Method for Mining Security Vulnerabilities of Data Storage of Electric Power Internet of Things Based On Spark Framework and RASP Technology

Abstract

The construction of power IoT (Internet of things) will greatly change the existing power business model and professional system, and will inevitably impact the data storage security of power IoT business. Due to the negligence or omission of the database designer in the process of designing and developing the database, there are a lot of security holes in the database, which makes the attacker successfully attack the database. In this paper, the research on data storage security vulnerability mining method of power IoT based on Spark framework and RASP technology is carried out. This paper puts forward a vulnerability mining methodology, which can be used to mine more potential vulnerabilities in Oracle more universally. In this way, the anonymous block is passed in as a parameter, and it is processed with the caller's permission instead of the definer's permission. Therefore, the attacker can only run the injected anonymous block with his own low permission, and can't achieve the attack purpose. The research results show that the algorithm designed in this paper will not be affected by memory space, so the mining efficiency of big data local frequent itemsets mining algorithm designed in this paper based on Spark framework will be much higher than that of traditional Apriori algorithm and FP-Growth algorithm. The mining performance of this method is better than that of the latter in three vulnerability types: injection vulnerability, XSS and CSRF.