1.59%) and message-dropping attacks (an average detection rate of 92% with a false-alarm rate of 2.2%).