{"id":23230,"date":"2023-03-06T04:47:32","date_gmt":"2023-03-06T04:47:32","guid":{"rendered":"https:\/\/nsfocusglobal.com\/?p=23230"},"modified":"2026-04-17T18:07:42","modified_gmt":"2026-04-17T18:07:42","slug":"key-technologies-for-software-supply-chain-security-detection-techniques-part-1-software-composition-analysis","status":"publish","type":"post","link":"https:\/\/nsfocusglobal.com\/pt-br\/key-technologies-for-software-supply-chain-security-detection-techniques-part-1-software-composition-analysis\/","title":{"rendered":"Key Technologies for Software Supply Chain Security \u2013 Detection Techniques (Part 1) \u2013 Software Composition Analysis"},"content":{"rendered":"\n

Software supply chain security detection techniques must cover the software delivery life cycle, including software design, building, testing, and operation. There are mainly five types of security detection techniques, namely software composition analysis (SCA), static application security testing (SAST), dynamic application security testing (DAST), interactive application security testing (IAST), and FUZZ testing. Each of these detection techniques offers solutions to a specific stage of a software supply chain. The following table briefly introduces and compares these five types of techniques.<\/p>\n\n\n

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We will detail the five detection techniques one by one. Today we are focusing on software composition analysis.<\/p>\n\n\n\n

Software Composition Analysis (SCA)<\/strong><\/h2>\n\n\n\n

Open-source software is widely used in the software supply chain because of its openness, sharing, and freedom. Wider use of open code significantly improves the efficiency of software development and reduces its cost. However, open-source projects, often of uneven quality, are more exposed to attackers’ malicious tampering due to the existence of security vulnerabilities or maintenance personnel’s neglect to fix those vulnerabilities. This has posed a serious security problem. Today, with its number growing exponentially, open-source software is becoming more and more interdependent, resulting in huge difficulties in vulnerability identifying using human capabilities alone. Therefore, software composition analysis and vulnerability detection techniques are needed in order to solve this problem in an automatic and efficient way. Software composition analysis (SCA) is one of the most effective techniques of the kind.<\/p>\n\n\n\n

SCA is a static, white-box detection technique, which automatically analyzes software’s source codes and binary files to identify its software bill of materials (SBOM) and detect vulnerabilities and compliance risks. Effective detection is the first step towards software security.<\/p>\n\n\n\n

From a technical perspective, SCA is a general analysis method that is able to analyze any programming language including but not limited to Java, C\/C++, Golang, Python, and JavaScript. It focuses on third-party artifacts composing software, and the dependencies between them. A single SCA process can be divided into four stages, namely source code\/binary file analysis, feature extraction and identification, vulnerability detection, and SBOM generation.<\/p>\n\n\n\n

1.<\/strong> Source Code\/Binary File Analysis<\/strong><\/h3>\n\n\n\n

SCA is featured by a high level of compatibility that works across programming languages. The programs on which SCA performs analysis can either be source code, or all types of compiled binary files. Moreover, SCA takes in various types of digital information regardless of its program architecture or compilation method. Its detection objects include class names, method\/function names, constant strings, etc. on x86 platform or ARM platform as a windows program or a Linux program. The format of a binary analysis is as follows:<\/p>\n\n\n

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2. Feature Extraction and Identification<\/strong><\/h3>\n\n\n\n

Component identification is the second stage of an SCA process, and it generally adopts two techniques: package manager parsing and fingerprint identification.<\/p>\n\n\n\n