Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

Smart Meter Deployment Threat and Vulnerability Analysis and Response

Advanced intelligent or ＂smart＂ meters are being deployed in Asia. A result of deployment of smart meters, with associated equipment, is the electric power industry faced with new and changing threats, vulnerabilities and re-evaluate traditional approaches to cyber security. Protection against emerging cyber-security threats targeting smart meter infrastructures will increase risk to both the utility and customer if not addressed within initial rollouts. This paper will discuss the issues in SMI （smart meter infrastructures） deployments that pertain to cyber security. It will cover topics such as the threats to operations, infrastructure, network and people and organization and their associated risks. SMI deployments include not only the smart meter, but also the interfaces for home energy management systems as well as communication interfaces back to the utility. Utilities must recognize and anticipate the new threat landscape that can attack and compromise the meter and the associated field network collectors. They must also include threats to the WAN （wide-area-network） backhaul networks, smart meter headends, MDMS （meter data management systems） and their interfaces to CIS （customer information systems） and billing and OMS （outage management systems）. Lessons learned from SMI implementations from North America, Europe and recently, Japan, will be discussed. How white-box and black-box testing techniques are applied to determine the threat impact to the SMI. Finally, organizational change risk will be discussed and how utilities have responded to re-organizing and developing a security governance structure for the SMI and other smart grid applications.

Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

Design & Test of an Advanced Web Security Analysis Tool (AWSAT)

Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for non-security professionals. This paper presents the design and implementation of an automated web security analysis tool, AWSAT, aimed at enabling individuals with limited security expertise to effectively assess and mitigate vulnerabilities in web applications. Leveraging advanced scanning techniques, the tool identifies common threats such as Cross-Site Scripting (XSS), SQL Injection, and Cross-Site Request Forgery (CSRF), providing detailed reports with actionable insights. By integrating sample payloads and reference study links, the tool facilitates informed decision-making in enhancing the security posture of web applications. Through its user-friendly interface and robust functionality, the tool aims to democratize web security practices, empowering a wider audience to proactively safeguard against cyber threats.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Benchmarking Approach to Compare Web Applications Static Analysis Tools Detecting OWASP Top Ten Security Vulnerabilities

To detect security vulnerabilities in a web application,the security analyst must choose the best performance Security Analysis Static Tool(SAST)in terms of discovering the greatest number of security vulnerabilities as possible.To compare static analysis tools for web applications,an adapted benchmark to the vulnerability categories included in the known standard Open Web Application Security Project(OWASP)Top Ten project is required.The information of the security effectiveness of a commercial static analysis tool is not usually a publicly accessible research and the state of the art on static security tool analyzers shows that the different design and implementation of those tools has different effectiveness rates in terms of security performance.Given the significant cost of commercial tools,this paper studies the performance of seven static tools using a new methodology proposal and a new benchmark designed for vulnerability categories included in the known standard OWASP Top Ten project.Thus,the practitioners will have more precise information to select the best tool using a benchmark adapted to the last versions of OWASP Top Ten project.The results of this work have been obtaining using widely acceptable metrics to classify them according to three different degree of web application criticality.

DAVS:Dockerfile Analysis for Container Image Vulnerability Scanning

Container technology plays an essential role in many Information and Communications Technology(ICT)systems.However,containers face a diversity of threats caused by vulnerable packages within container images.Previous vulnerability scanning solutions for container images are inadequate.These solutions entirely depend on the information extracted from package managers.As a result,packages installed directly from the source code compilation,or packages downloaded from the repository,etc.,are ignored.We introduce DAVS–A Dockerfile analysis-based vulnerability scanning framework for OCI-based container images to deal with the limitations of existing solutions.DAVS performs static analysis using file extraction based on Dockerfile information to obtain the list of Potentially Vulnerable Files(PVFs).The PVFs are then scanned to figure out the vulnerabilities in the target container image.The experimental shows the outperform of DAVS on detecting Common Vulnerabilities and Exposures(CVE)of 10 known vulnerable images compared to Clair–the most popular container image scanning project.Moreover,DAVS found that 68%of real-world container images are vulnerable from different image registries.

Nonhomogeneous Risk Rank Analysis Method for Security Network System

Security measures for a computer network system can be enhanced with better understanding the vulnerabilities and their behavior over the time. It is observed that the effects of vulnerabilities vary with the time over their life cycle. In the present study, we have presented a new methodology to assess the magnitude of the risk of a vulnerability as a “Risk Rank”. To derive this new methodology well known Markovian approach with a transition probability matrix is used including relevant risk factors for discovered and recorded vulnerabilities. However, in addition to observing the risk factor for each vulnerability individually we have introduced the concept of ranking vulnerabilities at a particular time taking a similar approach to Google Page Rank Algorithm. New methodology is exemplified using a simple model of computer network with three recorded vulnerabilities with their CVSS scores.

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

Hybrid Security Assessment Methodology for Web Applications

This study presents a methodology to evaluate and prevent security vulnerabilities issues for web applications.The analysis process is based on the use of techniques and tools that allow to perform security assessments of white box and black box,to carry out the security validation of a web application in an agile and precise way.The objective of the methodology is to take advantage of the synergies of semi-automatic static and dynamic security analysis tools and manual checks.Each one of the phases contemplated in the methodology is supported by security analysis tools of different degrees of coverage,so that the results generated in one phase are used as feed for the following phases in order to get an optimized global security analysis result.The methodology can be used as part of other more general methodologies that do not cover how to use static and dynamic analysis tools in the implementation and testing phases of a Secure Software Development Life Cycle(SSDLC).A practical application of the methodology to analyze the security of a real web application demonstrates its effectiveness by obtaining a better optimized vulnerability detection result against the true and false positive metrics.Dynamic analysis with manual checking is used to audit the results,24.6 per cent of security vulnerabilities reported by the static analysis has been checked and it allows to study which vulnerabilities can be directly exploited externally.This phase is very important because it permits that each reported vulnerability can be checked by a dynamic second tool to confirm whether a vulnerability is true or false positive and it allows to study which vulnerabilities can be directly exploited externally.Dynamic analysis finds six(6)additional critical vulnerabilities.Access control analysis finds other five(5)important vulnerabilities such as Insufficient Protected Passwords or Weak Password Policy and Excessive Authentication Attacks,two vulnerabilities that permit brute force attacks.

Potential Vulnerability of Encrypted Messages: Decomposability of Discrete Logarithm Problems

This paper provides a framework that reduces the computational complexity of the discrete logarithm problem. The paper describes how to decompose the initial DLP onto several DLPs of smaller dimensions. Decomposability of the DLP is an indicator of potential vulnerability of encrypted messages transmitted via open channels of the Internet or within corporate networks. Several numerical examples illustrate the frame- work and show its computational efficiency.

基于数字孪生的网络安全漏洞分析方法研究

由于网络威胁不断演变,传统的漏洞分析方法已不能满足网络安全的需求。数字孪生作为一种先进的虚拟化技术,能够对网络漏洞进行全面和实时的分析。本文首先从模拟网络拓扑、硬件配置和通信协议三个方面构建网络安全数字孪生模型,并通过注入漏洞测试系统的安全响应情况。其次,基于构建的数字孪生模型,探讨了数字孪生如何模拟各种威胁事件,并阐述了网络安全漏洞的一般分析方法。最后,通过数字孪生对漏洞的跟踪和演化进行分析,揭示漏洞对系统安全的潜在影响,为网络安全提供了更全面的保护策略。

针对5G核心网协议的自动化漏洞挖掘方法

随着5G在全球范围内的广泛商用,5G网络安全问题广泛受到关注。针对5G核心网协议的自动化漏洞挖掘手段主要以黑盒模糊测试为主,但由于5G核心网协议设计复杂,黑盒模糊测试存在效率低、通用性差、拓展性不足等问题,不能有效检测到5G核心网协议安全漏洞。针对上述问题,深入了解5G核心网架构和重点接口协议的工作原理,总结在5G核心网场景下基于静态分析的自动化漏洞挖掘方法相较传统黑盒模糊测试方法的优势。对5G核心网的开源解决方案进行了源代码审计,发现在5G核心网协议实现中一类变量生命周期管理不当而导致的内存泄露安全问题,可导致5G核心网网元被拒绝服务攻击。基于此,提出了针对此类安全问题的通用漏洞模型,设计并实现了基于控制流和数据流混合分析的自动化漏洞挖掘方法。通过实验对该方法进行了有效性测试和效率评估,成功挖掘到了通用5G开源解决方案——Open5GS中的5个未公开安全漏洞,涵盖多个接口协议应用场景,获得了4个CVE编号。通过模拟环境,验证了该系列漏洞的影响范围广且利用条件低,充分证明了所提自动化漏洞挖掘方法的有效性,并向相关厂商进行了披露。

智能合约漏洞检测技术综述

智能合约作为可信的去中心化应用,获得了广泛的关注,但其安全漏洞问题对其可靠性带来了巨大威胁.为此,研究者们利用各种前沿技术(如模糊测试、机器学习、形式化验证等)研究了多种漏洞检测技术,并取得了可观的效果.为了系统性地梳理与分析现有智能合约漏洞检测技术,搜集截至2021年7月关于智能合约漏洞检测的84篇论文,根据它们的核心方法进行分类,从每种技术的实现方法、漏洞类型、实验数据等方面展开分析,同时对比国内外研究现状在这些方面的差异.最后,对现有的智能合约漏洞检测技术进行总结,探讨面临的挑战,并展望了未来的研究方向.

软件漏洞自动化利用综述

近年来软件漏洞数目急剧增加,漏洞危害也引起业界广泛关注.准确、高效、快速地编写出漏洞利用代码是漏洞危害性评估和漏洞修复的关键.当前漏洞利用代码主要依赖人工手动分析编写,效率较低.因此,如何实现自动化的漏洞利用代码生成是该领域研究的热点和难点.综述分析了该领域近30年的代表性成果,首先将漏洞自动化利用过程分为典型的4个环节:漏洞根源定位、可达路径搜索、漏洞原语生成、利用代码生成.然后从人机边界、攻防博弈、共性技术3个角度对上述成果进行梳理,明确当前研究的重点、难点,以及取得的阶段性成果.最后从现有成果与技术实用化所面临的差距方面,论述当前研究存在的瓶颈问题、未来的发展趋势,以及下一步的研究重点.

模糊测试技术的研究进展与挑战

模糊测试作为一种高效的漏洞挖掘技术,近年来发展快速,受到了越来越多研究人员的广泛关注.为了深入研究模糊测试技术,介绍了模糊测试的定义,分析了优点和缺点;从种子选择的能量调度、测试用例变异算法、模糊测试执行性能、混合模糊测试等方面,总结了模糊测试的研究进展,比较了各项研究的改进点和不足,进一步提出了模糊测试未来改进的建议;描述了模糊测试在操作系统内核、协议、固件、深度学习等领域的漏洞挖掘研究成果;对模糊测试未来的挑战和研究热点提出一些思考.

基于故障传播模型的硬件安全性与可靠性验证方法

大规模集成电路正面临着诸如设计脆弱性、侧信道、硬件木马等安全漏洞的威胁。传统的功能测试验证方法无法遍历所有的输入空间,同样无法检测侧信道安全漏洞。现有的形式化验证方法关注硬件设计的等价性和功能的正确性,难以满足安全性和可靠性验证需求。研究面向安全性和可靠性验证的形式化模型,形成有效的硬件安全性与可靠性形式化验证方法。该方法能够从门级对集成电路进行建模,生成细粒度的形式化模型,实现对安全性与可靠性的形式化验证,可以捕捉硬件设计中潜在的安全隐患。实验结果表明该验证方法对硬件设计中存在的侧信道和硬件木马导致的信息泄露和篡改有很好的检测效果。

智能模糊测试综述:问题探索和方法分类

随着近年来软件系统规模以及复杂性的增加,安全漏洞数量持续增长、影响面逐步扩大,全球安全形势依然严峻.针对该问题,学术界和工业界致力于研究高效的漏洞挖掘技术,提前发现和修复潜在的漏洞.其中模糊测试作为先进的漏洞挖掘技术之一,吸引了学术界和工业界的广泛关注.为了进一步提高漏洞挖掘的能力,研究人员提出了智能模糊测试,即利用人工智能和程序分析等技术作为辅助,从而实现对复杂软件系统更高效的测试和分析并智能引导漏洞挖掘方向.本文回顾了近八年来智能模糊测试研究进展,提出了一个通用模糊测试流程模型和问题导向的智能模糊技术分类方法,从优化测试输入生成、提高测试效率以及增强测试预言机三个方面总结了当前智能模糊测试的优势和不足之处,最后对智能模糊测试面临的挑战和未来研究方向进行展望和总结.

大数据背景下计算机信息技术在网络安全中的运用

随着互联网的迅猛发展和信息技术的日新月异,网络安全问题变得越来越突出和复杂化。恶意攻击、数据泄露、网络病毒等威胁不断涌现,对信息安全造成了严重的威胁。在这样的背景下,大数据技术的兴起为网络安全领域带来了新的机遇和挑战。首先介绍了大数据和网络安全的关系,其次详细阐述了大数据在网络安全中的应用领域,最后分析了大数据驱动的网络安全挑战和解决方案,并展望了未来的研究方向。