Tampering Events Research Articles

General Review on Opioids

Opioids are widely utilized by the public as both legal and illegal substances. Opiates, particularly heroin, are among the most widely used illicit narcotics and still account for the majority of seizures globally, despite the development of Novel Psychoactive Substances (NPS), particularly new fentanyl derivatives. The article gives a general review of opiates' pharmacology, analgesic action, and classification. It discussed how they might be abused by the general public and in the event of tampering. Because of the hazards of accidents linked to opiate use and dependence, opiates are furthermore closely monitored in workplace drug testing programs around the world. Also it covered analytical techniques for opiate detection in biological fluids. In the modern era, substance abuse has emerged as a global epidemic. Opium and its derivatives are second only to cannabis in terms of global consumption among illegal narcotics.

Revamped Dual-key Stealth Address Protocol for IoT Using Encryption and Decentralized Storage

Blockchain technology unarguably has over a decade gained widespread attention owing to its often-tagged disruptive nature and remarkable features of decentralization, immutability and transparency among others. However, the technology comes bundled with challenges. At center-stage of these challenges is privacy-preservation which has massively been researched with diverse solutions proposed geared towards privacy protection for transaction initiators, recipients and transaction data. Dual-key stealth address protocol for IoT (DkSAP-IoT) is one of such solutions aimed at privacy protection for transaction recipients. Induced by the need to reuse locally stored data, the current implementation of DkSAP-IoT is deficient in the realms of data confidentiality, integrity and availability consequently defeating the core essence of the protocol in the event of unauthorized access, disclosure or data tampering emanating from a hack and theft or loss of the device. Data unavailability and other security-related data breaches in effect render the existing protocol inoperable. In this paper, we propose and implement solutions to augment data confidentiality, integrity and availability in DkSAP-IoT in accordance with the tenets of information security using symmetric encryption and data storage leveraging decentralized storage architecture consequently providing data integrity. Experimental results show that our solution provides content confidentiality consequently strengthening privacy owing to the encryption utilized. We make the full code of our solution publicly available on GitHub.

Nearfield RF Sensing for Feature-Detection and Algorithmic Classification of Tamper Attacks

As digital assets grow in value tremendously, the electronic cybersystems must become more secure. Physical attacks on electronic systems have been widely used to hack, then steal or modify assets in a criminal manner. These attacks are possible if adversaries gain physical access to the secure cybersystem. Therefore, a solution to protect the system against physical attacks is required. In this article, we detect and classify certain types of tampering events, such as probing, removing, adding, or moving components of a system, using the frequency characteristics of a wide-band antenna repurposed as a nearfield probe. Our idea is based on the notion that changes in the radio channel have an influence on an antenna’s frequency characteristics, which can be utilized to detect and identify certain tampering events. We empirically validate our claim by emulating various types of physical tampering incidents and collecting the ensuing RF signals leading to learnable feature maps. Our experimental results demonstrate that we can identify specific types of tampering events with more than 81.5% accuracy using machine learning algorithms. We ensure the robustness of our proposed technique for a variety of hardware platforms and in a noisy environment such as with the presence of both electrical additive white Gaussian noise from thermal effects, and mechanical perturbations on the setup.

Physical Tampering Detection Using Single COTS Wi-Fi Endpoint.

This paper proposes a practical physical tampering detection mechanism using inexpensive commercial off-the-shelf (COTS) Wi-Fi endpoint devices with a deep neural network (DNN) on channel state information (CSI) in the Wi-Fi signals. Attributed to the DNN that identifies physical tampering events due to the multi-subcarrier characteristics in CSI, our methodology takes effect using only one COTS Wi-Fi endpoint with a single embedded antenna to detect changes in the relative orientation between the Wi-Fi infrastructure and the endpoint, in contrast to previous sophisticated, proprietary approaches. Preliminary results show that our detectors manage to achieve a 95.89% true positive rate (TPR) with no worse than a 4.12% false positive rate (FPR) in detecting physical tampering events.

Variational Bayesian Learning of Generalized Dirichlet-Based Hidden Markov Models Applied to Unusual Events Detection.

Learning a hidden Markov model (HMM) is typically based on the computation of a likelihood which is intractable due to a summation over all possible combinations of states and mixture components. This estimation is often tackled by a maximization strategy, which is known as the Baum-Welch algorithm. However, some drawbacks of this approach have led to the consideration of Bayesian methods that add a prior over the parameters in order to work with the posterior probability and the marginal likelihood. These approaches can lead to good models but to the cost of extremely long computations (e.g., Markov Chain Monte Carlo). More recently, variational Bayesian frameworks have been proposed as a Bayesian alternative that keeps the computation tractable and the approximation tight. It relies on the introduction of a prior over the parameters to be learned and on an approximation of the true posterior distribution. After proving good standing in the case of finite mixture models and discrete and Gaussian HMMs, we propose here to derive the equations of the variational learning of the Dirichlet mixture-based HMM, and to extend it to the generalized Dirichlet. The latter case presents several properties that make the estimation more accurate. We prove the validity of this approach within the context of unusual event detection in public areas using the University of California San Diego data sets. HMMs are trained over normal video sequences using the typical Baum-Welch approach versus the variational one. The variational learning leads to more accurate models for the detection and localization of anomaly, and the general HMM approach is shown to be versatile enough to handle the detection of various synthetically generated tampering events.

Automated camera sabotage detection for enhancing video surveillance systems

Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke.

A theory of integrating tamper evidence with stabilization

We propose the notion of tamper-evident stabilization –that combines stabilization with the concept of tamper evidence– for computing systems. On the first glance, these notions are contradictory; stabilization requires that eventually the system functionality is fully restored whereas tamper evidence requires that the system functionality is permanently degraded in the event of tampering. Tamper-evident stabilization captures the intuition that the system will tolerate perturbation upto a limit. In the event that it is perturbed beyond that limit, it will exhibit permanent evidence of tampering, where it may provide reduced (possibly none) functionality. We compare tamper-evident stabilization with (conventional) stabilization and with active stabilization and propose an approach to verify tamper-evident stabilizing programs in polynomial time. We demonstrate tamper-evident stabilization with two examples and argue how approaches for designing stabilization can be used to design tamper-evident stabilization. We also study issues of composition in tamper-evident stabilization. Finally, we point out how tamper-evident stabilization can effectively be used to provide tradeoff between fault-prevention and fault tolerance.

Design and Implementation of Automatic Defensive Websites Tamper-Resistant System

Webpages tampering attack has become one of the most serious hazards to network security. Security measures of Website system were limited to install network layer protection devices, thus they could not provide effective monitoring and protection for illegal page tampering attack which making use of vulnerabilities of operating system. In this paper, we analyzed the research development of domestic and international Webpages tamper-resistant technologies, presented Website tamper-resistant technology system architecture of combining the third-generation anti-tampering technology with hardware device. We also established an integrated anti-tampering system that could actively detect attacks and dynamically defend tampering attack for Websites. Test results showed that the system could effectively prevent Webpages to be tampered, and could recovery real time in the event of tampering and alert administrators. It was proved that the deployment of this system would not result in a performance loss of Web server environment.

Evaluation of the Alcopatch, a transdermal dosimeter for monitoring alcohol consumption.

The Alcopatch is an improved transdermal dosimeter for the measurement of alcohol consumption, by detection of ethanol in fluid excreted from the skin. The device is worn as a band around the ankle and provides a visual signal in the event of tampering. Fourteen volunteers wore duplicate Alcopatches for a period of 7 or 8 days, while keeping a written record of their beverage alcohol consumption. Ethanol concentration in the Alcopatch was measured by gas chromatography and correlated with self-reported consumption. All alcohol consumption in excess of 0.25 g/kg/day resulted in measurable levels of ethanol in the Alcopatch. A positive correlation was observed between the reported consumption of ethanol (in g/kg/day) and the concentration of ethanol in the Alcopatch (square root, in mg/dl) (y = 0.91x + 0.28, r = 0.61) in 12 of 14 subjects. The Alcopatch detected the consumption of beverage alcohol with high sensitivity and specificity over a period of 7 to 8 days and may be useful for the study of target populations.