Detect Security Vulnerabilities Research Articles

A Study of The Effectiveness of Code Review in Detecting Security Vulnerabilities

A Study of The Effectiveness of Code Review in Detecting Security Vulnerabilities

Detection of security vulnerabilities in cryptographic ICs against fault injection attacks based on compressed sensing and basis pursuit

Detection of security vulnerabilities in cryptographic ICs against fault injection attacks based on compressed sensing and basis pursuit

Critical Analysis on Detection and Mitigation of Security Vulnerabilities in Virtualization Data Centers

There is an increasing demand for IT resources in growing business enterprises. Data center virtualization helps to meet this increasing demand by driving higher server utilization and utilizing un-used CPU cycles without causes much increase in new servers. Reduction in infrastructure complexities, Optimization of cost of IT system management, power and cooling are some of the additional benefits of virtualization. Virtualization also brings various security vulnerabilities. They are prone to attacks like hyperjacking, intrusion, data thefts, denial of service attacks on virtualized servers and web facing applications etc. This works identifies the security challenges in virtualization. A critical analysis on existing state of art works on detection and mitigation of various vulnerabilities is presented. The aim is to identify the open issues and propose prospective solutions in brief for these open issues.

A Survey on Web Application Penetration Testing

Websites are becoming increasingly effective communication tools. Nevertheless, web applications are vulnerable to attack and can give attackers access to sensitive information or unauthorized access to accounts. The number of vulnerabilities in web applications has increased dramatically over the past decade. Many are due to improper validation and sanitization of input. Identifying these vulnerabilities is essential for developing high-quality, secure web applications. Whenever a website is released to the public, it is required to have had penetration testing to a certain standard to ensure the security of the information. Application-level security vulnerability detection is possible for many commercial and open-source applications. However, developers are curious about which tools detect security vulnerabilities and how quickly they do so. The purpose of this study is to discuss penetration testing and how it can be implemented. This paper also explores the hazards and vulnerabilities associated with the web environment as well as the protective measures that can be taken. In addition, a comprehensive review and comparison of common web penetration testing tools is provided. The aim of this paper is to help web penetration testers choose a technology that is optimal for their requirements. The paper also sets out to guide and provide recommendations to users for choosing the best web penetration test tool and increasing their awareness of secure web environments. The study results indicate that not all web penetration testing tools offer the same features and that combining analysis tools can provide detailed information about web vulnerabilities.

Exploring the Effectiveness of Web Crawlers in Detecting Security Vulnerabilities in Computer Software Applications

Exploring the Effectiveness of Web Crawlers in Detecting Security Vulnerabilities in Computer Software Applications

A security vulnerability predictor based on source code metrics

Detecting security vulnerabilities in the source code of software systems is one of the most important challenges in the field of software security. We need an effective solution to discover and patch vulnerabilities before our valuable information is compromised. Security testing is a type of software testing that checks whether software is vulnerable to cyber attacks. This study aimed to pursue three main objectives: (1) The first goal is to identify the vulnerable functions of a C/C++ software program based on code metrics. This can reduce the cost of software security testing and also redirect the related activities to identified vulnerable functions rather than to the entire software, (2) The second goal is to identify the type of attack related to the vulnerability function, and (3) Finally, the ultimate goal is to analyze the relationship between code metrics and the vulnerabilities. This goal can help us understand which code structure is most likely to contain vulnerable code. This paper first aimed to create a comprehensive view of the source code of the target software using graph concepts. Second, a set of source code metrics and calculated by crawling on the related graph using the static analysis approach. Finally, the vulnerability prediction model presented in this paper is based on machine learning technique applied on metrics extracted from program source code. Compared to previous work, new achievements have been made in this paper. One of the most important ones is the very high accuracy detection of the proposed model in detecting the type of vulnerability. Moreover, 15 code metrics are used to predict vulnerabilities. Our analysis on feature importance indicates that what structure the software program code has, most likely, it will be vulnerable. Experimental results in 10 real projects (OpenSSL, SQLite, FreeType, LibTiff, Libxslt, Binutils, FFmpeg, ImageMagick, OpenSC, and rdesktop) indicated that the security testing predictor proposed in this paper could predict on average 89% of the really vulnerable functions of the source code and 86% of the vulnerability type of the detected functions correctly.

Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model

A smart contract, as an important part of blockchain technology, has attracted considerable interest from both industry and academia. It provides the basis for the realization of a variety of practical blockchain applications and plays a crucial role in the blockchain ecosystem. While it also holds a large number of digital assets, the frequent occurrence of smart contract vulnerabilities have caused huge economic losses and destroyed the blockchain-based credit system. Currently, the security and reliability of smart contracts have become a new focus of research, and there are a number of smart contract vulnerability detection methods, such as traditional detection tools based on static or dynamic analysis. However, most of them often rely on expert rules, and therefore have poor scalability and high false negative and false positive rates. Recent deep learning methods alleviate this issue, but without considering the semantic information and context of source code. To this end, we propose a hybrid attention mechanism (HAM) model to detect security vulnerabilities in smart contracts. We extract code fragments from the source code, which focus on key points of vulnerability. We conduct extensive experiments on two public smart contract datasets (a total of 24,957 contracts). Empirical results show remarkable accuracy improvement over the state-of-the art methods on five kinds of vulnerabilities, where the detection accuracy could achieve 93.36%, 80.85%, 82.56%, 85.62%, and 82.19% for reentrancy, arithmetic vulnerability, unchecked return value, timestamp dependency, and tx.origin, respectively.

Mobile cellular network security vulnerability detection using machine learning

Mobile cellular network security vulnerability detection using machine learning

Static Detection of Security Vulnerabilities Using Machine Learning on Graph Information

Static Detection of Security Vulnerabilities Using Machine Learning on Graph Information

BinVulDet: Detecting vulnerability in binary program via decompiled pseudo code and BiLSTM-attention

Static detection of security vulnerabilities in binary programs is an important research field in software supply chain security. However, existing vulnerability detection methods based on code similarity can only detect known vulnerabilities. Vulnerability features generated by vulnerability pattern-based detection methods are low robust due to the influence of manually defined patterns, compiler diversity, and irrelevant function instructions. In this paper, we propose BinVulDet, which is a binary level vulnerability detection tool for accurate known and unknown vulnerability detection. BinVulDet uses decompilation techniques to obtain pseudo code containing high-level semantic information against the impact of compilation diversity. Then the program slicing technique is used to extract the statements with data dependencies and control dependencies related to the vulnerability. A BiLSTM-attention neural network is used to extract rich contextual semantic information from slice codes to generate more robust vulnerability patterns to detect vulnerabilities. The experimental results show that BinVulDet outperforms the state-of-the-art binary vulnerability detection methods. The FPR and FNR of BinVulDet are 1.04% and 0.89% on average, respectively, which are 3.93% and 22.86% lower than the baseline model on average. BinVulDet can effectively against the influence of compilation diversity and successfully be used for real-world vulnerability detection by being evaluated in three CVE vulnerability projects.

Combine sliced joint graph with graph neural networks for smart contract vulnerability detection

Smart contract security has drawn extensive attention in recent years because of the enormous economic losses caused by vulnerabilities. Even worse, fixing bugs in a deployed smart contract is difficult, so developers must detect security vulnerabilities in a smart contract before deployment. Existing smart contract vulnerability detection efforts heavily rely on fixed rules defined by experts, which are inefficient and inflexible.To overcome the limitations of existing vulnerability detection approaches, we propose a GNN based approach for smart contract vulnerability detection. First, we construct a graph representation for a smart contract function with syntactic and semantic features by combining abstract syntax tree (AST), control flow graph (CFG), and program dependency graph (PDG). To further strengthen the presentation ability of our approach, we perform program slicing to normalize the graph and eliminate the redundant information unrelated to vulnerabilities. Then, we use a Bidirectional Gated Graph Neural-Network model with hybrid attention pooling to identify potential vulnerabilities in smart contract functions. Empirical results show that our approach can achieve 89.2% precision and 92.9% recall in smart contract vulnerability detection on our dataset and reveal the effectiveness and efficiency of our approach.

A Framework for Modeling and Detecting Security Vulnerabilities in Human-Machine Pair Programming

<p>To detect and mitigate security vulnerabilities early in the coding phase is an important strategy for secure software development. Existing solutions typically focus on finding certain vulnerabilities in certain computer systems without giving a systematic way of handling different types of vulnerabilities. In this paper, we present a framework for systematically modeling and detecting potential security vulnerabilities during the construction of programs using a particular programming paradigm known as Human-Machine Pair Programming. The framework provides designers with a general way of modeling a class of attacks in detail, and shows how programmers can discover and fix a vulnerability in a timely manner. Specifically, our framework advocates three primary steps: (1) generating an attack tree to model a given security threat, (2) constructing vulnerability-matching patterns based on the result of the attack tree analysis, and (3) detecting corresponding vulnerabilities based on the patterns during the program construction. We also present a case study to demonstrate how it works in practice.</p> <p> </p>

AflIot: Fuzzing on linux-based IoT device with binary-level instrumentation

In recent years, coverage-guided greybox fuzzing has demonstrated its efficiency in detecting security vulnerabilities on traditional devices. Instrumentation information plays a significant role in sophisticated greybox fuzzer such as American Fuzzing Lop to directionally improve coverage and distill seeds. While open-source programs leverage wrapped assemblers to glean instrumentation information, closed-source programs can utilize the emulation-based instrumentation for coverage-guided fuzzing. The pervasiveness of the closed source puts a strong demand for emulation instrumentation. However, the required access to peripherals brings great difficulty in fuzzing on the emulator, especially for those various IoT devices. This paper presents AflIot, the first generic on-device fuzzing framework for Linux-based IoT binary programs. By leveraging offset-free binary-level instrumentation, binary programs can avoid unnecessarily rewriting, inherit compatibility of peripherals, and be executed directly on IoT devices by AflIot. We evaluate AflIot on multiple benchmarks with real-world IoT programs. AflIot identified 437 unique crashes in 13 binary programs, including 95 newly confirmed unique crashes. Those crashes demonstrate that AflIot is efficient and effective in detecting potential software bugs in binary programs on Linux-based IoT devices.

Improving Security of Web Servers in Critical IoT Systems through Self-Monitoring of Vulnerabilities.

IoT (Internet of Things) systems are complex ones that may comprise large numbers of sensing and actuating devices; and servers that store data and further configure the operation of such devices. Usually, these systems involve real-time operation as they are closely bound to particular physical processes. This real-time operation is often threatened by the security solutions that are put in place to alleviate the ever growing attack surface in IoT. This paper focuses on critical IoT domains where less attention has been paid to the web security aspects. The main reason is that, up to quite recently, web technologies have been considered unreliable and had to be avoided by design in critical systems. In this work, we focus on the server side and on how attacks propagate from server to client as vulnerabilities and from client to unprotected servers; we describe the concerns and vulnerabilities introduced by the intensive usage of web interfaces in IoT from the server templating engines perspective. In this context, we propose an approach to perform self monitoring on the server side, propagating the self monitoring to the IoT system devices; the aim is to provide rapid detection of security vulnerabilities with a low overhead that is transparent to the server normal operation. This approach improves the control over the vulnerability detection. We show a set of experiments that validate the feasibility of our approach.

Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning Algorithms

Nowadays, software vulnerabilities pose a serious problem, because cyber-attackers often find ways to attack a system by exploiting software vulnerabilities. Detecting software vulnerabilities can be done using two main methods: i) signature-based detection, i.e. methods based on a list of known security vulnerabilities as a basis for contrasting and comparing; ii) behavior analysis-based detection using classification algorithms, i.e., methods based on analyzing the software code. In order to improve the ability to accurately detect software security vulnerabilities, this study proposes a new approach based on a technique of analyzing and standardizing software code and the random forest (RF) classification algorithm. The novelty and advantages of our proposed method are that to determine abnormal behavior of functions in the software, instead of trying to define behaviors of functions, this study uses the Word2vec natural language processing model to normalize and extract features of functions. Finally, to detect security vulnerabilities in the functions, this study proposes to use a popular and effective supervised machine learning algorithm.

Computer Deep Learning Network Security Vulnerability Detection Based on Virtual Reality Technology

In order to detect the computer network security technology vulnerabilities due to various factors, the normal operation of the computer network must be ensured, the user’s confidential information must be protected, and it is proposed that the analysis and research on security vulnerability detection must be strengthened. This study introduces the working principle of the network security monitoring system, analyzes the key technologies involved in the system development process and network programming technology, gives the overall architecture of the system, and designs the processing flow of the monitoring function. The test and analysis of the system show that the design of the system has achieved the expected design goal. The design of the system has achieved the expected design goal. The five test points can meet the standard time specified in the demand analysis process. The time difference of all module test points in the test is less than 3 s. The system can realize the remote acquisition and real-time monitoring of the network access, file system operation, system operation status, and other information of the controlled computer. Through the test and analysis of the system, it is shown that the system has achieved the expected design goal, the working state problem, and can meet the functional requirements of internal network security monitoring. It can be applied to enterprises, institutions, and departments that have higher requirements for intranet information security.

Proximal Instance Aggregator networks for explainable security vulnerability detection

Security vulnerabilities in software are the root cause of cyberattacks. Considering that these defects have huge associated costs, they should be proactively detected and resolved before shipping the software. Data-driven approaches like Artificial Intelligence (AI) are vastly explored for automatic vulnerability detection, given their potential to leverage large-scale vulnerability data feeds and learn from these scenarios. This work introduces a novel Proximal Instance Aggregator (PIA) neural network for accurately capturing insecure C code patterns from Abstract Syntax Tree (AST). It is built upon the concept of Multiple Instance Learning (MIL), which treats the AST representation of the code as a ‘bag’ of tree path ‘instances’. The security vulnerability can manifest in one or multiple such AST path instances. The PIA model dynamically learns a set of abstract concepts to describe the patterns associated with the AST paths. Specifically, the vulnerable nature of an AST path is characterized by its proximity to these concepts. The model also employs the attention mechanism to generate deep representations. By drawing cross-correlation of features between the path instances, the self-attention robustly weighs the relevance of each AST path towards vulnerability classification. The MIL utilizes these deep feature sets to construct the concept space. Thus, even without explicit supervision for localizing the line of defect, the AI automatically learns AST instance classification in a weakly supervised manner. Since AST-level prediction is formed as an aggregation of instance classifications, the AI is inherently explainable. The model outperforms state-of-the-art methods by a fair margin. It achieves 95.63% detection accuracy and 95.65% F1-score on the benchmarked NIST SARD, NVD datasets for a range of vulnerabilities.

RTL-ConTest: Concolic Testing on RTL for Detecting Security Vulnerabilities

This article presents RTL-ConTest, a register transfer-level (RTL) security vulnerability detection algorithm, that extracts critical process flows from a RTL design and executes RTL-level concolic testing to generate security test cases for identifying critical exploits manifested in a System on Chip (SoC). The efficiency of the proposed approach is evaluated on opensource RISC-V-based SoCs. Our technique is successful in detecting the security vulnerabilities manifested in the processor core as well as in the rest of the SoC, e.g., debug modules, peripherals, etc., thereby providing a thorough vulnerability check on the entire hardware design. As demonstrated by our experimental results, in circumstances where conventional security verification tools are limited, RTL-ConTest furnishes significantly improved efficiency in detecting SoC security vulnerabilities.

Cryptographic core design security verification and vulnerability detection based on information flow analysis

Cryptographic cores are the key components to enforce information security properties related to confidentiality and integrity. Since the security of a cryptographic core implementation and the security of the cryptographic algorithm itself from the mathematical perspective are two different problems, cryptographic cores may contain hidden security vulnerabilities such as design flaws and side channels. Security analysis methods based on functional verification rely heavily on the quality of test vectors and usually have low test coverage, which is difficult to meet the security verification requirements of security-critical components like cryptographic cores. This work proposes a cryptographic core security verification and vulnerability detection method from the perspective of information flow security. The proposed method can accurately measure all logical information flows in cryptographic core designs to formally verify security properties such as confidentiality and integrity. It can detect potential security vulnerabilities in cryptographic implementations by capturing harmful information flows. Experimental results show that our information flow security verification method is effective in detecting sensitive information leakage caused by the design vulnerabilities and side channels in cryptographic cores.

Research and Practice on Security Protection and Disaster Recovery Strategy of Oracle Database in Colleges and Universities

Database security protection, database backup and disaster recovery are important tasks for all colleges and universities to ensure the safe and stable operation of information systems. Based on the operating environment of the Oracle production database in China University of Geosciences (Beijing), combined with the practical operation and maintenance experience, this paper provides a design and implementation case of Oracle database security protection system and disaster recovery architecture. The network security protection architecture of the three-layer firewall and fortress machine, the detection and repair of security vulnerabilities, the management of system accounts and permissions, data encryption and database audit constitute the security protection system of the database. Oracle RAC (Real Application Clusters), Oracle DataGuard, redundant backup management and backup recovery constitute the disaster recovery architecture of the database. The case has practical significance for database operation and maintenance management in other colleges and universities.