Software Security Research Articles

Strategy Indicators for Secure Software Development Lifecycle in Software Startups Based on Information Security Governance

The development of startup software is massive because many services have turned digital. Various applications have emerged that match their functions by taking advantage of the momentum of digital transformation. For example, applications for the Education sector, industrial sector, and economic sector. With their top priority being generating applications, the security factor becomes less of a concern. This is evidenced by the many data breaches that have caused losses to serious companies and public institutions. This is where the role of top-level management emerges in restructuring the company’s strategy, especially software startups, to support producing more secure applications. In this study, the RACI matrix is used to determine who is suitable for implementing a security strategy in information security governance within the organizational structure of the software startup. Where this study starts from stakeholder identification, top-level management roles in software startup, and activities in the secure software development lifecycle. And the results of this study are strategy indicators that are suitable for software startup.

A critical review of the state of computer security in the health sector

There is growing concern about IT security in the healthcare sector due to the number of cyberattacks. The objective of the review is to analyze the state of adoption of computer security in the healthcare sector and provide valuable knowledge to researchers and health organizations interested in this field of study. An exhaustive search of international and regional articles on computer security in healthcare organizations was conducted using Scopus, Dimensions, and pubMed databases. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement was used for the selection of articles published between 2018 and 2022. The final number of articles considered is 50. The review explored approaches related to computer security types, mechanisms, and technologies. The findings reveal that blockchain is the most widely used technology to protect medical information. In addition, network, software, and hardware security approaches are employed, using mechanisms such as data encryption, authentication, and access control. Based on these findings, a perimeter security model for the protection of medical information is proposed. In conclusion, these results highlight the importance of adopting robust security measures in terms of networks, software, and hardware, as well as adopting blockchain technology to improve data security in the healthcare sector.

Research on Computer Software Security Testing Techniques and Applications

Computer software security testing is a critical step in ensuring the security of computer systems. This research focuses on the techniques and applications of computer software security testing. Firstly, an analysis is conducted on three common testing methods: black-box testing, white-box testing, and grey-box testing. Next, the analysis and evaluation of security testing technology based on vulnerability mining, static analysis, and dynamic analysis are discussed. Lastly, the application practice of computer software security testing technology in web applications and mobile applications is explored. This research provides insight and guidance for computer software security testing.

Detecting Cryptography Misuses With Machine Learning: Graph Embeddings, Transfer Learning and Data Augmentation in Source Code Related Tasks

Cryptography is a ubiquitous tool in secure software development in order to guarantee security requirements in general. However, software developers have scarce knowledge about cryptography and rely on limited support tools that cannot properly detect bad uses of cryptography, thus generating vulnerabilities in software. In this work, we extend the scarcely use of machine learning to detect cryptography misuse in source code by using a state of the art deep learning model (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">code2vec</i> ) through transfer learning to generate features that feed machine learning models. In addition, we compare this approach to previous ones in different types of binary models. Also, we adapt code obfuscation to serve as data augmentation in machine learning source code related tasks. Finally, we show that through transfer learning <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">code2vec</i> can be a competitive feature generator for cryptography misuse detection and simple code obfuscation can be used to generate data to enhance machine learning models training in source code related tasks.

Development of Voice and Face Recognition Based Security Software for Biometric Systems

Nowadays, technology continues to make our life easier in every field. It is not possible to provide a high level of security access in security systems without the use of technology. Facial recognition and voice recognition technologies are encountered in many applications in every area of our lives. In this study, a sample application was developed by using a combination of face recognition and voice recognition technologies to create a security system. The Speech.dll library was used to control voice commands and the EmguCV libraries for face recognition. The application was developed in C # programming language. In this application, a security system with a very controlled structure was developed due to the importance of security systems. With the developed application, any high-security operation can be carried out anywhere with computer control. In the developed application, a method with two controls was proposed to provide security control of any system.

Towards a successful secure software acquisition

Towards a successful secure software acquisition

Mitigating risks of online engagement; considering the contexts and mechanisms of cybercrime among older adults

AbstractBackgroundThis talk will explore how benefits of remote interventions can be realised for older people with cognitive impairments alongside risk mitigation, drawing on a realist review of how and in what circumstances older people become victims of cybercrime. We aimed to identify factors leading to older adults' victimisation, and review theory and evidence for interventions to reduce victimisation risks.MethodWe conducted a realist review synthesising published literature and gathering stakeholder experiences via an online workshop to understand how, why and in what circumstances older adults are at risk of financial cybercrime victimisation. We used the evidence to develop a programme theory and Context, Mechanism, Outcome Configurations (CMOCs) to explain the mechanisms by which older adults become victims of financial cybercrime in different contexts, and to inform the development of interventions to produce desired outcomes of reducing and preventing cybercrime.ResultWe analysed 52 primary and secondary data sources and consulted with 21 stakeholders from across health, social, legal, and financial sectors. Our programme theory comprised 16 CMOCs describing how social isolation, cognitive decline, physical and mental health problems; wealth status, limited cyber security skills or awareness, societal attitudes and content of scams led to increased risk of victimisation. Only evidence for interventions to enhance cyber security awareness and skills of potential older victims was identified. Targeting offender motivation, redesign of security software to increase usability by older people and training programmes for professionals and family members are rational, yet untested interventions. A shift in societal attitudes, conveying positive messages, of older people’s rights to be safely active online, is needed too.ConclusionProtection from cybercrime is instrumental to supporting ageing in a digital world. Incorporating cyber security awareness into future interventions that promote social connectedness, cognition and healthy lifestyles, alongside legal structural protections, and measures to improve societal attitudes towards financial cybercrime victimisation will help older adults navigate online spaces safely. Training programmes targeting older adults, or their supporters should be deliverable by a range of professionals for greatest reach; and be co‐designed with end users to ensure that if they are useful, they are acceptable and used.

Ransomware Attacks in the Healthcare Industry

This paper explores the growing problem of ransomware attacks in the healthcare industry. It dives deep into the meaning of ransomware attacks and the basics of the attack such as how hackers gain access to data through the ransomware. Furthermore, this paper specifically focuses on hospitals and patients that are directly affected by the impact of ransomware attacks. This writing also includes information about noteworthy attacks in the health sector that have caught the attention of people around the world and how they were addressed. An aspect of Bitcoin has been added as cyber criminals demand ransom money in this currency from their victims. Lastly, this paper mentions steps that can be taken by hospitals to reduce the risk of getting infected by ransomware or what to do during a ransomware attack, such as having preventative safety measures in place with the use of antivirus software and Artificial Intelligence security systems as well has having a response plan during an attack. Apart from the Information Technology team in hospitals practicing these preventative measures, businesses and individuals should put in the time to implement these precautionary steps to ensure that they aren’t victims of ransomware attacks and that no one can gain access to any of their classified information. This paper was written by the researcher in a limited amount of time and the researcher was not able to conduct any of their own trials. Hence, all the information and data included has been previously collected from researchers around the world.

Bypassing Heaven’s Gate Technique Using Black-Box Testing

In recent years, the number and sophistication of malware attacks on computer systems have increased significantly. One technique employed by malware authors to evade detection and analysis, known as Heaven’s Gate, enables 64-bit code to run within a 32-bit process. Heaven’s Gate exploits a feature in the operating system that allows the transition from a 32-bit mode to a 64-bit mode during execution, enabling the malware to evade detection by security software designed to monitor only 32-bit processes. Heaven’s Gate poses significant challenges for existing security tools, including dynamic binary instrumentation (DBI) tools, widely used for program analysis, unpacking, and de-virtualization. In this paper, we provide a comprehensive analysis of the Heaven’s Gate technique. We also propose a novel approach to bypass the Heaven’s Gate technique using black-box testing. Our experimental results show that the proposed approach effectively bypasses and prevents the Heaven’s Gate technique and strengthens the capabilities of DBI tools in combating advanced malware threats.

THREAT PREVENTION &amp; VULNERABILITY ANALYSIS OF SMART CONTRACTS IN BLOCKCHAIN NETWORKS

Blockchain technology now relies heavily on smart contracts, which offer self-executing code for the exchange of goods and data. Smart contracts enable financial activities like payments and auctions on blockchain systems. Even if bugs are found, they cannot be changed. They are frequently used to automate the implementation of agreements so that all parties may be confident in the conclusion without the need for an intermediary. But because of their openness and how they interact with one another in the blockchain ecosystem, these contracts are vulnerable to security risks. Smart contract security research has advanced significantly in recent years. The research community of smart contract security has made a significant improvement recently. Researchers have identified several security flaws in smart contracts and have created frameworks for verification and static analysis to find them. This study attempts to identify the most critical software security issues that affect smart contracts. These are then handled utilizing a variety of methods and equipment common to the sector

APT Attack Detection Based on Graph Convolutional Neural Networks

Advanced persistent threat (APT) attacks are malicious and targeted forms of cyberattacks that pose significant challenges to the information security of governments and enterprises. Traditional detection methods struggle to extract long-term relationships within these attacks effectively. This paper proposes an APT attack detection model based on graph convolutional neural networks (GCNs) to address this issue. The aim is to detect known attacks based on vulnerabilities and attack contexts. We extract organization-vulnerability relationships from publicly available APT threat intelligence, along with the names and relationships of software security entities from CVE, CWE, and CAPEC, to generate triple data and construct a knowledge graph of APT attack behaviors. This knowledge graph is transformed into a homogeneous graph, and GCNs are employed to process graph features, enabling effective APT attack detection. We evaluate the proposed method on the dataset constructed in this paper. The results show that the detection accuracy of the GCN method reaches 95.9%, improving by approximately 2.1% compared to the GraphSage method. This approach proves to be effective in real-world APT attack detection scenarios.

A survey of strategy-driven evasion methods for PE malware: Transformation, concealment, and attack

The continuous proliferation of malware poses a formidable threat to the cyberspace landscape. Researchers have proffered a multitude of sophisticated defense mechanisms aimed at its detection and mitigation. Nevertheless, malware writers persistently pursue pioneering and innovative methods to evade detection by security software, thereby presenting an ever-evolving and dynamic threat to computer systems. Malware evasion refers to the use of certain strategies by malware to evade the detection of security software. Despite numerous surveys on malware evasion techniques, the existing surveys were fragmented and focused on specific types of evasion methods, leading to a lack of systematic and comprehensive research on malware evasion approaches. To fill this gap, this paper proposed a strategy-driven framework from the perspective of malware writers. Based on this framework, we categorize existing evasion detection techniques into transformation (alter the structural and behavioral pattern of the malware), concealment (conceal the behavior of the malware), and attack-based (engage in an attack on the detector to render it inoperable) methods and conduct a comprehensive survey of the relevant research works. In addition, we demonstrate how to integrate existing evasion strategies in the process of generating malware from the perspective of malware writers to subvert the multiple defenses of defenders. Our investigation indicates that: 1) evasion techniques such as packer and code obfuscation remain the foremost selection for attackers, no fewer than 10 off-the-shelf tools provide great assistance to them, 2) environment analysis is the primary concealment-based strategy used by the attacker (48% of the reviewed concealment-based strategy), defenders need greater efforts to counter them, 3) only 3 works discussed techniques for evasion attacks by leveraging fragilities in antivirus engines, meaning that direct attack on the detector is no longer as effective, 4) reinforcement learning algorithm serves as the most popular adversarial attack-based methods and 50% of works based on reinforcement learning are effective against real-world antivirus engines. Furthermore, this paper delves into the development trends in evasive malware and open issues for defenders. The primary objective of this survey is to furnish researchers and practitioners with a thorough comprehension of malware evasion strategies and techniques, thereby fostering the advancement of more potent and efficient approaches to detect and thwart malware.

Detecting security vulnerabilities with vulnerability nets

Detecting security vulnerabilities is a crucial part in secure software development. Many static analysis tools have proved to be effective in finding vulnerabilities, but generally there are some complex and subtle vulnerabilities that can escape from detection. Manual audits are a complementary approach to using tools. Unfortunately, most manual analyses are tedious and error prone. To benefit from both the tools and manual audits, some approaches incorporate the auditor's expertise into a static analysis tool during vulnerability discovery. Following this strategy, this paper presents a representation of source code called a vulnerability net, which is a special Petri net that integrates with data dependence graphs and control flow graphs. The combined representation can facilitate the detection of taint-style vulnerabilities such as buffer overflows and injection vulnerabilities. We test the proposed approach on Securibench Micro and demonstrate that it has the capability to identify a variety of vulnerabilities while keeping the rates of false negatives and positives low.

A software defect prediction method based on learnable three-line hybrid feature fusion

Software defect prediction (SDP) plays a crucial role in ensuring the security and quality of software systems. However, it faces challenges posed by high-dimensional features present in software defect datasets and the limited effectiveness of traditional nonlinear dimensionality reduction methods in extracting essential feature information. To address these issues, we propose a novel approach called learnable three-line hybrid feature fusion (LTHFFA), which incorporates the principle of three-line hybrid breeding into feature fusion for the first time. In this method, three distinct dimensionality reduction techniques are initially employed to obtain three separate sets of features. Subsequently, a learnable weight factor feature fusion method is proposed to facilitate automatically learn and dynamically update of feature weights. By integrating the three feature sets based on the principle of three-line hybrid breeding, we derive learnable three-line hybrid fusion features. These features are then utilized in the context of software defect prediction. Experimental results demonstrate the superior performance of LTHFFA compared to nine other dimensionality reduction methods across seventeen publicly available software defect datasets. LTHFFA exhibits the ability to effectively integrate multiple feature sets, reduce feature redundancy, and enhance predictive accuracy. Moreover, statistical analysis using Friedman ranking and Holm's post-hoc test confirms the significant advantage of LTHFFA over alternative dimensionality reduction methods.

A Practical Implementation of Quantum-Derived Keys for Secure Vehicle-to-Infrastructure Communications

We provide a practical implementation of a free space optical quantum key distribution (FSO-QKD) system within a vehicle-to-infrastructure (V2I) application developed under the Innovate UK AirQKD project. The FSO-QKD system provides the quantum secure encryption keys that serve as the foundation for secure communications throughout the V2I application to address known concerns over V2I security. This document includes summaries of the quantum key generation process and the deployed V2I technology. Subsequently, a high-level view of the system design, the practical experiment, and its execution are presented. Multiple AirQKD project partners developed technologies ranging from semiconductors and hardware to security protocols and software, to enable the QKD-secured V2I system. The developed technology includes a novel zero-trust security protocol used to protect the V2I communications, ensuring that spoofed V2I messages from a compromised device are not accepted by the system.

Scaling the Library Resources with Zscaler

More and more institutions and universities are implementing security measures to prevent wide-spread cyberattacks. When institutions initiate new security applications, the library is often overlooked. Libraries have complex ecosystems where multiple applications and systems converge with user data and online library resources. These ecosystems have various considerations including multi-factor authentication, proxies, multiple virtual private networks (VPNs), knowledgebases, and vendor inconsistencies. Duke University Health recently launched data security software called Zscaler, which prevented on-campus users from accessing the library e-resources. This presentation describes how Duke University Medical Center Library staff discovered the access issue and tested various workarounds to allow patrons to access library e-resources. The discussion covered how staff continue to work with Duke University Health IT security staff and university administration to find resolutions for the complex integration of security software with the library’s online resources.

Graph neural network based approach to automatically assigning common weakness enumeration identifiers for vulnerabilities

Vulnerability reports are essential for improving software security since they record key information on vulnerabilities. In a report, CWE denotes the weakness of the vulnerability and thus helps quickly understand the cause of the vulnerability. Therefore, CWE assignment is useful for categorizing newly discovered vulnerabilities. In this paper, we propose an automatic CWE assignment method with graph neural networks. First, we prepare a dataset that contains 3394 real world vulnerabilities from Linux, OpenSSL, Wireshark and many other software programs. Then, we extract statements with vulnerability syntax features from these vulnerabilities and use program slicing to slice them according to the categories of syntax features. On top of slices, we represent these slices with graphs that characterize the data dependency and control dependency between statements. Finally, we employ the graph neural networks to learn the hidden information from these graphs and leverage the Siamese network to compute the similarity between vulnerability functions, thereby assigning CWE IDs for these vulnerabilities. The experimental results show that the proposed method is effective compared to existing methods.

Security of software defined networks: evolution and challenges

&lt;span&gt;In software-defined networking (SDN), network traffic is managed by software controllers or application programming interfaces (APIs) rather than hardware components. It differs from traditional networks, which use switches and routers to control traffic. Using SDN, you can create and control virtual networks or traditional hardware networks. Furthermore, OpenFlow allows network administrators to control exact network behavior through centralized control of packet forwarding. For these reasons, SDN has advantages over certain security issues, unlike traditional networks. However, most of the existing vulnerabilities and security threats in the traditional network also impact the SDN network. This document presents the attacks targeting the SDN network and the solutions that protect against these attacks. In addition, we introduce a variety of SDN security controls, such as intrusion detection systems (IDS)/intrusion prevention system (IPS), and firewalls. Towards the end, we outline a conclusion and perspectives.&lt;/span&gt;

A security analysis of SOHO embedded systems

Due to many unusual constraints imposed on their design, embedded systems carry a large set of challenges related to feature implementation and security. Many systems extant today prove the extreme difficulty of designing a secure software architecture. Nevertheless, IoT systems are crucial in many environments ranging from large-scale, tightly secured commercial deployments to relatively unprotected home networks.

Studi Perbandingan Antara Memori DRAM dan Memori SRAM Dalam Sistem Keamanan Komputer

The ever-evolving digital world has made computer security an increasingly important and urgent issue. Computer security refers to a set of practices, policies and technologies designed to protect computer systems, networks and data from threats such as cyber attacks, data theft and software damage. The purpose of this research is to compare DRAM memory and SRAM memory in computer security systems. This research uses the library research method, which is the collection of information or scientific papers related to literature reviews that are literature. the selection between DRAM and SRAM must consider physical and software security needs, as well as system performance and power consumption.