Object Oriented Security - Several software vulnerabilities emerge during the design phase of a software development process, which can be addressed using secure design patterns. However, using these patterns over web application vulnerabilities is comparatively more tricky for developers than using traditional design patterns. Although several practices exist for addressing software security vulnerabilities, they are sometimes difficult to reuse due to their implementation-specific nature. In this study, we discuss the secure design patterns that are intended to prevent vulnerabilities from being accidentally introduced into code or reduce the effects of flaws. The patterns are created by combining current best security design practices and adding security-specific functionality to the existing design patterns. Hence, this work outlines a convenient mechanism for deciding which secure design patterns to use for addressing online application vulnerabilities. We have demonstrated the applicability of our concept over a prevalent database security threat, namely SQL injection.

Object Oriented Security - In object-oriented software development, UML has become a de facto modeling standard. However, although UML is easy to understand and apply, it has inaccurate semantics, and UML is a semi-formal modeling language, which cannot be formally verified. Event-B is a formal method based on a large number of mathematical predicate logic, which is precise but difficult to understand and apply. Therefore, how to combine the advantages of UML diagram and Event-B method is the focus of the research. The previous transformation methods are based on the transformation from UML scatter diagram to Event-B, which is prone to conflict and inconsistency. Therefore, we propose a systematic transformation method that can realize the corresponding unification of elements in UML and those in Event-B. The general software system is a medium-sized system. We believe that the medium-sized system can be clearly expressed by using use case diagram, class diagram, state diagram and sequence diagram. In this paper, the transformation methods from these four diagrams to EventB are given respectively. The transformation method of the system is applied to the elevator control system which requires high safety and reliability. The system transformation method from UML to Event-B not only improves the accuracy of UML and is easy for software practitioners to use, but also enhances the comprehensibility of formal methods and is conducive to the promotion and application of formal methods.

Object Oriented Security - For the last 20 years, the number of vulnerabilities has increased near 20 times, according to NIST statistics. Vulnerabilities expose companies to risks that may seriously threaten their operations. Therefore, for a long time, it has been suggested to apply security engineering – the process of accumulating multiple techniques and practices to ensure a sufficient level of security and to prevent vulnerabilities in the early stages of software development, including establishing security requirements and proper security testing. The informal nature of security requirements makes it uneasy to maintain system security, eliminate redundancy and trace requirements down to verification artifacts such as test cases. To deal with this problem, Seamless Object-Oriented Requirements (SOORs) promote incorporating formal requirements representations and verification means together into requirements classes.

Object Oriented Security - A growing number of attacks and the introduction of new security standards, e.g. ISO 21434, are increasingly shifting the focus of industry and research to the cybersecurity of vehicles. Being cyber-physical systems, compromised vehicles can pose a safety risk to occupants and the environment. Updates over the air and monitoring of the vehicle fleet over its entire lifespan are therefore established in current and future vehicles. Elementary components of such a strategy are security sensors in the form of firewalls and intrusion detection systems, for example, and an operations center where monitoring and response activities are coordinated. A critical step in defending against, detecting, and remediating attacks is providing knowledge about the vehicle and fleet context. Whether a vehicle is driving on the highway or parked at home, what software version is installed, or what security incidents have occurred affect the legitimacy of data and network traffic. However, current security measures lack an understanding of how to operate in an adjusted manner in different contexts. This work is therefore dedicated to a concept to make security measures for vehicles context-aware. We present our approach, which consists of an object-oriented model of relevant context information within the vehicle and a Knowledge Graph for the fleet. With this approach, various use cases can be addressed, according to the different requirements for the use of context knowledge in the vehicle and operations center.

Neural Network Security - Software-Defined Network (SDN) is a new networking paradigm that adopts centralized control logic and provides more control to the network operators over the network infrastructure to meet future network requirements. SDN controller known as operation system, which is responsible for running network applications and maintaining the different network services and functionalities. Despite all its great capabilities, SDN is facing different security threats due to its various architectural entities and centralized nature. Distributed Denial of Service (DDoS) is a promptly growing attack and becomes a major threat for the SDN. To date, most of the studies focus on detecting high-rate DDoS attacks at the control layer of SDN and low-rate DDoS attacks are high concealed because they are difficult to detect. Furthermore, the existing methods are useful for the detection of high-rate DDoS, so need to focus on low-rate DDoS attacks separately. Hence, the use of machine learning algorithms is growing for the detection of low-rate DDoS attacks in the SDN, but they achieved low accuracy against this attack. To improve the detection accuracy, this paper first describes the attack s mechanism and then proposes a Recurrent Neural Network (RNN) based method. The extracted features from the flow rules are used by the RNN for the detection of low-rate attacks. The experimental results show that the proposed method intelligently detects the attack, and its detection accuracy reaches 98.59\%. The proposed method achieves good detection accuracy as compared to existing studies.

Network Security Resiliency - The 5G ecosystem is designed as a highly sophisticated and modularized architecture that decouples the radio access network (RAN), the multi-access edge computing (MEC) and the mobile core to enable different and scalable deployments. It leverages modern principles of virtualized network functions, microservices-based service chaining, and cloud-native software stacks. Moreover, it provides built-in security and mechanisms for slicing. Despite all these capabilities, there remain many gaps and opportunities for additional capabilities to support end-toend secure operations for applications across many domains. Although 5G supports mechanisms for network slicing and tunneling, new algorithms and mechanisms that can adapt network slice configurations dynamically to accommodate urgent and mission-critical traffic are needed. Such slices must be secure, interference-aware, and free of side channel attacks. Resilience of the 5G ecosystem itself requires an effective means for observability and (semi-)autonomous self-healing capabilities. To address this plethora of challenges, this paper presents the SECurity and REsiliency TEchniques for Differentiated 5G OPerationS (SECRETED 5G OPS) project, which is investigating fundamental new solutions that center on the zero trust, network slicing, and network augmentation dimensions, which together will achieve secure and differentiated operations in 5G networks. SECRETED 5G OPS solutions are designed to be easily deployable, minimally invasive to the existing infrastructure, not require modifications to user equipment other than possibly firmware upgrades, economically viable, standards compliant, and compliant to regulations.

Network Security Resiliency - The 5G ecosystem is designed as a highly sophisticated and modularized architecture that decouples the radio access network (RAN), the multi-access edge computing (MEC) and the mobile core to enable different and scalable deployments. It leverages modern principles of virtualized network functions, microservices-based service chaining, and cloud-native software stacks. Moreover, it provides built-in security and mechanisms for slicing. Despite all these capabilities, there remain many gaps and opportunities for additional capabilities to support end-toend secure operations for applications across many domains. Although 5G supports mechanisms for network slicing and tunneling, new algorithms and mechanisms that can adapt network slice configurations dynamically to accommodate urgent and mission-critical traffic are needed. Such slices must be secure, interference-aware, and free of side channel attacks. Resilience of the 5G ecosystem itself requires an effective means for observability and (semi-)autonomous self-healing capabilities. To address this plethora of challenges, this paper presents the SECurity and REsiliency TEchniques for Differentiated 5G OPerationS (SECRETED 5G OPS) project, which is investigating fundamental new solutions that center on the zero trust, network slicing, and network augmentation dimensions, which together will achieve secure and differentiated operations in 5G networks. SECRETED 5G OPS solutions are designed to be easily deployable, minimally invasive to the existing infrastructure, not require modifications to user equipment other than possibly firmware upgrades, economically viable, standards compliant, and compliant to regulations.

Network Security Resiliency - Software-Defined Networking (SDN) technique is presented in this paper to manage the Naval Supervisory Control and Data Acquisition (SCADA) network for equipping the network with the function of reconfiguration and scalability. The programmable nature of SDN enables a programmable Modular Topology Generator (MTG), which provides an extensive control over the network’s internal connectivity and traffic control. Specifically, two functions of MTG are developed and examined in this paper, namely linkHosts and linkSwitches. These functions are able to place the network into three different states, i.e., fully connected, fully disconnected, and partially connected. Therefore, it provides extensive security benefits and allows network administrators to dynamically reconfigure the network and adjust settings according to the network’s needs. Extensive tests on Mininet have demonstrated the effectiveness of SDN for enabling the reconfigurable and scalable Naval SCADA network. Therefore, it provides a potent tool to enhance the resiliency/survivability, scalability/compatibility, and security of naval SCADA networks.

Network Security Architecture - As a result of globalization, the COVID-19 pandemic and the migration of data to the cloud, the traditional security measures where an organization relies on a security perimeter and firewalls do not work. There is a shift to a concept whereby resources are not being trusted, and a zero-trust architecture (ZTA) based on a zero-trust principle is needed. Adapting zero trust principles to networks ensures that a single insecure Application Protocol Interface (API) does not become the weakest link comprising of Critical Data, Assets, Application and Services (DAAS). The purpose of this paper is to review the use of zero trust in the security of a network architecture instead of a traditional perimeter. Different software solutions for implementing secure access to applications and services for remote users using zero trust network access (ZTNA) is also summarized. A summary of the author’s research on the qualitative study of “Insecure Application Programming Interface in Zero Trust Networks” is also discussed. The study showed that there is an increased usage of zero trust in securing networks and protecting organizations from malicious cyber-attacks. The research also indicates that APIs are insecure in zero trust environments and most organization are not aware of their presence.

Network Security Architecture - Design a new generation of smart power meter components, build a smart power network, implement power meter safety protection, and complete smart power meter network security protection. The new generation of smart electric energy meters mainly complete legal measurement, safety fee control, communication, control, calculation, monitoring, etc. The smart power utilization structure network consists of the master station server, front-end processor, cryptographic machine and master station to form a master station management system. Through data collection and analysis, the establishment of intelligent energy dispatching operation, provides effective energy-saving policy algorithms and strategies, and realizes energy-smart electricity use manage. The safety protection architecture of the electric energy meter is designed from the aspects of its own safety, full-scenario application safety, and safety management. Own security protection consists of hardware security protection and software security protection. The full-scene application security protection system includes four parts: boundary security, data security, password security, and security monitoring. Security management mainly provides application security management strategies and security responsibility division strategies. The construction of the intelligent electric energy meter network system lays the foundation for network security protection.

Network Security Architecture - Software-Defined Networking or SDN (Software-Defined Networking) is a technology for software control and management of the network in order to improve its properties. Unlike classic network management technologies, which are complex and decentralized, SDN technology is a much more flexible and simple system. The new architecture may be vulnerable to several attacks leading to resource depletion and preventing the SDN controller from providing support to legitimate users. One such attack is the Distributed Denial of Service (DDoS), which is on the rise today. We suggest Modified-DDoSNet, a system for detecting DDoS attacks in the SDN environment. A model based on Deep Learning (DL) techniques will be implemented, combining a Recurrent Neural Network (RNN) with an Autoencoder. The proposed model, which was first trained to detect attacks, was implemented in the security architecture of the SDN network, as a new component. The security architecture of the SDN network contains a total of 13 components, each of which represents an individual part of the architecture, where the first component is the RNN - autoencoder. The model itself, which is the first component, was trained in the CICDDoS2019 dataset. It has high reliability for attack detection, which increases the security of the SDN network architecture.

Network on Chip Security - This paper designs a network security protection system based on artificial intelligence technology from two aspects of hardware and software. The system can simultaneously collect Internet public data and secret-related data inside the unit, and encrypt it through the TCM chip solidified in the hardware to ensure that only designated machines can read secret-related materials. The data edgecloud collaborative acquisition architecture based on chip encryption can realize the cross-network transmission of confidential data. At the same time, this paper proposes an edge-cloud collaborative information security protection method for industrial control systems by combining endaddress hopping and load balancing algorithms. Finally, using WinCC, Unity3D, MySQL and other development environments comprehensively, the feasibility and effectiveness of the system are verified by experiments.

Network Coding - Precise binary code vulnerability detection is a significant research topic in software security. Currently, the majority of software is released in binary form, and the corresponding vulnerability detection approaches for binary code are desired. Existing deep learning-based detection techniques can only detect binary code vulnerabilities but cannot precisely identify the types of vulnerabilities. This paper proposes a Binary code-based Hybrid neural network for Multiclass Vulnerability Detection, dubbed BHMVD. BHMVD generates binary slices according to the control dependence and data dependence of library/API function calls, and then extracts syntax features from binary slices to generate type slices, which can help identify vulnerability types. This paper uses a hybrid neural network of CNN-BLSTM to extract vulnerability features from binary and type slices. The former extracts local features, while the latter extracts global features. Experiment results on 19 types of vulnerabilities show that BHMVD is effective for binary code-based multiclass vulnerability detection, and using a hybrid neural network can improve detection ability.

Network Coding - Software vulnerabilities, caused by unintentional flaws in source codes, are the main root cause of cyberattacks. Source code static analysis has been used extensively to detect the unintentional defects, i.e. vulnerabilities, introduced into the source codes by software developers. In this paper, we propose a deep learning approach to detect vulnerabilities from their LLVM IR representations based on the techniques that have been used in natural language processing. The proposed approach uses a hierarchical process to first identify source codes with vulnerabilities, and then it identifies the lines of codes that contribute to the vulnerability within the detected source codes. This proposed twostep approach reduces the false alarm of detecting vulnerable lines. Our extensive experiment on real-world and synthetic codes collected in NVD and SARD shows high accuracy (about 98\%) in detecting source code vulnerabilities 1.

Named Data Network Security - Design of the English APP security verification framework based on fusion IP-Address-MAC data features is studied in the paper. APP is named the client application, including third-party applications on PCs and mobile terminals, that is, smartphones. At present, Praat has become a software commonly used by researchers in the world of experimental phonetics, linguistics, language investigation, language processing and other related fields. Under this background, our target is selected to be the English AP. For the design of the framework, node forms a corresponding topology table according to the neighbor list detected by itself and the topology information obtained from the received TC message. To deal with the challenge of the high robustness, the IP and MAC data analysis are both considered. Through the data collection, processing and the further fusion, the comprehensive system is implemented. The proposed model is tested under different testing scenarios.

Multiple Fault Diagnosis - In this article, fault detection (FD) method for multiple device open-circuit faults (OCFs) in modified neutral-point- clamped (NPC) inverters has been introduced using Average Current Park Vector (ACPV) algorithm. The proposed FD design circuit is loadindependent and requires only the converter 3- phase output current. The validity of the results has been demonstrated for OCF diagnostics using a 3-level inverter with one faulty switch. This article examines ACPV techniques for diagnosing multiple fault switches on the single-phase leg of 3-step NPC inverter. This article discusses fault tolerance for a single battery or inverter switch during a standard, active level 3 NPC inverter with connected neutral points. The primary goal here is to detect and locate open circuits in inverter switches. As a result, simulations and experiments are used to investigate and validate a FD algorithm based on a current estimator and two fault localization algorithms based on online adaptation of the space vector modulation (S VM) and the pulse pattern injection principle. This technique was efficiently investigated and provides three-stage modified NPC signature table that accounts for all possible instances of fault. The Matlab / S imulink software is used to validate the introduced signature table for the convergence of permanent magnet motors.

Moving Target Defense - As cyberattacks continuously threaten conventional defense techniques, Moving Target Defense (MTD) has emerged as a promising countermeasure to defend a system against them by dynamically changing attack surfaces of the system. MTD provides the system a state-of-art security mechanism that increases the attack cost or complexity of the system aiming for reducing vulnerabilities exposed to potential attackers. However, the notion of the proactive and dynamic systems adopting MTD services causes a substantial trade-off between system performance and security effectiveness, compared to conventional defense strategies. The MTD tactics accordingly result in performance degradation (e.g., interruptions of service availability) as one of the drawbacks caused by continuous mutations of the system configuration. Therefore, it is crucial to validate not only the security benefits against system threats but also quality-of-service (QoS) for clients when an MTDenabled system proactively continues to mutate attack surfaces. This paper contributes to (i) developing new security metrics; (ii) measuring both the performance degradation and security effectiveness against potential real attacks (i.e., scanning, HTTP flood, dictionary, and SQL injection attack); and (iii) comparing the proposed job management strategies (i.e., drop and switchover) from a performance and security perspective in a physical SDN testbed.

Moving Target Defense - The use of traditional defense mechanisms or intrusion detection systems presents a disadvantage for defenders against attackers since these mechanisms are essentially reactive. Moving target defense (MTD) has emerged as a proactive defense mechanism to reduce this disadvantage by randomly and continuously changing the attack surface of a system to confuse attackers. Although significant progress has been made recently in analyzing the security effectiveness of MTD mechanisms, critical gaps still exist, especially in maximizing security levels and estimating network reconfiguration speed for given attack power. In this paper, we propose a set of Petri Net models and use them to perform a comprehensive evaluation regarding key security metrics of Software-Defined Network (SDNs) based systems adopting a time-based MTD mechanism. We evaluate two use-case scenarios considering two different types of attacks to demonstrate the feasibility and applicability of our models. Our analyses showed that a time-based MTD mechanism could reduce the attackers’ speed by at least 78\% compared to a system without MTD. Also, in the best-case scenario, it can reduce the attack success probability by about ten times.

Multifactor Authentication - The article describes the development and integrated implementation of software modules of photo and video identification system, the system of user voice recognition by 12 parameters, neural network weights, Euclidean distance comparison of real numbers of arrays. The user s biometric data is encrypted and stored in the target folder. Based on the generated data set was developed and proposed a method for synthesizing the parameters of the mathematical model of convolutional neural network represented in the form of an array of real numbers, which are unique identifiers of the user of a personal computer. The training of the training model of multifactor authentication is implemented using categorical cross-entropy. The training sample is generated by adding distorted images by changing the receptive fields of the convolutional neural network. The authors have studied and applied features of simulation modeling of user authorization systems. The main goal of the study is to provide the necessary level of security of user accounts of personal devices. The task of this study is the software implementation of the synthesis of the mathematical model and the training neural network, necessary to provide the maximum level of protection of the user operating system of the device. The result of the research is the developed mathematical model of the software complex of multifactor authentication using biometric technologies, available for users of personal computers and automated workplaces of enterprises.

Multicore Computing Security - Physical memories or RAMs are essential components in a computer system to hold temporary information required for both software and hardware to work properly. When a system’s security is compromised (e.g., due to a malicious application), sensitive information being held in the memories can be leaked out for example to “the cloud”. The RISC-V privileged architecture standard adopts a method called Physical Memory Protection (PMP) to segregate a system’s memory into regions with different policy and permissions to prevent unprivileged software from accessing unauthorized regions. However, PMP does not prevent malicious software from hijacking an Input/Output (IO) device with Direct Memory Access (DMA) capability to indirectly gain unauthorized accesses and hence, a similar method commonly termed as “IOPMP” is being worked on in the RISC-V community. This paper describes an early implementation of IOPMP and how it is used to protect physical memory regions in a RISC-V system. Then, the potential performance impact of IOPMP is briefly elaborated. There are still work to be done and this early IOPMP implementation allows various aspects of the protection method such as its scalability, practicality, and effectiveness etc. to be studied for future enhancement.

Middleware Security - Online advertisements are a significant element of the Internet ecosystem. Businesses monitor their customers via pushing advertising (Ads). Within minutes, cybercriminals try to defraud and steal data through advertisements. Therefore, the issue of ads must be solved. Ads are obtrusive, a security risk, and they hinder performance and efficiency. Hence, the goal is to create an ad-blocker that would operate across the entire network and prevent advertisement on any website s web pages. To put it another way, it s a little computer with such a SoC (System - On - chip) also referred to as a Raspberry Pi that is merged with a networking system, for which we need to retrain the advertisements. On the home network, software named Pi Hole is used to block websites with advertisements. Any network traffic that passes via devices connected to the home network now passes through the Pi. As a result, the adverts are finally checked out during the Raspberry Pi before they reach the users machine and they will be blocked.

Middleware Security - Cybersecurity of power hardware is becoming increasingly critical with the emergence of smart and connected devices such as Grid-connected inverters, EVs and their chargers, microgrid controllers, energy storage / energy management controllers, and smart appliances. Cyber-attacks on power hardware have had far-reaching and widespread impacts. For such cyber-physical systems, security must be ensured at all levels in the design - hardware, firmware, software and interfaces. Although previous approaches to cybersecurity have focused mainly on vulnerabilities in the firmware middleware, or software, vulnerabilities in the hardware itself are hard to identify and harder to mitigate, especially when most hardware components are proprietary and not examinable. This paper presents one approach to mitigate this conundrum - a completely open-source implementation of a microcontroller core along with the associated peripherals based on the well-known RISC-V instruction set architecture (ISA). The proof-of-concept architecture presented here uses the “Shakti” E-Class microcontroller core integrated with a fully custom PWM controller implemented in Verilog, and validated on a Xilinx Artix FPGA. For critical applications such designs may be replicated as a custom ASIC thereby guaranteeing total security of the computing hardware.

Metadata Discovery Problem - Collaborative software development platforms like GitHub have gained tremendous popularity. Unfortunately, many users have reportedly leaked authentication secrets (e.g., textual passwords and API keys) in public Git repositories and caused security incidents and finical loss. Recently, several tools were built to investigate the secret leakage in GitHub. However, these tools could only discover and scan a limited portion of files in GitHub due to platform API restrictions and bandwidth limitations. In this paper, we present SecretHunter, a real-time large-scale comprehensive secret scanner for GitHub. SecretHunter resolves the file discovery and retrieval difficulty via two major improvements to the Git cloning process. Firstly, our system will retrieve file metadata from repositories before cloning file contents. The early metadata access can help identify newly committed files and enable many bandwidth optimizations such as filename filtering and object deduplication. Secondly, SecretHunter adopts a reinforcement learning model to analyze file contents being downloaded and infer whether the file is sensitive. If not, the download process can be aborted to conserve bandwidth. We conduct a one-month empirical study to evaluate SecretHunter. Our results show that SecretHunter discovers 57\% more leaked secrets than state-of-the-art tools. SecretHunter also reduces 85\% bandwidth consumption in the object retrieval process and can be used in low-bandwidth settings (e.g., 4G connections).

Measurement and Metrics Testing - In software regression testing, newly added test cases are more likely to fail, and therefore, should be prioritized for execution. In software regression testing for continuous integration, reinforcement learning-based approaches are promising and the RETECS (Reinforced Test Case Prioritization and Selection) framework is a successful application case. RETECS uses an agent composed of a neural network to predict the priority of test cases, and the agent needs to learn from historical information to make improvements. However, the newly added test cases have no historical execution information, thus using RETECS to predict their priority is more like ‘random’. In this paper, we focus on new test cases for continuous integration testing, and on the basis of the RETECS framework, we first propose a priority assignment method for new test cases to ensure that they can be executed first. Secondly, continuous integration is a fast iterative integration method where new test cases have strong fault detection capability within the latest periods. Therefore, we further propose an additional reward method for new test cases. Finally, based on the full lifecycle management, the ‘new’ additional rewards need to be terminated within a certain period, and this paper implements an empirical study. We conducted 30 iterations of the experiment on 12 datasets and our best results were 19.24\%, 10.67\%, and 34.05 positions better compared to the best parameter combination in RETECS for the NAPFD (Normalized Average Percentage of Faults Detected), RECALL and TTF (Test to Fail) metrics, respectively.

Measurement and Metrics Testing - Fuzz testing is an indispensable test-generation tool in software security. Fuzz testing uses automated directed randomness to explore a variety of execution paths in software, trying to expose defects such as buffer overflows. Since cyber-physical systems (CPS) are often safety-critical, testing models of CPS can also expose faults. However, while existing coverage-guided fuzz testing methods are effective for software, results can be disappointing when applied to CPS, where systems have continuous states and inputs are applied at different points in time.