ODINI: Escaping Sensitive Data From Faraday-Caged, Air-Gapped Computers via Magnetic Fields

Calcium homeostasis and low-frequency magnetic and electric field exposure: A systematic review and meta-analysis of in vitro studies

To investigate the effects of an extremely low-frequency (ELF) magnetic field on their fertility, adult male and female Sprague-Dawley rats were exposed to a 50 Hz sinusoidal magnetic field of approximately 25 microT (rms) for 90 days before they were mated with unexposed counterparts. Exposure to a 50 Hz field reduced male rat fertility. The number of pregnant females was reduced when mated with exposed males, and the number of resorptions increased. The effects of magnetic field on male fertility were shown to be partly reversible, when the same exposed group of males were remated 45 and 90 days after being removed from the fields. Exposure of adult female rats to 50 Hz magnetic fields for 90 days before mating significantly reduced their fertility. The mean numbers of implantations and living fetuses per litter were statistically significantly decreased in the 50 Hz group. These results suggest that low frequency magnetic fields have some adverse effects on fertility of male and female rats.

Highly-Secure Backdoors: Internet of Traitorous Things (IoTT)

Recent advances in the measurements with sensitive magnetic field sensors made the issue of magnetic shielding important. The application of high temperature superconductors allows to obtain full shielding in zero field cooling conditions for DC and low frequency magnetic fields by the means of the Meissner effect. However, currently used conventional bulk magnetic shields maintain full shielding only in low magnetic fields—up to approximately 10 mT. In this paper, it is proposed to apply an additional screen made of a coated superconducting tape in order to increase the magnetic field interval of full shielding region. Computer model of such set of screens was created in Matlab and validated experimentally. Improvement of shielding quality was observed experimentally and calculated with the model.

BackgroundThe increased use of electronic devices and technological advances has led to greater exposure to electromagnetic fields (EMF) in various occupational environments.ObjectivesThe study's objective was to assess the effect of work-related musculoskeletal disorders (WMSD) on the quality of life and physical activity levels of individuals exposed to high-frequency magnetic fields.MethodsThe mean age of one hundred and twenty EMF exposed workers was 37.44 ± 9.16 years. The following were assessed: musculoskeletal symptoms in the last 12 months (using the Extended Nordic Musculoskeletal Questionnaire Version (ENMQ) and the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ), working posture (Ovako Working Posture Analysis System (OWAS), physical activity level (International Physical Activity Questionnaire Short Form (IPAQ-Sf), and quality of life (World Health Organization Quality of Life Scale (WHOQOL-Bref).ResultsThe highest prevalence rate of ENMQ in the last 12 months was 77.5% (n = 31) in the low back region and 50% (n = 20) in the shoulder region in those exposed to high-grade magnetic fields. The mean scores of WHOQOL-Bref were given for those exposed to high and low magnetic fields and those not exposed to magnetic fields (M ± SD: 63.51 ± 8.35; 73.27 ± 9.37; 76.43 ± 8.43, respectively).ConclusionThe prevalence of WMSD in workers was found to be highest in the low back, shoulder, and hand region in HF-MF workers. LF-MF group, the highest concentration was found to be highest in the neck region. Prevalence rates have been reported for different body sites, with the highest prevalence rates observed in the group exposed to HF-MF. Quality of life was found to be lower in the group exposed to HF-MF.

Malware has become one of the most serious security threats to the Internet of Things (IoT). Detection of malware variants can inhibit the spread of malicious code from the traditional network to the IoT, and can also inhibit the spread of malicious code within the IoT, which is of great significance to the security detection and defense of the IoT. Since the terminals and the operating systems of IoT are very different from the traditional network, when malicious code is transferred from the traditional network to the IoT platform, the characteristics of the variants may change significantly. As a result, malicious code variant detection methods for traditional platforms cannot be directly applied to the IoT. In this article, a malware variant detection method for the IoT is proposed. First, we propose a feature representation method based on RGB image for IoT to solve the problem of representation difficulty caused by platform difference, which pays more attention to the assembly code and developer information of the malware. The generated image has richer texture information, which can dig out the deep association between the IoT variants and the original malicious code. Moreover, this article improves the convolutional neural network model by combining the self-attention mechanism and spatial pyramid pooling to solve the problem of large differences in the size of IoT malware. Experimental results show that our method can be used in cross-platform to detect malware variants in the IoT effectively.

Air-gapped computers are systems that are kept isolated from the Internet since they store or process sensitive information. In this paper, we introduce an optical covert channel in which an attacker can leak (or, exfiltlrate) sensitive information from air-gapped computers through manipulations on the screen brightness. This covert channel is invisible and it works even while the user is working on the computer. Malware on a compromised computer can obtain sensitive data (e.g., files, images, encryption keys and passwords), and modulate it within the screen brightness, invisible to users. The small changes in the brightness are invisible to humans but can be recovered from video streams taken by cameras such as a local security camera, smartphone camera or a webcam. We present related work and discuss the technical and scientific background of this covert channel. We examined the channel's boundaries under various parameters, with different types of computer and TV screens, and at several distances. We also tested different types of camera receivers to demonstrate the covert channel. Lastly, we present relevant countermeasures to this type of attack. Lastly, we present relevant countermeasures to this type of attack.

With the development of bio-magnetism, magnetic field (MF) treatment has been widely used in many fields, including Agriculture, Horticulture and Forestry. Among seed priming methods, the use of the MF treatment as a physical priming has the advantages of creating no pollution and allowing an easy operation. In this experiment, the low frequency magnetic field (LFMF) was generated by Viofor JPS Delux - a patented device adopted from human medical magnetic therapy. Seeds of radish (Raphanus sativus L.) cultivars Carmen (C) and Szkarłatna z Białym Końcem (SBK) were subjected to the routine accelerated aging (AA) procedure. Next, they were removed from the stress conditions, dried back to the standard moisture content level, and then treated with 10, 20 and 30 μT LFMF for 30, 60, 90 and 120 min., respectively. Afterwards, their seed quality was evaluated by germination and vigour tests. The results showed that LFMF treatment has improved the germination and vigour of the AA radish seeds. The optimal seed treatment conditions of the cultivars were different: for the cultivar C - 20 μT, 120 min. and for the cultivar SBK - 20 μT, 60 min. The quality of radish seeds and seedlings was also tested by a new method based on their ultra-weak photon emission during their germination and seedling growth. The received result could prove that LFMF treatment on AA seeds could have changed the metabolic processes responsible for germination and growth rate, but it had no harmful effect on the nature of their growth process.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Research ProblemCyber attacks are targeting the cloud computing systems, where enterprises, governments, and individuals are outsourcing their storage and computational resources for improved scalability and dynamic management of their data. However, the different types of cyber attacks, as well as the different attack goals, create difficulties providing the right security solution needed. This is because different cyber attacks are associated with different threats in the cloud computing systems, where the importance of threats varies based on the cloud user requirements. For example, a hospital patient record system may prioritize the security of cyber attacks tampering patient records, while a media storage system may prioritize the security of cyber attacks carrying out a denial of service attack for ensuring a high availability. As a result, it is of paramount importance to analyze the risk associated with the cloud computing systems taking into account the importance of threats based on different cloud user requirements.However, the current risk evaluation approaches focus on evaluating the risk associated with the asset, rather than the risk associated with different types of threats. Such a holistic approach to risk evaluation does not show explicitly how different types of threats contribute to the overall risk of the cloud computing systems. Consequently, This makes it difficult for security administrators to make fine-grained decisions in order to select security solutions based on different importance of threats given the cloud user requirements. Therefore, it is necessary to analyze the risk of the cloud computing systems taking into account the different importance of threats, which enables the allocation of resources to reduce particular threats, identify the risk associated with different threats imposed, and identify different threats associated with cloud components.Proposed SolutionThe STRIDE threat modeling framework (short for STRIDE) is proposed by Microsoft, which can be used for threat categorization. Using the STRIDE, we propose a threat-guided risk evaluation approach for the cloud computing systems, which can evaluate the risk associated with each threat category from the STRIDE explicitly. Further, we utilize seven different types of security metrics to evaluate the risk namely: \textit{component, component-threat, threat-category, snapshot, path-components, path-threat}, and \textit{overall asset}. Component, component-threat, threat-cateory, and snapshot risks measure the total risk on a component, component risk for a particular threat category, total snapshot risk for a single threat, and the total risk of the snapshot considering all threat categories, respectively. Path-components, path-threat, and overall asset measure the total risk of components in an attack path, the risk of a single threat category in the attack path, and the overal risk to an asset considering all attack paths, respectively. These metrics makes it possible to measure the contribution of each threat category to the overall risk more precisely.When a vulnerability is discovered in a component (e.g. a Virtual Machine) of the Cloud deployment, the administrator first determines which types of threats could be posed should the vulnerability be successfully exploited, and what would be the impacts of each of those threats on the asset. The impact assignment of each threat type is weighted depending on the importance of the component. For example, a Virtual Machine (VM) that acts a Web Server in a medical records management application could be assigned a higher weighting for \textit{denial-of-service} threats because if such attacks are successfully launched then the rest of the VMs that are reached through the Web Server will be unavailable. On the other hand, a vulnerability discovered in a VM that hosts a database of medical records would be rated highest impact for \textit{information disclosure} because if it is compromised confidentiality of the medical history of patients will be violated.By multiplying the probability of successfully exploiting the vulnerability with the threat impact, we compute the risk of each threat type. The variation in the assignment of impact for different threat types enables our approach to compute risks associated with the threats - thus empowering the security administrator with the ability to make fine-grained decisions on how much resources to allocate for mitigating which type of threat and which threats to prioritize. We evaluated the usefulness of our approach through its application to attack scenarios in an example Cloud deployment. Our results show that it is more effective and informative to administrators compared to asset-based approaches to risk evaluation.

The Internet of Things (IoT) enable the IoT to sense and respond using the power of computing to autonomously come up with the best solutions for any industry today. However, Internet of Things have vulnerabilities since it can be hacked by cybercriminals. The cybercriminals know where the IoT vulnerabilities are, such as unsecured update mechanisms and malware (Malicious Software) to attack the IoT devices. The recently posted IoT-23 dataset based on several IoT devices such as Philips Hue, Amazon Echo devices and Somfy door lock were used for machine learning classification algorithms and data mining techniques with training and testing for predictive modelling of a variety of malware attacks like Distributed Denial of Service (DDoS), Command and Control (C&C) and various IoT botnets like Mirai and Okiru. This paper aims to develop predictive modeling that will predict malicious software to protect IoT and reduce vulnerabilities by using machine learning and data mining techniques. We collected, analyzed and processed benign and several of malicious software in IoT network traffic. Malware prediction is crucial in maintaining IoT devices’ safety and security from cybercriminals’ activities. Furthermore, the Principal Component Analysis (PCA) method was applied to determine the important features of IoT-23. In addition, this study compared with previous studies that used the IoT-23 dataset in terms of accuracy rate and other metrics. Experiments show that Random Forest (RF) classifier achieved the predictive model produced classification accuracy 0.9714% as well as predict 8754 samples with various types of malware and obtained 0.9644% of Area Under Curve (AUC) which outperforms several bassline machine learning classification models.

With the development of intelligent perception, recognition technology and pervasive computing, Internet of things(IoT) is widely used, the security problem is also concerned by more and more researchers. IoT is a double-edged sword. On the one hand, it has great potential in simplifying the business process and provides an effective way for the enterprise to interact with the customers. But on the other hand, it also provides convenience for cyber crimes and hackers.First of all, Three layers logic architecture of IoT is introduced, and the security problems at each level and the key points of the research are expounded, and then the security requirements are analyzed. The main causes of security problems are summarized and analyzed:physical attack and the threat of equipment and malware, file attack and hacker attack. Finally, through the application of the existing technology in IoT environment, the exploration of new technology and the security of the hardware related to IoT, the security of the software and the security team, the future research direction of the security of IoT is pointed out.

Recent advances in Internet of Things (IoT) technologies require a new type of IoT security environment. Various heterogeneous smart devices have easy access to IoT environment, and as the number of users increases, they are exposed to various threats such as malicious attacks on IoT devices and IoT infrastructure, and data tampering by malicious code. Malware detection in IoT requires data and models for continuous and changing learning of smart devices. Methods/Statistical analysis: To minimize these security threats, various malware detection techniques in the field of IoT security have been studied. Malware detection in IoT environment is important for data derivation and learning model required for continuous and changing learning of smart devices. The metadata of malware detection can be normalized by the value of device id, time, behavior, location and state. This paper proposes behavior-based malware detection using deep learning (BMD-DL). Findings: BMD-DL was able to collect metadata about behavior-based malicious behavior and learn and detect malicious codes through deep learning. In addition, through the learned model, IoT Security is provided by disconnecting malicious devices that cause malicious behavior in the IoT environment. Improvements/Applications: BMD-DL collects behavioral data generated from multiple devices in the IoT and applies the results learned through deep learning to detect persistent malware.

Advancements in artificial intelligence (AI), the Internet of Things (IoT), and cloud computing have paved the way for transformative technologies in the healthcare industry. This report focuses on the development of an AI-Enabled Mobile Cloud-Based Federated IoMT Architecture for ambient-assisted healthy living. The objective is to leverage the power of AI, IoT, and cloud computing to create intelligent and context-aware healthcare solutions that promote healthy living in personalized and efficient ways. The integration of AI with the Internet of Medical Things (IoMT) enables the creation of ambient-assisted living (AAL) environments [1], which utilize IoT devices and sensors to support individuals in their daily routines. Real-time data collected from these IoT devices facilitates personalized healthcare services, enabling individuals to live independently and safely. To ensure secure and efficient operation, lightweight two-factor authentication frameworks with privacy preservation for mobile sinks in the social IoMT have been investigated. These frameworks aim to protect sensitive healthcare data transmitted through the IoMT ecosystem. IoT architectures based on microservices have been explored for AAL environments, aiming to provide personalized assistance and healthcare support to seniors.

With the popularisation and development of the internet of things, big data and cloud computing, the search of data in cloud-based internet of things becomes a hot research topic. However, the sensitive data, such as the medical data collected by wearable devices, is inevitable to be stored in the cloud server. Homomorphic encryption has the ability to calculate the ciphertext without decryption. We separate the calculating and the decryption into different security domains to preserve the privacy of sensitive data, so the original plaintext would not be exposed in the cloud. Hence, we can compare two ciphertexts to get the difference of them in a privacy preserving way. In order to accelerate the search process of range query, we build an encrypted self-balancing binary index tree. Based on oblivious RAM, the searching scheme can hide the access patterns of the node of tree. The actual nodes and logic relation of tree are stored on different servers. A sample implementation of the proposed scheme is given, and the experimental results and analysis are presented to illustrate the scheme's effectiveness and security.