A novel fuzzy sliding mode control (FSMC) strategy is proposed to enhance the robustness and stability of position control for underactuated quadrotor unmanned aerial vehicles (UAVs) in the presence of external disturbances and model uncertainties. To realize the adaptive ability and robustness of the system in complex dynamic environments, an intelligent two-dimensional fuzzy controller is designed based on traditional sliding mode control (SMC) to adjust SMC parameters in real time, thereby adapting to the variable structure parameters of the system. First, based on the designed filter variables regarding errors, traditional SMC is used to reduce tracking errors. Then, the fuzzy logic module (FLM) combined with SMC, i.e., the self-learning module (FLM+SMC), is developed based on the filter variables and their rate of change to adjust the two parameters of the above SMC. Subsequently, the output signals of the FLM are fed back into the SMC module, and then a closed-loop tuning system using FSMC is developed for the UAVs. Moreover, the stability of the FSMC is rigorously verified using the Lyapunov theory. Finally, comprehensive simulations demonstrate that the designed FSMC not only offers accurate trajectory precision but also has robustness and disturbance rejection, and comparative simulations using SMC and adaptive radial basis function neural network control (RBFNNC) are used to validate the result.

Deep learning has achieved excellent results in many fields due to powerful feature extraction and learning ability. In this study, an improved method for analog circuit fault diagnosis based on a deep residual network is presented. The proposed method utilizes a ResNet to extract the performance characteristics of an analog circuit and determine the fault type of a component to realize the fault diagnosis of a circuit. The Short-time Fourier Transform is used to convert the time-domain output signals of a circuit into two-dimensional circuit spectrum maps, which are further used as the ResNet input. The fault diagnostic performance of the proposed method is verified by simulation with the Sallen-key bandpass filter circuit and the Four-opamp biquad high-pass filter circuit. The simulation results show that the proposed method performs well on both test circuits, achieving the diagnostic accuracy of up to 99.1% on the second-mentioned circuit.

In the modern world, Machine Learning (ML) touches our day-to-day routine in various ways. Researchers have been actively working on adding intelligence to Intrusion Detection Systems (IDSs) using ML techniques because a traditional IDS can detect known attacks but is incapable of detecting unknown attacks. Two major factors on which the efficiency of an intelligent IDS model depends are - the data and the mechanism used by the model to learn the data. This paper focuses on the contribution of data, by performing an analysis of recently published datasets, namely, UNSW-NB15, Bot-IoT, and CSE-CIC-IDS2018, employing ML algorithms including Random Forest (RF), Support Vector Machines (SVMs), Keras Deep Learning models, and XGBoost. The paper compares the performance of an ML-based IDS by training it with each of them, thereby, analyzing how the choice of a dataset impacts the performance.

The security of Android Debug Bridge (ADB) has attracted much attention from researchers, because it has a high privilege level and a low level of protection. Many attacks on Android systems have taken advantage of the security holes of ADB. Thus, in the updating patch of Android 4.2.2, a security feature secure USB debugging was implemented so that only trusted hosts can use ADB. Our research analyzes its protection effects on ADB based attacks and found that the new feature cannot provide sufficient protection when the host used to connect with Android devices has been compromised. A demonstration attack following this method is given along with an improvement design of the security mechanism of USB Debugging Mode. An implementation of this design and its evaluation are also provided to demonstrate its effectiveness.

The threat of digital counterfeiting and forgery of integrated circuits is growing at an alarming rate. At the same time, the functionalities of integrated circuits are becoming much more complex. This paper presents a design for cryptographic applications using Physically Unclonable Functions (PUFs) that can be implemented on an FPGA taking advantage of its unique architecture. The first part of the research involves development of techniques for the generation of uniquely distinguishable responses from ROPUFs taking advantage of the unique architecture of the FPGA and the latter part involves development of error correction technique using Artificial Neural Networks. The proposed design is implemented on several Xilinx Spartan FPGAs and the Hamming distances for the responses are computed and analyzed. The uniqueness of the responses is found to be 49.0625%. It is also found that the results of the proposed ECC also prove to be computationally efficient than the conventional BCH codes.

Access control is a means by which the ability to access the system is explicitly enabled or restricted in some way. Access control system enables an authority to control access to areas and resources in a given physical facility or computer-based information system. In Role-based Access Control (RBAC) model, access to resources is based on the role of the user in an organization. Previous RBAC models have encountered various problems in meeting the growing and diverse security needs of the organizations. Hence, we propose Isolation Enabled Role-based Access Control (I-RBAC) model. The basic idea of our model is to incorporate the isolation concepts into the NIST (National Institute of Standards and Technology) RBAC model such that it can be applied in a broader domain. In I-RBAC, the operation on an object by the role is executed inside isolation environment if the role or the operation is predefined to be isolated. Typical roles include inexperienced personnel (such as intern doctors) and delegatee. At the end of each session a security check for accumulated modifications will notify the user about the consistency of the environments. Evaluation of the implementation of the prototype on a Health Care System demonstrates the effectiveness of I-RBAC model. The key idea is to ensure system availability at all times for all the roles, while simultaneously ensuring the system integrity and security. Another main advantage is that, it would be a cost-effective alternative to building a separate RBAC system to enable otherwise disallowed accesses, such as the training roles.

In this article, we present an approach for realizing a safe execution environment (SEE) that enables users to “try out” new software (or configuration changes to existing software) without the fear of damaging the system in any manner. A key property of our SEE is that it faithfully reproduces the behavior of applications, as if they were running natively on the underlying (host) operating system. This is accomplished via one-way isolation: processes running within the SEE are given read-access to the environment provided by the host OS, but their write operations are prevented from escaping outside the SEE. As a result, SEE processes cannot impact the behavior of host OS processes, or the integrity of data on the host OS. SEEs support a wide range of tasks, including: study of malicious code, controlled execution of untrusted software, experimentation with software configuration changes, testing of software patches, and so on. It provides a convenient way for users to inspect system changes made within the SEE. If these changes are not accepted, they can be rolled back at the click of a button. Otherwise, the changes can be committed so as to become visible outside the SEE. We provide consistency criteria that ensure semantic consistency of the committed results. We develop two different implementation approaches, one in user-land and the other in the OS kernel , for realizing a safe-execution environment. Our implementation results show that most software, including fairly complex server and client applications, can run successfully within our SEEs. It introduces low performance overheads, typically below 10 percent.

Unlike today's reactive approaches, information flow based approaches can provide positive assurances about overall system integrity, and hence can defend against sophisticated malware. However, there hasn't been much success in applying information flow based techniques to desktop systems running modern COTS operating systems. This is, in part, due to the fact that a strict application of information flow policy can break existing applications and OS services. Another important factor is the difficulty of policy development, which requires us to specify integrity labels for hundreds of thousands of objects on the system. This paper develops a new approach for proactive integrity protection that overcomes these challenges by decoupling integrity labels from access policies. We then develop an analysis that can largely automate the generation of integrity labels and policies that preserve the usability of applications in most cases. Evaluation of our prototype implementation on a Linux desktop distribution shows that it does not break or inconvenience the use of most applications, while stopping a variety of sophisticated malware attacks.