Vulnerability Detection 2022 - With the booming development of deep learning and machine learning, the use of neural networks for software source code security vulnerability detection has become a hot pot in the field of software security. As a data structure, graphs can adequately represent the complex syntactic information, semantic information, and dependencies in software source code. In this paper, we propose the MPGVD model based on the idea of text classification in natural language processing. The model uses BERT for source code pre-training, transforms graphs into corresponding feature vectors, uses MPNN (Message Passing Neural Networks) based on graph neural networks in the feature extraction phase, and finally outputs the detection results. Our proposed MPGVD, compared with other existing vulnerability detection models on the same dataset CodeXGLUE, obtain the highest detection accuracy of 64.34\%.

Vulnerability Detection 2022 - The increasing number of software vulnerabilities pose serious security attacks and lead to system compromise, information leakage or denial of service. It is a challenge to further improve the vulnerability detection technique. Nowadays most applications are implemented using C/C++. In this paper we focus on the detection of overflow vulnerabilities in C/C++ source code. A novel scheme named VulMiningBGS (Vulnerability Mining Based on Graph Similarity) is proposed. We convert the source code into Top N-Weighted Range Sum Feature Graph (TN-WRSFG), and graph similarity comparisons based on source code level can be effectively carried on to detect possible vulnerabilities. Three categories of vulnerabilities in the Juliet test suite are used, i.e., CWE121, CWE122 and CWE190, with four indicators for performance evaluation (precision, recall, accuracy and F1\_score). Experimental results show that our scheme outperforms the traditional methods, and is effective in the overflow vulnerability detection for C/C++ source code.

Vulnerability Detection 2022 - The increasing number of security vulnerabilities has become an important problem that needs to be solved urgently in the field of software security, which means that the current vulnerability mining technology still has great potential for development. However, most of the existing AI-based vulnerability detection methods focus on designing different AI models to improve the accuracy of vulnerability detection, ignoring the fundamental problems of data-driven AI-based algorithms: first, there is a lack of sufficient high-quality vulnerability data; second, there is no unified standardized construction method to meet the standardized evaluation of different vulnerability detection models. This all greatly limits security personnel’s in-depth research on vulnerabilities. In this survey, we review the current literature on building high-quality vulnerability datasets, aiming to investigate how state-of-the-art research has leveraged data mining and data processing techniques to generate vulnerability datasets to facilitate vulnerability discovery. We also identify the challenges of this new field and share our views on potential research directions.

Visible Light Security 2022 - The world moves towards innovation; internet and mobile users are rising tremendously, and there is a desire for high-speed and uninterrupted internet access. Because of its high speed, improved bandwidth, and security, everyone is now interested in a new emergent wireless communication technology called Visible Light Communication (VLC). A VLC system with and without noise has been developed and modelled using an optical source of 450 nm LED wavelength and photodiode as a receiver. For noise, white light source is used that has an impact on the performance and quality of the VLC system. At the receiver side, Trapezoidal Optical Filter is employed before the photo detector to reduce ambient light noise, enhance the Q-factor, Bit Error Rate and provides a clear eye diagram. This paper also discusses the effect of Bit Rate with LED Bandwidth and Q-factor. Optisystem-7 software is used to simulate the circuits. In this work, the performance is assessed using Bit Error Rate and Q-factor values, as well as an eye diagram for improved communication and the use of a rectangular optical filter and polarizer to separate the sequences at the receiver side when they are sharing the same channel at the same time.

Science of Security 2022 - Security is a critical aspect in the process of designing, developing, and testing software systems. Due to the increasing need for security-related skills within software systems, there is a growing demand for these skills to be taught in computer science. A series of security modules was developed not only to meet the demand but also to assess the impact of these modules on teaching critical cyber security topics in computer science courses. This full paper in the innovative practice category presents the outcomes of six security modules in a freshman-level course at two institutions. The study adopts a Model-Eliciting Activity (MEA) as a project for students to demonstrate an understanding of the security concepts. Two experimental studies were conducted: 1) Teaching effectiveness of implementing cyber security modules and MEA project, 2) Students’ experiences in conceptual modeling tasks in problem-solving. In measuring the effectiveness of teaching security concepts with the MEA project, students’ performance, attitudes, and interests as well as the instructor’s effectiveness were assessed. For the conceptual modeling tasks in problem-solving, the results of student outcomes were analyzed. After implementing the security modules with the MEA project, students showed a great understanding of cyber security concepts and an increased interest in broader computer science concepts. The instructor’s beliefs about teaching, learning, and assessment shifted from teacher-centered to student-centered during their experience with the security modules and MEA project. Although 64.29\% of students’ solutions do not seem suitable for real-world implementation, 76.9\% of the developed solutions showed a sufficient degree of creativity.

Science of Security 2022 - To improve the quality of network security service, the physical device service mode in traditional security service is improved, and the NFV network security service system is constructed by combining software defined networking (SDN) and network function virtualization technology (NFV). Where, network service is provided in the form of security service chain, and Web security scan service is taken as the task, finally the implementation and verification of the system are carried out. The test result shows that the security service system based on NFV can balance the load between the security network service devices in the Web security scan, which proves that the network security system based on software defined security and NFV technology can meet certain service requirements, and lays the research foundation for the improvement of the subsequent user network security service.

Quantum Computing Security 2022 - With the continuous development of Internet of Things (IoT) technology, information and communication technology is also progressing rapidly, among which quantum computer secrecy communication scheme is a new type of cryptographic lock system. It uses both traditional security software encryption algorithms and classical cryptographic systems to achieve a series of operations such as secret storage, transmission and restoration of data. This paper introduces the principle of quantum key distribution and its applications from the physical level; then analyses its security problems and the corresponding research status and proposes improvement methods and measures; finally, with the goal of "bit-based computing information security", a new type of secure communication scheme is designed.

QR Codes 2022 - One of many challenges created by COVID-19 pandemic is to reduce need of contact. Quick Response (QR) codes offered a readily available solution to this challenge with offer to support contact less processes. Wide adaption of smart mobile devices like smart phones and tablets and huge number of mobile applications available in the respective application stores, which support QR code scanning acted as a catalyst in rapid adaption of QR codes to support contact less processes. Support of QR code-based processing rapidly increased during the pandemic, penetrated all processes like sales and marketing, authentication, and digital payments to name some. On one hand, this served the cause in terms of reducing contact, on other hand, factors like rapid adaption and using it in smart mobile devices, which are existing to cater to the larger purpose of human usage, scanning QR codes was not in that list to start with is bringing in the series of security issues which can arise starting from the human factor, software, misuse and hacking factors. This paper focuses on the QR code processes, differences in terms of security while using a smart device for QR codes when compared to the rugged devicebased barcode scanners, the kind of security issues such process can encounter while using smart devises for QR code scanning, factors that must be considered by the applications development as well as the consumers of such functionality and the way to ensure security of consumers of such functionality.

Provable Security - Spectre vulnerabilities violate our fundamental assumptions about architectural abstractions, allowing attackers to steal sensitive data despite previously state-of-the-art countermeasures. To defend against Spectre, developers of verification tools and compiler-based mitigations are forced to reason about microarchitectural details such as speculative execution. In order to aid developers with these attacks in a principled way, the research community has sought formal foundations for speculative execution upon which to rebuild provable security guarantees.

Predictive Security Metrics - With the emergence of Zero Trust (ZT) Architecture, industry leaders have been drawn to the technology because of its potential to handle a high level of security threats. The Zero Trust Architecture (ZTA) is paving the path for a security industrial revolution by eliminating location-based implicant access and focusing on asset, user, and resource security. Software Defined Perimeter (SDP) is a secure overlay network technology that can be used to implement a Zero Trust framework. SDP is a next-generation network technology that allows network architecture to be hidden from the outside world. It also hides the overlay communication from the underlay network by employing encrypted communications. With encrypted information, detecting abnormal behavior of entities on an overlay network becomes exceedingly difficult. Therefore, an automated system is required. We proposed a method in this paper for understanding the normal behavior of deployed polices by mapping network usage behavior to the policy. An Apache Spark collects and processes the streaming overlay monitoring data generated by the built-in fabric API in order to do this mapping. It sends extracted metrics to Prometheus for storage, and then uses the data for machine learning training and prediction. The cluster-id of the link that it belongs to is predicted by the model, and the cluster-ids are mapped onto the policies. To validate the legitimacy of policy, the labeled polices hash is compared to the actual polices hash that is obtained from blockchain. Unverified policies are notified to the SDP controller for additional action, such as defining new policy behavior or marking uncertain policies.

Predictive Security Metrics - Predicting vulnerabilities through source code analysis and using it to guide software maintenance can effectively improve software security. One effective way to predict vulnerabilities is by analyzing library references and function calls used in code. In this paper, we extract library references and function calls from project files through source code analysis, generate sample sets for statistical learning based on these data. Design and train an integrated learning model that can be used for prediction. The designed model has a high accuracy rate and accomplishes the prediction task well. It also proves the correlation between vulnerabilities and library references and function calls.

Predictive Security Metrics - A threat source that might exploit or create a hole in an information system, system security procedures, internal controls, or implementation is a computer operating system vulnerability. Since information security is a problem for everyone, predicting it is crucial. The typical method of vulnerability prediction involves manually identifying traits that might be related to unsafe code. An open framework for defining the characteristics and seriousness of software problems is called the Common Vulnerability Scoring System (CVSS). Base, Temporal, and Environmental are the three metric categories in CVSS. In this research, neural networks are utilized to build a predictive model of Windows 10 vulnerabilities using the published vulnerability data in the National Vulnerability Database. Different variants of neural networks are used which implements the back propagation for training the operating system vulnerabilities scores ranging from 0 to 10. Additionally, the research identifies the influential factors using Loess variable importance in neural networks, which shows that access complexity and polarity are only marginally important for predicting operating system vulnerabilities, while confidentiality impact, integrity impact, and availability impact are highly important.

Predictive Security Metrics - Software developers mostly focus on functioning code while developing their software paying little attention to the software security issues. Now a days, security is getting priority not only during the development phase, but also during other phases of software development life cycle (starting from requirement specification till maintenance phase). To that end, research have been expanded towards dealing with security issues in various phases. Current research mostly focused on developing different prediction models and most of them are based on software metrics. The metrics based models showed higher precision but poor recall rate in prediction. Moreover, they did not analyze the roles of individual software metric on the occurrences of vulnerabilities separately. In this paper, we target to track the evolution of metrics within the life-cycle of a vulnerability starting from its born version through the last affected version till fixed version. In particular, we studied a total of 250 files from three major releases of Apache Tomcat (8, 9 , and 10). We found that four metrics: AvgCyclomatic, AvgCyclomaticStrict, CountDeclM ethod, and CountLineCodeExe show significant changes over the vulnerability history of Tomcat. In addition, we discovered that Tomcat team prioritizes in fixing threatening vulnerabilities such as Denial of Service than less severe vulnerabilities. The results of our research will potentially motivate further research on building more accurate vulnerability prediction models based on the appropriate software metrics. It will also help to assess developer’s mindset about fixing different types of vulnerabilities in open source projects.

Predictive Security Metrics - Security metrics for software products give a quantifiable assessment of a software system s trustworthiness. Metrics can also help detect vulnerabilities in systems, prioritize corrective actions, and raise the level of information security within the business. There is a lack of studies that identify measurements, metrics, and internal design properties used to assess software security. Therefore, this paper aims to survey security measurements used to assess and predict security vulnerabilities. We identified the internal design properties that were used to measure software security based on the internal structure of the software. We also identified the security metrics used in the studies we examined. We discussed how software refactoring had been used to improve software security. We observed that a software system with low coupling, low complexity, and high cohesion is more secure and vice versa. Current research directions have been identified and discussed.

Outsourced Database Security - The outsourced data inside the data dispersion middle server are calm and unsecure when compared with the current methods and security measures. Lost in Client get to benefits control tends to unsecure data sharing inside the stockroom. Existing Login affirmation is executed by utilizing extraordinary username and mystery word as substance organize. But this system faces colossal challenges from software engineers; organize interlopers or irregular works out where people can get the user’s mystery word easily by a number of hacking techniques. In this way, this paper proposes the system for multilevel secured login confirmation system utilizing OTP, picture hotspot security and capture methodologies. The building for picture hot spot is utilized to avoid the unauthorized client looking over the system and it as well avoid from hacking the watchword and unusual works out inside the stockroom So that we propose a Methodology based on guidelines such as Multilevel secured confirmation system to secure from harmful clients Secured Client control benefits for data scrutinized and sort in and Taking after the client conduct plan based on development log and Within the occasion that any unordinary activity is done by the individuals who are getting to data stockroom, the admin will be educated and this irregular development will be captured by keeping up a log record of all the clients. Cutting edge shows up has been proposed utilizing four level security techniques by checking the Picture Hotspot Security. AES Calculation is utilized to scramble and translate the login inconspicuous components in database for more information security to administer information proprietorship and security. For blended information capacity in information stockroom framework utilizing progressed record security and Information advantage Official.

Operating Systems Security - Design of the high-confidence embedded operating system based on artificial intelligence and smart chips is studied in this paper. The cooperative physical layer security system is regarded as a state machine. Relay nodes with untrusted behavior will affect the physical layer security of the system, and the system tries to prevent the untrusted behavior of relay nodes. While implementing public verification, it realizes the protection of data privacy. The third party can directly verify the data holding of the data stored in the cloud without verification by the user, and in the process of system expansion and growth, software can ensure vigorous vitality. For the verification, the smart chips are combined for the systematic implementations. The experimental results have shown the satisfactory results.

Operating Systems Security - Drive Backup is an application for backing up data, including creating copies of partitions for quick recovery in case of an accident, virus attack or, if necessary, replacing all data, including the operating system and installed ones. Software, plus a new hard drive. Reinstalling the operating system and applications after a hardware failure or virus attack does not take you much time and effort. The best way to protect your computer is to create a backup of the system partition with the operating system installed on it and all the necessary applications. In this paper, The commercial hard disk backup system for quick recovery operating system in cloud storage system. Copies can be made to hard drives and removable media as well as network-connected drives. If you need a disk management program, check out the corporate version of this package. A multicast function for transferring copies of an image to multiple computers at the same time, well suited to the needs of corporate offices (for example, to create or restore multiple workstations). But for home backup, you may need to think about other programs - simpler and faster.

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.