Effective Secure Data Agreement Approach-based cloud storage for a healthcare organization

In recent days, there has been a significant development in the field of computers as they need to handle the vast resource using cloud computing and performing various cloud services. The cloud helps to manage the resource dynamically based on the user demand and is transmitted to multiple users in healthcare organizations. Mainly the cloud helps to reduce the performance cost and enhance data scalability & flexibility. The main challenges faced by the existing technologies integrated with the cloud need to be solved in managing the data and the problem of data heterogeneity. As the above challenges, mitigation makes the services more data stable should the healthcare organization identify the malware. Developed countries are utilizing the services through the cloud as it needs more security. In this work, a secure data agreement approach is proposed as it is associated with feature extraction with cloud computing for healthcare to examine and enhance the user parties to make effective decisions. The proposed method classifies into two components. The first component deals with the modified data formulation algorithm, used to identify the relationship among variables, i.e., data correlation, and validate the data using trained data. It helps to achieve data reduction and data scale development. In the second component, Feature selection is used to validate the model using subset selection to determine the model fitness based on the data. It is necessary to have more samples of different Android applications to examine the framework using factors like data correctness and the F-measure. As feature selection is a concern, this study focuses on Chi-square, gain ratio, information gain, logistic regression analysis, OneR, and PCA

The Role of the Adversary Model in Applied Security Research

Adversary models have been integral to the design of provably-secure cryptographic schemes or protocols. However, their use in other computer science research disciplines is relatively limited, particularly in the case of applied security research (e.g., mobile app and vulnerability studies). In this study, we conduct a survey of prominent adversary models used in the seminal field of cryptography, and more recent mobile and Internet of Things (IoT) research. Motivated by the findings from the cryptography survey, we propose a classification scheme for common app-based adversaries used in mobile security research, and classify key papers using the proposed scheme. Finally, we discuss recent work involving adversary models in the contemporary research field of IoT. We contribute recommendations to aid researchers working in applied (IoT) security based upon our findings from the mobile and cryptography literature. The key recommendation is for authors to clearly define adversary goals, assumptions and capabilities

An Analysis of the Relationship between Security Information Technology Enhancements and Computer Security Breaches and Incidents

Financial services institutions maintain large amounts of data that include both intellectual property and personally identifiable information for employees and customers. Due to the potential damage to individuals, government regulators hold institutions accountable for ensuring that personal data are protected and require reporting of data security breaches. No company wants a data breach, but finding a security incident or breach early in the attack cycle may decrease the damage or data loss a company experiences. In multiple high profile data breaches reported in major news stories over the past few years, there is a pattern of the adversary being inside the company’s network for months, and often law enforcement is the first to inform the company of the breach. The problem that was investigated in this case study was whether new information technology (IT) utilized by Fortune 500 financial services companies led to the changes in data security incidents and breaches. The goal of this dissertation is to gain a deeper understanding on how IT can increase awareness of a security incident or breach, and can also decrease security incidents and breaches. This dissertation also explores how threat information sharing increases awareness and decreases information security incidents and breaches. The objective of the study was to understand how changes in IT can influence an increase or decrease in data security breaches. This investigation was a case study of nine Fortune 500 financial services companies to understand what types of IT increase or decrease detection of security incidents and breaches. An increase in detecting and stopping a security incident or breach may have positive effects on the security of an enterprise. The longer a hacker has access to IT systems, the more entrenched they become and the more time the hacker has to locate data with high value. Time is of the essence to detect a compromise and react. The results of the case study showed that Fortune 500 companies utilized new IT that allowed them to improve their visibility of security incidents and breaches from months and years to hours and days

Detection of Anomalous Behavior of IoT/CPS Devices Using Their Power Signals

Embedded computing devices, in the Internet of Things (IoT) or Cyber-Physical Systems (CPS), are becoming pervasive in many domains around the world. Their wide deployment in simple applications (e.g., smart buildings, fleet management, and smart agriculture) or in more critical operations (e.g., industrial control, smart power grids, and self-driving cars) creates significant market potential ($ 4-11 trillion in annual revenue is expected by 2025). A main requirement for the success of such systems and applications is the capacity to ensure the performance of these devices. This task includes equipping them to be resilient against security threats and failures. Globally, several critical infrastructure applications have been the target of cyber attacks. These recent incidents, as well as the rich applicable literature, confirm that more research is needed to overcome such challenges. Consequently, the need for robust approaches that detect anomalous behaving devices in security and safety-critical applications has become paramount. Solving such a problem minimizes different kinds of losses (e.g., confidential data theft, financial loss, service access restriction, or even casualties). In light of the aforementioned motivation and discussion, this thesis focuses on the problem of detecting the anomalous behavior of IoT/CPS devices by considering their side-channel information. Solving such a problem is extremely important in maintaining the security and dependability of critical systems and applications. Although several side-channel based approaches are found in the literature, there are still important research gaps that need to be addressed. First, the intrusive nature of the monitoring in some of the proposed techniques results in resources overhead and requires instrumentation of the internal components of a device, which makes them impractical. It also raises a data integrity flag. Second, the lack of realistic experimental power consumption datasets that reflect the normal and anomalous behaviors of IoT and CPS devices has prevented fair and coherent comparisons with the state of the art in this domain. Finally, most of the research to date has concentrated on the accuracy of detection and not the novelty of detecting new anomalies. Such a direction relies on: (i) the availability of labeled datasets; (ii) the complexity of the extracted features; and (iii) the available compute resources. These assumptions and requirements are usually unrealistic and unrepresentative. This research aims to bridge these gaps as follows. First, this study extends the state of the art that adopts the idea of leveraging the power consumption of devices as a signal and the concept of decoupling the monitoring system and the devices to be monitored to detect and classify the "operational health'' of the devices. Second, this thesis provides and builds power consumption-based datasets that can be utilized by AI as well as security research communities to validate newly developed detection techniques. The collected datasets cover a wide range of anomalous device behavior due to the main aspects of device security (i.e., confidentiality, integrity, and availability) and partial system failures. The extensive experiments include: a wide spectrum of various emulated malware scenarios; five real malware applications taken from the well-known Drebin dataset; distributed denial of service attack (DDOS) where an IoT device is treated as: (1) a victim of a DDOS attack, and (2) the source of a DDOS attack; cryptomining malware where the resources of an IoT device are being hijacked to be used to advantage of the attacker’s wish and desire; and faulty CPU cores. This level of extensive validation has not yet been reported in any study in the literature. Third, this research presents a novel supervised technique to detect anomalous device behavior based on transforming the problem into an image classification problem. The main aim of this methodology is to improve the detection performance. In order to achieve the goals of this study, the methodology combines two powerful computer vision tools, namely Histograms of Oriented Gradients (HOG) and a Convolutional Neural Network (CNN). Such a detection technique is not only useful in this present case but can contribute to most time-series classification (TSC) problems. Finally, this thesis proposes a novel unsupervised detection technique that requires only the normal behavior of a device in the training phase. Therefore, this methodology aims at detecting new/unseen anomalous behavior. The methodology leverages the power consumption of a device and Restricted Boltzmann Machine (RBM) AutoEncoders (AE) to build a model that makes them more robust to the presence of security threats. The methodology makes use of stacked RBM AE and Principal Component Analysis (PCA) to extract feature vector based on AE's reconstruction errors. A One-Class Support Vector Machine (OC-SVM) classifier is then trained to perform the detection task. Across 18 different datasets, both of our proposed detection techniques demonstrated high detection performance with at least ~ 88% accuracy and 85% F-Score on average. The empirical results indicate the effectiveness of the proposed techniques and demonstrated improved detection performance gain of 9% - 17% over results reported in other methods