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By Alexandra Mileva 1, *, † , Alexander Velinov 1, † , Vesna Dimitrova 2, † , Luca Caviglione 3, † and Stefan Wendzel 4, †

Received: 28 December 2021 / Revised: 18 January 2022 / Accepted: 23 January 2022 / Published: 25 January 2022

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The DICOM (Digital Imaging and Communication in Medicine) standard provides a framework for the clinically accurate representation, processing, transmission, storage and display of medical image data. Information hiding in DICOM is currently limited to the application of digital media steganography and watermarking techniques to the media portions of DICOM files, as well as text steganographic techniques to embed information in the metadata of DICOM files. To improve the overall security of the DICOM standard, we investigate its susceptibility to network steganographic techniques. For this, we are developing several network covert channels that can be built using a specific transport mechanism – DICOM messaging service and overlay service. The bandwidth, undetectability, and robustness of the proposed covert channels are evaluated, and possible countermeasures are suggested. Additionally, a detection mechanism using entropy-based statistics is introduced and its performance is evaluated.

Today’s hospitals implement integrated electronic medical records and big information systems (IS) through computer networks and software applications. There are different types of information systems. First, there is a Hospital Information System (HIS), which is a computerized management system for handling and supporting clinical operations, medical patient care activities, hospital business and administrative functions, etc. [1]. There is also a radiology information system (RIS) for the radiology department, which stores patient-related information (eg, medical, patient demographic and billing information), and examination-related information (eg, patient arrival documents, procedure descriptions, diagnostic reporting). does the management. , Various IS exchange information using the HL7 standard (https://www.hl7.org, accessed 5 December 2021). In this scenario, an important part is the PACS infrastructure responsible for the integration of image collection and communication systems or modalities. The latter, according to the standard, are medical imaging devices (such as CT scanners, MRI scanners, ultrasound, etc.); archives for storing medical images; workstations where radiologists and other physicians view, reprocess, and interpret images; and the printer [2] (Figure 1) for printing images.

The glue that holds all these components together is the Digital Imaging and Communication in Medicine (DICOM) standard (https://www.dicomstandard.org/current/, accessed December 5, 2021)), American College of Established in 1985 by. Radiology (ACR) and the National Electrical Manufacturers Association (NEMA). DICOM provides a file format for representing medical images and associated data (for example, patient information, instrument and imaging process information, imaging information). In addition, it provides a framework, services, and tools to process, store, display, and transfer medical image data between all components and devices from different vendors. Each PACS device implements only a subset of the DICOM standard required for a specific function (presented in the DICOM Conformance Statement of the specific device or its software).

While digitization of healthcare has many advantages, such as reduced turnaround time for everyday hospital activities, improved usability, etc. On the downside, it turns medical institutions into a new and valuable target for cybercriminals. We have seen several recent attacks on medical institutions linked to data breaches (https://www.nytimes.com/2015/02/05/business/hackers-breached-data-of-millions-insurer-says.html, 5 Accessed December , 2021), ransomware (https://www.nbcnews.com/tech/security/cyberattack-hits-major-u-s-hospital-system-n1241254, accessed December 5, 2021), Insider threat (https://archives.fbi.gov/archives/dallas/press-releases/2011/dl031811.htm, accessed December 5, 2021) and DDOS attacks (https://thethreatreport.com/story- behind-the-ddos -attack-vs-boston) -Children’s Hospital/, accessed December 5, 2021)—to name just a few. Attackers can also add or remove evidence of medical conditions from medical scans [3] with the aim of changing the patient’s diagnosis and outcome. Duggal [4] demonstrated various attack scenarios on medical devices and healthcare infrastructure, such as obtaining patient information, fingerprint architecture, examining and changing diagnoses, gaining access to non-prescribed drugs, drug switching, medical DoS attacks on devices etc.

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As shown in our previous work, DICOM files can also be used for steganographic purposes [5]. Steganography deals with the concealment of secret information, including its storage and transmission. Various digital artifacts have been investigated in relation to both steganography applications and its detection, including digital text, digital images, video and audio files, file systems, cyber-physical systems, and networks. Using steganography, it is possible to create parasitic communication paths, often defined as covert channels, see for example reference. [6, 7, 8] for an overview on the topic. In particular, data can be hidden within a suitable carrier (for example, part of a protocol data unit that is not used or is optional) allowing remote endpoints to exchange secret information. Is. There are many intended scenarios for data hiding in DICOM files and traffic [5]. On the illegal side, this includes creating abusive communication paths to orchestrate infected devices, dropping malicious payloads to spread infections, running scanning or data collection campaigns, creating channels to transfer data between different devices, different To be, to create privacy, to enforce. – Leak and data exfiltration campaigns, just to name the most popular use cases. In contrast, legitimate applications include the detection, limiting, or prevention of threats or malware equipped with the ability to hide information, “enhanced” various DICOM data streams and entities with secret data to destroy machine learning-enabled attacks. ” Doing. Designing suitable algorithms to prevent malicious tampering and alteration of DICOM images.

In the past years, it has become common practice to classify steganographic methods (including covert channels) using so-called concealment patterns, which were introduced in [9], while their latest version exists in [10]. In the rest of this paper, we will indicate the appropriate pattern by reporting it in parentheses, i.e. using the notation borrowed from [10].

In summary, in this paper we show how to apply steganographic techniques to DICOM messaging and upper layer service for the purpose of creating secret channels. The most important contributions of this work can be summarized as follows:

The rest of the paper is structured as follows. Section 2 reviews related work, while Section 3 describes the basics of DICOM. Section 4 describes new proposed covert channels. We cover metrics and possible countermeasures in Section 5. The entropy-based detection method as well as its performance evaluation are shown in Section 6. The concluding remarks of this letter have been made in Section 7.

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Allows access to 1849 DICOM servers and 842 PACS servers exposed to the Internet through a web interface. Regardless of the intentional nature, the volume of the leak represents a real threat in terms of DICOM images and personal data. In more detail, weak security requirements, (eg default accounts, scripts between websites, unencrypted traffic, vulnerabilities in web servers) that characterize various DICOM/PACS deployments, can be used to view and edit 3D DICOM images. could [11]. Furthermore, the impact in terms of uncertainty is also largely due to open-source or small-fee PACS often used in small health institutions or practices [11]. Therefore, DICOM assets are expected to become a valuable target for attackers in the near future [12]. Fraud attempts are another significant attempt to circumvent the security of DICOM content. As a perfect example, Mirsky et al. [3] recently demonstrated how a malicious threat can remove evidence of medical conditions from volumetric medical scans. Specifically, using a 3D conditional GAN, they showed how to fool radiologists and the latest AI screening tools.

The growing prevalence of ransomware attacks against carefully selected targets may find PACS/DICOM deployments a highly profitable ecosystem (see e.g. [13]). Nevertheless, the complexity of the standard, the heterogeneity of various deployments, and the widespread digitization of medical content are effective drivers for exploiting DICOM content for information hiding and steganographic purposes. In fact, as shown in our early work [5] , which is extended by this paper, DICOM artifacts can be used to encode secret data via text steganography (e.g. white hiding a secret message through the manipulation of spaces), which is thus a wide variety of privacy-violating