Biblio

Security is often a critical problem in software systems. The consequences of the failure lead to substantial economic loss or extensive environmental damage. Developing secure software is challenging, and retrofitting existing systems to introduce security is even harder. In this paper, we propose an automated approach for Finding and Repairing Bugs based on security patterns (FireBugs), to repair defects causing security vulnerabilities. To locate and fix security bugs, we apply security patterns that are reusable solutions comprising large amounts of software design experience in many different situations. In the evaluation, we investigated 2,800 Android app repositories to apply our approach to 200 subject projects that use javax.crypto APIs. The vision of our automated approach is to reduce software maintenance burdens where the number of outstanding software defects exceeds available resources. Our ultimate vision is to design more security patterns that have a positive impact on software quality by disseminating correlated sets of best security design practices and knowledge.

Security vulnerabilities and software defects are prevalent in software systems, threatening every aspect of cyberspace. The complexity of modern software makes it hard to secure systems. Security vulnerabilities and software defects become a major target of cyberattacks which can lead to significant consequences. Manual identification of vulnerabilities and defects in software systems is very time-consuming and tedious. Many tools have been designed to help analyze software systems and to discover vulnerabilities and defects. However, these tools tend to miss various types of bugs. The bugs that are not caught by these tools usually include vulnerabilities and defects that are too complicated to find or do not fall inside of an existing rule-set for identification. It was hypothesized that these undiscovered vulnerabilities and defects do not occur randomly, rather, they share certain common characteristics. A methodology was proposed to detect the probability of a bug existing in a code structure. We used a comprehensive experimental evaluation to assess the methodology and report our findings.

If, as most experts agree, the mathematical basis of major blockchain systems is (probably if not provably) sound, why do they have a bad reputation? Human misbehavior (such as failed Bitcoin exchanges) accounts for some of the issues, but there are also deeper and more interesting vulnerabilities here. These include design faults and code-level implementation defects, ecosystem issues (such as wallets), as well as approaches such as the "51% attack" all of which can compromise the integrity of blockchain systems. With particular attention to the emerging non-financial applications of blockchain technology, this paper demonstrates the kinds of attacks that are possible and provides suggestions for minimizing the risks involved.