Biblio

Even as Internet of Things (IoT) network security grows, concerns about the security of IoT devices have arisen. Although a few companies produce IP-connected gadgets for such ranging from small office, their security policies and implementations are often weak. They also require firmware updates or revisions to boost security and reduce vulnerabilities in equipment. A brownfield advance is necessary to verify systems where these helpless devices are present: putting in place basic security mechanisms within the system to render the system powerless possibly. Gadgets should cohabit without threatening their security in the same device. IoT network security has evolved into a platform that can segregate a large number of IoT devices, allowing law enforcement to compel the communication of defenseless devices in order to reduce the damage done by its unlawful transaction. IoT network security appears to be doable in well-known gadget types and can be deployed with minimum transparency.

Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system.

Fully homomorphic encryption technologies allow you to operate on encrypted data without disclosing it, therefore they have a lot of potential for solving personal data storage and processing issues. Because of the increased interest in these technologies, various software tools and libraries that allow completely homomorphic encryption have emerged. However, because this subject of cryptography is still in its early stages, standards and recommendations for the usage of completely homomorphic encryption algorithms are still being developed. The paper presents the main areas of application of homomorphic encryption. The analysis of existing developments in the field of homomorphic encryption is carried out. The analysis showed that existing library implementations do not support the division and subtraction operation. The analysis revealed the need to develop a library of fully homomorphic encryption, which allows performing all mathematical operations on them (addition, difference, multiplication and division), as well as the relevance of developing its own implementation of a library of homomorphic encryption on integers. Then, implement the development of a fully homomorphic encryption library in C++ and on an ESP 32 microcontroller. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the scope of application of homomorphic encryption. A method of homomorphic division and subtraction is proposed that allows performing the division and subtraction operation on homomorphically encrypted data. The level of security, the types of operations executed, the maximum length of operands, and the algorithm's running time are all described as a consequence of numerical experimentation with parameters.

Wireless-fidelity (Wi-Fi) and Bluetooth are examples of modern wireless communication technologies that employ radio waves as the primary channel for data transmission. but it ought to find alternatives over the limitation and interference in the radio frequency (RF) band. For viable alternatives, visible light communication (VLC) technology comes to play as Light Fidelity (Li-Fi) which uses visible light as a channel for delivering very high-speed communication in a Wi-Fi way. In terms of availability, bandwidth, security and efficiency, Li-Fi is superior than Wi-Fi. In this paper, we present a Li-Fi-based indoor communication system. prototype model has been proposed for single user scenario using visible light portion of electromagnetic spectrum. This system has been designed for audio data communication in between the users in transmitter and receiver sections. LED and photoresistor have been used as optical source and receiver respectively. The electro-acoustic transducer provides the required conversion of electrical-optical signal in both ways. This system might overcome problems like radio-frequency bandwidth scarcity However, its major problem is that it only works when it is pointed directly at the target.

As an emerging technology, IoT is rapidly revolutionizing the global communication network with billions of new devices deployed and connected with each other. Many of these devices collect and transfer a large amount of sensitive or mission critical data, making security a top priority. Compared to traditional Internet, IoT networks often operate in open and harsh environment, and may experience frequent delays, traffic loss and attacks; Meanwhile, IoT devices are often severally constrained in computational power, storage space, network bandwidth, and power supply, which prevent them from deploying traditional security schemes. Authentication is an important security mechanism that can be used to identify devices or users. Due to resource constrains of IoT networks, it is highly desirable for the authentication scheme to be lightweight while also being highly effective. In this paper, we developed and evaluated a hash-chain-based multi-node mutual authentication algorithm. Nodes on a network all share a common secret key and broadcast to other nodes in range. Each node may also add to the hash chain and rebroadcast, which will be used to authenticate all nodes in the network. This algorithm has a linear running time and complexity of O(n), a significant improvement from the O(nˆ2) running time and complexity of the traditional pairwise multi-node mutual authentication.

Internet of Things devices collect and share data (IoT). Internet connections and emerging technologies like IoT offer privacy and security challenges, and this trend is anticipated to develop quickly. Internet of Things intrusions are everywhere. Businesses are investing more to detect these threats. Institutes choose accurate testing and verification procedures. In recent years, IoT utilisation has increasingly risen in healthcare. Where IoT applications gained popular among technologists. IoT devices' energy limits and scalability raise privacy and security problems. Experts struggle to make IoT devices more safe and private. This paper provides a machine-learning-based IDS for IoT network threats (ML-IDS). This study aims to implement ML-supervised IDS for IoT. We're going with a centralised, lightweight IDS. Here, we compare seven popular categorization techniques on three data sets. The decision tree algorithm shows the best intrusion detection results.

This work proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model’s decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model.

Modular exponentiation, especially for very large integers of hundreds or thousands of bits, is a commonly used function in popular cryptosystems such as RSA. The complexity of this algorithm is partly driven by the very large word sizes, which require many - often millions - of primitive operations in a CPU implementation, or a large amount of logic when accelerated by an ASIC. FPGAs, with their many embedded DSP resources have started to be used as well. In almost all cases, the calculations have required multiple - occasionally many - clock cycles to complete. Recently, blockchain algorithms have required very low-latency implementations of modular multiplications, motivating new implementations and approaches.In this paper we show nine different high performance modular exponentiation for 1024-bit operands, using a 1024-bit modular multiplication as it’s core. Rather than just showing a number of completed designs, our paper shows the evolution of architectures which lead to different resource mix options. This will allow the reader to apply the examples to different FPGA targets which may have differing ratios of logic, memory, and embedded DSP blocks. In one design, we show a 1024b modular multiplier requiring 83K ALMs and 2372 DSPs, with a delay of 21.21ns.

Distributed computation and AI processing at the edge has been identified as an efficient solution to deliver real-time IoT services and applications compared to cloud-based paradigms. These solutions are expected to support the delay-sensitive IoT applications, autonomic decision making, and smart service creation at the edge in comparison to traditional IoT solutions. However, existing solutions have limitations concerning distributed and simultaneous resource management for AI computation and data processing at the edge; concurrent and real-time application execution; and platform-independent deployment. Hence, first, we propose a novel three-layer architecture that facilitates the above service requirements. Then we have developed a novel platform and relevant modules with integrated AI processing and edge computer paradigms considering issues related to scalability, heterogeneity, security, and interoperability of IoT services. Further, each component is designed to handle the control signals, data flows, microservice orchestration, and resource composition to match with the IoT application requirements. Finally, the effectiveness of the proposed platform is tested and have been verified.

The marine and maritime domain is well represented in the Sustainable Development Goals (SDG) envisaged by the United Nations, which aim at conserving and using the oceans, seas and their resources for sustainable development. At the same time, there is a need for improved safety in navigation, especially in coastal areas. Up to date, there exist operational services based on advanced technologies, including remote sensing and in situ monitoring networks which provide aid to the navigation and control over the environment for its preservation. Yet, the possibilities offered by crowdsensing have not yet been fully explored. This paper addresses this issue by presenting an app based on a crowdsensing approach for improved safety and awareness at sea. The app can be integrated into more comprehensive systems and frameworks for environmental monitoring as envisaged in our future work.

The development of cloud apps enables people to exchange resources, goods, and expertise online with other clients. The material is more vulnerable to numerous security dangers from outsiders due to the fact that millions of users exchange data through the same system. How to maintain the security of this data is now the main concern. The current data protection system functions best when it places a greater priority on safeguarding data maintained in online storage than it does on cybersecurity during transportation. The data becomes open to intrusion attacks while being transferred. Additionally, the present craze states that an outside auditor may view data as it is being transmitted. Additionally, by allowing the hacker to assume a third-person identity while obtaining the information, this makes the data more susceptible to exploitation. The proposed system focuses on using encryption to safeguard information flow since cybersecurity is seen as a major issue. The approach also takes into account the fourth auditing issue, which is that under the recommended manner, the inspector is not allowed to see the user information. Tests have shown that the recommended technique improves security overall by making it harder for hackers to decode the supplied data.

This paper is a conceptual paper that explores how the sensemaking process by intelligence analysts completed within a cognitive immersive environment might be impacted by the inclusion of a progressive dialog system. The tools enabled in the sensemaking room (a specific instance within the cognitive immersive environment) were informed by tools from the intelligence analysis domain. We explore how a progressive dialog system would impact the use of tools such as the collaborative brainstorming exercise [1]. These structured analytic techniques are well established in intelligence analysis training literature, and act as ways to access the intended users' cognitive schema as they use the cognitive immersive room and move through the sensemaking process. A prior user study determined that the sensemaking room encouraged users to be more concise and representative with information while using the digital brainstorming tool. We anticipate that addition of the progressive dialog function will enable a more cohesive link between information foraging and sensemaking behaviors for analysts.

A power amplifier(PA) is inherently nonlinear device and is used in a communication system widely. Due to the nonlinearity of PA, the communication system is hard to work well. Digital predistortion (DPD) is the way to solve this problem. Using Volterra function to fit the PA is what most DPD solutions do. However, when it comes to wideband signal, there is a deduction on the performance of the Volterra function. In this paper, we replace the Volterra function with B-spline function which performs better on fitting PA at wideband signal. And the other benefit is that the orthogonality of coding matrix A could be improved, enhancing the stability of computation. Additionally, we use compressive sampling to reduce the complexity of the function model.

Nowadays, cloud computing has become one of the most important and easily available storage options. This paper represents providing a platform where the data redundancy and the data security is maintained. Materials and Methods: This study contains two groups, the elliptic curve cryptography is developed in group 1 with 480 samples and advanced encryption is developed in group 2 with 960 samples. The accuracy of each of the methods is compared for different sample sizes with G power value as 0.8. Result: Advanced elliptic curve cryptography algorithm provides 1.2 times better performance compared to conventional elliptic curve cryptography algorithm for various datasets. The results were obtained with a significance value of 0.447 (p\textgreater0.05). Conclusion: From the obtained results the advanced elliptic curve cryptography algorithm seems to be better than the conventional algorithm.

As the demand for effective information protection grows, security has become the primary concern in protecting such data from attackers. Cryptography is one of the methods for safeguarding such information. It is a method of storing and distributing data in a specific format that can only be read and processed by the intended recipient. It offers a variety of security services like integrity, authentication, confidentiality and non-repudiation, Malicious. Confidentiality service is required for preventing disclosure of information to unauthorized parties. In this paper, there are no ideal hash functions that dwell in digital signature concepts is proved.

This paper presents a study on the "Dynamic Load Altering Attacks" (D-LAAs), their effects on the dynamics of a transmission network, and provides a robust control protection scheme, based on polytopic uncertainties, invariance theory, Lyapunov arguments and graph theory. The proposed algorithm returns an optimal Energy Storage Systems (ESSs) placement, that minimizes the number of ESSs placed in the network, together with the associated control law that can robustly stabilize against D-LAAs. The paper provides a contextualization of the problem and a modelling approach for power networks subject to D-LAAs, suitable for the designed robust control protection scheme. The paper also proposes a reference scenario for the study of the dynamics of the control actions and their effects in different cases. The approach is evaluated by numerical simulations on large networks.

Energy trading in small groups or microgrids is interesting to study. The energy market may overgrow in the future, so accessing the energy market by small prosumers may not be difficult anymore. This paper has modeled a decentralized P2P energy trading and exchange system in a microgrid group. The Islanded microgrid system is simulated to create a small energy producer and consumer trading situation. The simulation results show the increasing energy transactions and profit when including V2G as an energy storage device. In addition, blockchain is used for system security because a peer-to-peer marketplace has no intermediary control.

Higher education management has problems producing 100% of graduates capable of responding to the needs of industry while industry also is struggling to find qualified graduates that responded to their needs in part because of the inefficient way of evaluating problems, as well as because of weaknesses in the evaluation of problem-solving capabilities. The objective of this paper is to propose an appropriate classification model to be used for predicting and evaluating the attributes of the data set of the student in order to meet the selection criteria required by the industries in the academic field. The dataset required for this analysis was obtained from a private firm and the execution was carried out using Chimp Optimization Algorithm (COA) based Deep Belief Neural Network (COA-DBNN) and the obtained results are compared with various classifiers such as Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The proposed model outperforms other classifiers in terms of various performance metrics. This critical analysis will help the college management to make a better long-term plan for producing graduates who are skilled, knowledgeable and fulfill the industry needs as well.

Secure hash functions are widely used in cryptographic algorithms to secure against diverse attacks. A one-way secure hash function is used in the various research fields to secure, for instance, blockchain. Notably, most of the hash functions provide security based on static parameters and publicly known operations. Consequently, it becomes easier to attack by the attackers because all parameters and operations are predefined. The publicly known parameters and predefined operations make the oracle regenerate the key even though it is a one-way secure hash function. Moreover, the sensitive data is mixed with the predefined constant where an oracle may find a way to discover the key. To address the above issues, we propose a novel one-way secure hash algorithm, OSHA for short, to protect sensitive data against attackers. OSHA depends on a pseudo-random number generator to generate a hash value. Particularly, OSHA mixes multiple pseudo-random numbers to produce a secure hash value. Furthermore, OSHA uses dynamic parameters, which is difficult for adversaries to guess. Unlike conventional secure hash algorithms, OSHA does not depend on fixed constants. It replaces the fixed constant with the pseudo-random numbers. Also, the input message is not mixed with the pseudo-random numbers; hence, there is no way to recover and reverse the process for the adversaries.

Cloud computing is a fast growing field that provides the user with resources like software, infrastructure and virtual hardware processing power. The steady rise of cloud computing in recent times allowed large companies and even individual users to move towards working with cloud storage systems. However, the risks of leakage of uploaded data in the cloud storage and the questions about the privacy of such systems are becoming a huge problem. Security incidents occur frequently everywhere around the world. Sometimes, data leak may occur at the server side by hackers for their own profit. Data being shared must be encrypted before outsourcing it to the cloud storage. Existing encryption/decryption systems utilize large computational power and have troubles managing the files. This paper introduces a file system that is a more efficient, virtual, with encryption/decryption scheme using parallel encryption. To make encryption and decryption of files easier, Parallel encryption is used in place of serial encryption which is integrated with Identity-Based Encryption in the file system. The proposed file system aims to secure files, reduce the chances of file stored in cloud storage getting leaked thus providing better security. The proposed file system, OutFS+, is more robust and secure than its predecessor, OutFS. Cloud outsourcing takes place faster and the files can be downloaded to the OutFS+ instance on the other side. Moreover, OutFS+ is secure since it is a virtual layer on the operating system and can be unmounted whenever the user wants to.

This document addresses the issue of the actual security level of PDF documents. Two types of detection approaches are utilized to detect dangerous elements within malware: static analysis and dynamic analysis. Analyzing malware binaries to identify dangerous strings, as well as reverse-engineering is included in static analysis for t1he malware to disassemble it. On the other hand, dynamic analysis monitors malware activities by running them in a safe environment, such as a virtual machine. Each method has its own set of strengths and weaknesses, and it is usually best to employ both methods while analyzing malware. Malware detection could be simplified without sacrificing accuracy by reducing the number of malicious traits. This may allow the researcher to devote more time to analysis. Our worry is that there is no obvious need to identify malware with numerous functionalities when it isn't necessary. We will solve this problem by developing a system that will identify if the given file is infected with malware or not.

The Manufacturer Usage Description (MUD) standard aims to reduce the attack surface for IoT devices by locking down their behavior to a formally-specified set of network flows (access control entries). Formal network behaviors can also be systematically and rigorously verified in any operating environment. Enforcing MUD flows and monitoring their activity in real-time can be relatively effective in securing IoT devices; however, its scope is limited to endpoints (domain names and IP addresses) and transport-layer protocols and services. Therefore, misconfigured or compromised IoTs may conform to their MUD-specified behavior but exchange unintended (or even malicious) contents across those flows. This paper develops PicP-MUD with the aim to profile the information content of packet payloads (whether unencrypted, encoded, or encrypted) in each MUD flow of an IoT device. That way, certain tasks like cyber-risk analysis, change detection, or selective deep packet inspection can be performed in a more systematic manner. Our contributions are twofold: (1) We analyze over 123K network flows of 6 transparent (e.g., HTTP), 11 encrypted (e.g., TLS), and 7 encoded (e.g., RTP) protocols, collected in our lab and obtained from public datasets, to identify 17 statistical features of their application payload, helping us distinguish different content types; and (2) We develop and evaluate PicP-MUD using a machine learning model, and show how we achieve an average accuracy of 99% in predicting the content type of a flow.

Trust is a cognitive ability that can be dependent on behavioral consistency. In this paper, a partially observable Markov Decision Process (POMDP)-based computational robot-human trust model is proposed for hand-over tasks in human-robot collaborative contexts. The robot's trust in its human partner is evaluated based on the human behavior estimates and object detection during the hand-over task. The human-robot hand-over process is parameterized as a partially observable Markov Decision Process. The proposed approach is verified in real-world human-robot collaborative tasks. Results show that our approach can be successfully applied to human-robot hand-over tasks to achieve high efficiency, reduce redundant robot movements, and realize predictability and mutual understanding of the task.

SQL Injection has been around as a harmful and prolific threat on web applications for more than 20 years, yet it still poses a huge threat to the World Wide Web. Rapidly evolving web technology has not eradicated this threat; In 2017 51 % of web application attacks are SQL injection attacks. Most conventional practices to prevent SQL injection attacks revolves around secure web and database programming and administration techniques. Despite developer ignorance, a large number of online applications remain susceptible to SQL injection attacks. There is a need for a more effective method to detect and prevent SQL Injection attacks. In this research, we offer a unique machine learning-based strategy for identifying potential SQL injection attack (SQL injection attack) threats. Application of the proposed method in a Security Information and Event Management(SIEM) system will be discussed. SIEM can aggregate and normalize event information from multiple sources, and detect malicious events from analysis of these information. The result of this work shows that a machine learning based SQL injection attack detector which uses SIEM approach possess high accuracy in detecting malicious SQL queries.

New advancements in cloud computing technology enable the usage of cloud platforms for business purposes rapidly increasing every day. Data accumulation related to business transactions, Communications, business model architecture and much other information are stored in the cloud platform and access Dubai the business Associates commonly. Considering the security point of view data stored in the cloud need to be highly secured and accessed through authentication. The proposed system is focused on evaluating a cloud integrity auditing model in which the security and privacy preserving system is being audited, privacy is decided using a machine learning algorithm. The proposed model is developed using a hybrid CatBoost algorithm (HCBA) in which the input data is stored into the cloud platform using Bring your own encryption Key (BYOEK). The security of BYOEK model is evaluated and validated with respect to the given test model in terms of Execution time comparison Vs. Data transactions.