Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

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Deep Learning for Generic Object Detection: A Survey

System Identification I

Cooperative Cyber-Physical Systems (Co-CPSs) can be enabled using wireless communication technologies, which in principle should address reliability and safety challenges. Safety for Co-CPS enabled by wireless communication technologies is a crucial aspect and requires new dedicated design approaches. In this paper, we provide an overview of five Co-CPS use cases, as introduced in our SafeCOP EU project, and analyze their safety design requirements. Next, we provide a comprehensive analysis of the main existing wireless communication technologies giving details about the protocols developed within particular standardization bodies. We also investigate to what extent they address the non-functional requirements in terms of safety, security and real time, in the different application domains of each use case. Finally, we discuss general recommendations about the use of different wireless communication technologies showing their potentials in the selected real-world use cases. The discussion is provided under consideration in the 5G standardization process within 3GPP, whose current efforts are inline to current gaps in wireless communications protocols for Co-CPSs including many future use cases.

https://www.cister.isep.ipp.pt/docs/wireless_communication_technologies_for_safe_cooperative_cyber_physical_systems/1464/view.pdf

Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems

Monitoring the “physics” of cyber-physical systems to detect attacks is a growing area of research. In its basic form, a security monitor creates time-series models of sensor readings for an industrial control system and identifies anomalies in these measurements to identify potentially false control commands or false sensor readings. In this article, we review previous work on physics-based anomaly detection based on a unified taxonomy that allows us to identify limitations and unexplored challenges and to propose new solutions.

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A Survey of Physics-Based Attack Detection in Cyber-Physical Systems

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.

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Machine learning and deep learning

While attacks on information systems have for most practical purposes binary outcomes (information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we study if attack-detection can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.

https://dl.acm.org/doi/pdf/10.1145/2976749.2978388

Limiting the Impact of Stealthy Attacks on Industrial Control Systems

Monitoring the “physics” of control systems to detect attacks is a growing area of research. In its basic form a security monitor creates time-series models of sensor readings for an industrial con ...

Survey and New Directions for Physics-Based Attack Detection in Control Systems

Attacking Fieldbus Communications in ICS: Applications to the SWaT Testbed.

Examining and modifying data of interest in the memory of a target program is an important capability for security applications such as memory forensics, rootkit detection, game hacking, and virtual machine introspection. In this paper we present a novel memory graph based approach for program data introspection and modification, which does not require source code, debugging symbols, or any API in the target program. It takes as input a sequence of memory snapshots taken while the program executes, and produces a path signature, which can be used in different executions of the program to efficiently locate and traverse the in-memory data structures where the data of interest is stored. We have implemented our approach in a tool called SigPath. We have applied SigPath to game hacking, building cheats for 10 popular real-time and turn-based games, and for memory forensics, recovering from snapshots the contacts a user has stored in four IM applications including Skype and Yahoo Messenger.

/pdf/sigpath-a-memory-graph-based-approach-for-program-data-r9wa3hrtmv.pdf

David I. Urbina

Papers

A Survey of Physics-Based Attack Detection in Cyber-Physical Systems

Limiting the Impact of Stealthy Attacks on Industrial Control Systems

Survey and New Directions for Physics-Based Attack Detection in Control Systems

Attacking Fieldbus Communications in ICS: Applications to the SWaT Testbed.

SigPath: A Memory Graph Based Approach for Program Data Introspection and Modification