Measurement and Metrics Testing - Nowadays, attackers are increasingly using UseAfter-Free(UAF) vulnerabilities to create threats against software security. Existing static approaches for UAF detection are capable of finding potential bugs in the large code base. In most cases, analysts perform manual inspections to verify whether the warnings detected by static analysis are real vulnerabilities. However, due to the complex constraints of constructing UAF vulnerability, it is very time and cost-intensive to screen all warnings. In fact, many warnings should be discarded before the manual inspection phase because they are almost impossible to get triggered in real-world, and it is often overlooked by current static analysis techniques.

Measurement and Metrics Testing - Software testing is one of the most critical and essential processes in the software development life cycle. It is the most significant aspect that affects product quality. Quality and service are critical success factors, particularly in the software business development market. As a result, enterprises must execute software testing and invest resources in it to ensure that their generated software products meet the needs and expectations of end-users. Test prioritization and evaluation are the key factors in determining the success of software testing. Test suit coverage metrics are commonly used to evaluate the testing process. Soft Computing techniques like Genetic Algorithms and Particle Swarm Optimization have gained prominence in various aspects of testing. This paper proposes an automated Genetic Algorithm approach to prioritizing the test cases and the evaluation through code coverage metrics with the Coverlet tool. Coverlet is a.NET code coverage tool that works across platforms and supports line, branch, and method coverage. Coverlet gathers data from Cobertura coverage test runs, which are then utilized to generate reports. Resultant test suits generated were validated and analyzed and have had significant improvement over the generations.

Malware Classification - With the rapid development of technology and the increase in the use of Android software, the number of malware has also increased. This study presents a classification as malware/goodware with the features of 4465 Android applications. Cost is an important problem for the increasing number of applications and the analyzes to be made on each application. This study focused on this problem with the hybrid use of Gray Wolf Optimization Algorithm (GWO) and Deep Neural Networks (DNN). With the use of GWO, both feature selection and the features of the model to be created with DNN are determined. In this way, an approximate solution proposal is presented for the most suitable features and the most suitable model design. The model, which was created with the use of GWO-DNN hybrid in this study, offers an F1 score of 99.74%.

MANET Attack Prevention - Since the mid-1990s, the growth of laptops and Wi-Fi networks has led to a great increase in the use of MANET (Mobile ad hoc network) in wireless communication. MANET is a group of mobile devices for example mobile phones, computers, laptops, radios, sensors, etc., that communicate with each other wirelessly without any support from existing internet infrastructure or any other kind of fixed stations. As MANET is an infrastructure-less network it is prone to various attacks, which can lead to loss of information during communication, security breaches or other unauthentic malpractices. Various types of attacks to which MANET can be vulnerable are denial of service (DOS) and packet dropping attacks such as Gray hole, Blackhole, Wormhole, etc. In this research, we are particularly focusing on the detection and prevention of Gray hole attack. Gray hole node drops selective data packets, while participating in the routing process like other nodes, and advertises itself as a genuine node. The Intrusion Detection System (IDS) technique is used for identification and aversion of the Gray hole attack. Use of AODV routing protocol is made in the network. The network is incorporated and simulation parameters such as PDR (Packet Delivery Ratio), Energy Consumption, End-to-end delay, and Throughput are analyzed using simulation software.

Malware Analysis - The effective security system improvement from malware attacks on the Android operating system should be updated and improved. Effective malware detection increases the level of data security and high protection for the users. Malicious software or malware typically finds a means to circumvent the security procedure, even when the user is unaware whether the application can act as malware. The effectiveness of obfuscated android malware detection is evaluated by collecting static analysis data from a data set. The experiment assesses the risk level of which malware dataset using the hash value of the malware and records malware behavior. A set of hash SHA256 malware samples has been obtained from an internet dataset and will be analyzed using static analysis to record malware behavior and evaluate which risk level of the malware. According to the results, most of the algorithms provide the same total score because of the multiple crime inside the malware application.

Malware Analysis - Any software that runs malicious payloads on victims’ computers is referred to as malware. It is an increasing threat that costs people, businesses, and organizations a lot of money. Attacks on security have developed significantly in recent years. Malware may infiltrate both offline and online media, like: chat, SMS, and spam (email, or social media), because it has a built-in defensive mechanism and may conceal itself from antivirus software or even corrupt it. As a result, there is an urgent need to detect and prevent malware before it damages critical assets around the world. In fact, there are lots of different techniques and tools used to combat versus malware. In this paper, the malware samples were analyzing in the Virtual Box environment using in-depth analysis based on reverse engineering using advanced static malware analysis techniques. The results Obtained from malware analysis which represent a set of valuable information, all anti-malware and anti-virus program companies need for in order to update their products.

Information Reuse and Security - In software engineering, the aspect of addressing security requirements is considered to be of paramount importance. In most cases, however, security requirements for a system are considered as non-functional requirements (NFRs) and are addressed at the very end of the software development life cycle. The increasing number of security incidents in software systems around the world has made researchers and developers rethink and consider this issue at an earlier stage. An important and essential step towards this process is the elicitation of relevant security requirements. In a recent work, Imtiaz et al. proposed a framework for creating a mapping between existing requirements and the vulnerabilities associated with them. The idea is that, this mapping can be used by developers to predict potential vulnerabilities associated with new functional requirements and capture security requirements to avoid these vulnerabilities. However, to what extent, such existing vulnerability information can be useful in security requirements elicitation is still an open question. In this paper, we design a human subject study to answer this question. We also present the results of a pilot study and discuss their implications. Preliminary results show that existing vulnerability information can be a useful resource in eliciting security requirements and lays ground work for a full scale study.

Information Reuse and Security - New malware increasingly adopts novel fileless techniques to evade detection from antivirus programs. Process injection is one of the most popular fileless attack techniques. This technique makes malware more stealthy by writing malicious code into memory space and reusing the name and port of the host process. It is difficult for traditional security software to detect and intercept process injections due to the stealthiness of its behavior. We propose a novel framework called ProcGuard for detecting process injection behaviors. This framework collects sensitive function call information of typical process injection. Then we perform a fine-grained analysis of process injection behavior based on the function call chain characteristics of the program, and we also use the improved RCNN network to enhance API analysis on the tampered memory segments. We combine API analysis with deep learning to determine whether a process injection attack has been executed. We collect a large number of malicious samples with process injection behavior and construct a dataset for evaluating the effectiveness of ProcGuard. The experimental results demonstrate that it achieves an accuracy of 81.58\% with a lower false-positive rate compared to other systems. In addition, we also evaluate the detection time and runtime performance loss metrics of ProcGuard, both of which are improved compared to previous detection tools.

Information Reuse and Security - Evaluating the security gains brought by software diversity is one key issue of software diversity research, but the existing software diversity evaluation methods are generally based on conventional code features and are relatively single, which are difficult to accurately reflect the security gains brought by software diversity. To solve these problems, from the perspective of return-oriented programming (ROP) attack, we present a software diversity evaluation method which integrates metrics for the quality and distribution of gadgets. Based on the proposed evaluation method and SpiderMonkey JavaScript engine, we implement a software diversity evaluation system for compiled languages and script languages. Diversity techniques with different granularities are used to test. The evaluation results show that the proposed evaluation method can accurately and comprehensively reflect the security gains brought by software diversity.

Information Reuse and Security - Common Vulnerabilities and Exposures (CVE) databases contain information about vulnerabilities of software products and source code. If individual elements of CVE descriptions can be extracted and structured, then the data can be used to search and analyze CVE descriptions. Herein we propose a method to label each element in CVE descriptions by applying Named Entity Recognition (NER). For NER, we used BERT, a transformer-based natural language processing model. Using NER with machine learning can label information from CVE descriptions even if there are some distortions in the data. An experiment involving manually prepared label information for 1000 CVE descriptions shows that the labeling accuracy of the proposed method is about 0.81 for precision and about 0.89 for recall. In addition, we devise a way to train the data by dividing it into labels. Our proposed method can be used to label each element automatically from CVE descriptions.

Information Reuse and Security - The experimental results demonstrated that, With the development of cloud computing, more and more people use cloud computing to do all kinds of things. However, for cloud computing, the most important thing is to ensure the stability of user data and improve security at the same time. From an analysis of the experimental results, it can be found that Cloud computing makes extensive use of technical means such as computing virtualization, storage system virtualization and network system virtualization, abstracts the underlying physical facilities into external unified interfaces, maps several virtual networks with different topologies to the underlying infrastructure, and provides differentiated services for external users. By comparing and analyzing the experimental results, it is clear that virtualization technology will be the main way to solve cloud computing security. Virtualization technology introduces a virtual layer between software and hardware, provides an independent running environment for applications, shields the dynamics, distribution and differences of hardware platforms, supports the sharing and reuse of hardware resources, provides each user with an independent and isolated computer environment, and facilitates the efficient and dynamic management and maintenance of software and hardware resources of the whole system. Applying virtualization technology to cloud security reduces the hardware cost and management cost of "cloud security" enterprises to a certain extent, and improves the security of "cloud security" technology to a certain extent. This paper will outline the basic cloud computing security methods, and focus on the analysis of virtualization cloud security technology

Information Reuse and Security - Code-reuse attacks (including ROP/JOP) severely threaten computer security. Control-flow integrity (CFI), which can restrict control flow in legal scope, is recognised as an effective defence mechanism against code-reuse attacks. Hardware-based CFI uses Instruction Set Architecture (ISA) extensions with additional hardware modules to implement CFI and achieve better performance. However, hardware-based fine-grained CFI adds new instructions to the ISA, which can not be executed on old processors and breaks the compatibility of programs. Some coarse-grained CFI designs, such as Intel IBT, maintain the compatibility of programs but can not provide enough security guarantees.To balance the security and compatibility of hardware CFI, we propose Transparent Forward CFI (TFCFI). TFCFI implements hardware-based fine-grained CFI designs without changing the ISA. The software modification of TFCFI utilizes address information and hint instructions in RISC-V as transparent labels to mark the program. The hardware module of TFCFI monitors the control flow during execution. The program modified by TFCFI can be executed on old processors without TFCFI. Benefiting from transparent labels, TFCFI also solves the destination equivalence problem. The experiment on FPGA shows that TFCFI incurs negligible performance overhead (1.82\% on average).

Information Reuse and Security - With the development of software defined network and network function virtualization, network operators can flexibly deploy service function chains (SFC) to provide network security services more than before according to the network security requirements of business systems. At present, most research on verifying the correctness of SFC is based on whether the logical sequence between service functions (SF) in SFC is correct before deployment, and there is less research on verifying the correctness after SFC deployment. Therefore, this paper proposes a method of using Colored Petri Net (CPN) to establish a verification model offline and verify whether each SF deployment in SFC is correct after online deployment. After the SFC deployment is completed, the information is obtained online and input into the established model for verification. The experimental results show that the SFC correctness verification method proposed in this paper can effectively verify whether each SF in the deployed SFC is deployed correctly. In this process, the correctness of SF model is verified by using SF model in the model library, and the model reuse technology is preliminarily discussed.

Information Reuse and Security - At present, code reuse attacks, such as Return Oriented Programming (ROP), execute attacks through the code of the application itself, bypassing the traditional defense mechanism and seriously threatening the security of computer software. The existing two mainstream defense mechanisms, Address Space Layout Randomization (ASLR), are vulnerable to information disclosure attacks, and Control-Flow Integrity (CFI) will bring high overhead to programs. At the same time, due to the widespread use of software of unknown origin, there is no source code provided or available, so it is not always possible to secure the source code. In this paper, we propose FRCFI, an effective method based on binary rewriting to prevent code reuse attacks. FRCFI first disrupts the program s memory space layout through function shuffling and NOP insertion, then verifies the execution of the control-flow branch instruction ret and indirect call/jmp instructions to ensure that the target address is not modified by attackers. Experiment show shows that FRCFI can effectively defend against code reuse attacks. After randomization, the survival rate of gadgets is only 1.7\%, and FRCFI adds on average 6.1\% runtime overhead on SPEC CPU2006 benchmark programs.

Malware Analysis and Graph Theory - This paper provides an in-depth analysis of Android malware that bypassed the strictest defenses of the Google Play application store and penetrated the official Android market between January 2016 and July 2021. We systematically identified 1,238 such malicious applications, grouped them into 134 families, and manually analyzed one application from 105 distinct families. During our manual analysis, we identified malicious payloads the applications execute, conditions guarding execution of the payloads, hiding techniques applications employ to evade detection by the user, and other implementation-level properties relevant for automated malware detection. As most applications in our dataset contain multiple payloads, each triggered via its own complex activation logic, we also contribute a graph-based representation showing activation paths for all application payloads in form of a control- and data-flow graph. Furthermore, we discuss the capabilities of existing malware detection tools, put them in context of the properties observed in the analyzed malware, and identify gaps and future research directions. We believe that our detailed analysis of the recent, evasive malware will be of interest to researchers and practitioners and will help further improve malware detection tools.

Malware Analysis and Graph Theory - With the dramatic increase in malicious software, the sophistication and innovation of malware have increased over the years. In particular, the dynamic analysis based on the deep neural network has shown high accuracy in malware detection. However, most of the existing methods only employ the raw API sequence feature, which cannot accurately reflect the actual behavior of malicious programs in detail. The relationship between API calls is critical for detecting suspicious behavior. Therefore, this paper proposes a malware detection method based on the graph neural network. We first connect the API sequences executed by different processes to build a directed process graph. Then, we apply Bert to encode the API sequences of each process into node embedding, which facilitates the semantic execution information inside the processes. Finally, we employ GCN to mine the deep semantic information based on the directed process graph and node embedding. In addition to presenting the design, we have implemented and evaluated our method on 10,000 malware and 10,000 benign software datasets. The results show that the precision and recall of our detection model reach 97.84\% and 97.83\%, verifying the effectiveness of our proposed method.

Malware Analysis - The effective security system improvement from malware attacks on the Android operating system should be updated and improved. Effective malware detection increases the level of data security and high protection for the users. Malicious software or malware typically finds a means to circumvent the security procedure, even when the user is unaware whether the application can act as malware. The effectiveness of obfuscated android malware detection is evaluated by collecting static analysis data from a data set. The experiment assesses the risk level of which malware dataset using the hash value of the malware and records malware behavior. A set of hash SHA256 malware samples has been obtained from an internet dataset and will be analyzed using static analysis to record malware behavior and evaluate which risk level of the malware. According to the results, most of the algorithms provide the same total score because of the multiple crime inside the malware application.

Malware Analysis - Any software that runs malicious payloads on victims’ computers is referred to as malware. It is an increasing threat that costs people, businesses, and organizations a lot of money. Attacks on security have developed significantly in recent years. Malware may infiltrate both offline and online media, like: chat, SMS, and spam (email, or social media), because it has a built-in defensive mechanism and may conceal itself from antivirus software or even corrupt it. As a result, there is an urgent need to detect and prevent malware before it damages critical assets around the world. In fact, there are lots of different techniques and tools used to combat versus malware. In this paper, the malware samples were analyzing in the Virtual Box environment using in-depth analysis based on reverse engineering using advanced static malware analysis techniques. The results Obtained from malware analysis which represent a set of valuable information, all anti-malware and anti-virus program companies need for in order to update their products.

Machine Learning - A good ecological environment is crucial to attracting talents, cultivating talents, retaining talents and making talents fully effective. This study provides a solution to the current mainstream problem of how to deal with excellent employee turnover in advance, so as to promote the sustainable and harmonious human resources ecological environment of enterprises with a shortage of talents.This study obtains open data sets and conducts data preprocessing, model construction and model optimization, and describes a set of enterprise employee turnover prediction models based on RapidMiner workflow. The data preprocessing is completed with the help of the data statistical analysis software IBM SPSS Statistic and RapidMiner.Statistical charts, scatter plots and boxplots for analysis are generated to realize data visualization analysis. Machine learning, model application, performance vector, and cross-validation through RapidMiner s multiple operators and workflows. Model design algorithms include support vector machines, naive Bayes, decision trees, and neural networks. Comparing the performance parameters of the algorithm model from the four aspects of accuracy, precision, recall and F1-score. It is concluded that the performance of the decision tree algorithm model is the highest. The performance evaluation results confirm the effectiveness of this model in sustainable exploring of enterprise employee turnover prediction in human resource management.

Internet-scale Computing Security - With the rapid growth of the number of global network entities and interconnections, the security risks of network relationships are constantly accumulating. As the basis of network interconnection and communication, Internet routing is facing severe challenges such as insufficient online monitoring capability of large-scale routing events and lack of effective and credible verification mechanism. Major global routing security events emerge one after another, causing extensive and far-reaching impacts. To solve these problems, China Telecom studied the BGP (border gateway protocol) SDN (software defined network) controller technology to monitor the interconnection routing, constructed the global routing information database trust source integrating multi-dimensional information and developed the function of the protocol level based real-time monitoring system of Internet routing security events. Through these means, it realizes the second-level online monitoring capability of large-scale IP network Internet service routing events, forms the minute-level route leakage interception and route hijacking blocking solutions, and achieves intelligent protection capability of Internet routing security.

Internet-scale Computing Security - Wireless Sensor networks can be composed of smart buildings, smart homes, smart grids, and smart mobility, and they can even interconnect all these fields into a large-scale smart city network. Software-Defined Networking is an ideal technology to realize Internet-of-Things (IoT) Network and WSN network requirements and to efficiently enhance the security of these networks. Software defines Networking (SDN) is used to support IoT and WSN related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. This work is a study of different security mechanisms available in SDN for IoT and WSN network secure communication. This work also formulates the problems when existing methods are implemented with different networks parameters.

Internet of Vehicles Security - In construction machinery, connectivity delivers higher advantages in terms of higher productivity, lower costs, and most importantly safer work environment. As the machinery grows more dependent on internet-connected technologies, data security and product cybersecurity become more critical than ever. These machines have more cyber risks compared to other automotive segments since there are more complexities in software, larger after-market options, use more standardized SAE J1939 protocol, and connectivity through long-distance wireless communication channels (LTE interfaces for fleet management systems). Construction machinery also operates throughout the day, which means connected and monitored endlessly. Till today, construction machinery manufacturers are investigating the product cybersecurity challenges in threat monitoring, security testing, and establishing security governance and policies. There are limited security testing methodologies on SAE J1939 CAN protocols. There are several testing frameworks proposed for fuzz testing CAN networks according to [1]. This paper proposes security testing methods (Fuzzing, Pen testing) for in-vehicle communication protocols in construction machinery.

Intelligent Data and Security - Intelligent Systems for Personal Data Cyber Security is a critical component of the Personal Information Management of Medicaid Enterprises. Intelligent Systems for Personal Data Cyber Security combines components of Cyber Security Systems with Human-Computer Interaction. It also uses the technology and principles applied to the Internet of Things. The use of software-hardware concepts and solutions presented in this report is, in the authors’ opinion, some step in the working-out of the Intelligent Systems for Personal Data Cyber Security in Medicaid Enterprises. These concepts may also be useful for developers of these types of systems.

Intellectual Property Security - The rapid improvement of computer and network technology not only promotes the improvement of productivity and facilitates people s life, but also brings new threats to production and life. Cyberspace security has attracted more and more attention. Different from traditional cyberspace security, APT attacks on key networks or infrastructure, with the main goal of stealing intellectual property, confidential information or sabotage, seriously threatening the interests and security of governments, enterprises and scientific research institutions. Timely detection and blocking is particularly important. The purpose of this paper is to study the security of software supply chain in power industry based on BAS technology. The experimental data shows that Type 1 projects account for the least amount and Type 2 projects account for the highest proportion. Type 1 projects have high unit price contracts and high profits, but the number is small and the time for signing orders is long.

Information Centric Networks - This work expands on our prior work on an architecture and supporting protocols to efficiently integrate constrained devices into an Information-Centric Network-based Internet of Things in a way that is both secure and scalable. In this work, we propose a scheme for addressing additional threats and integrating trust-based behavioral observations and attribute-based access control by leveraging the capabilities of less constrained coordinating nodes at the network edge close to IoT devices. These coordinating devices have better insight into the behavior of their constituent devices and access to a trusted overall security management cloud service. We leverage two modules, the security manager (SM) and trust manager (TM). The former provides data confidentiality, integrity, authentication, and authorization, while the latter analyzes the nodes behavior using a trust model factoring in a set of service and network communication attributes. The trust model allows trust to be integrated into the SM s access control policies, allowing access to resources to be restricted to trusted nodes.