Conditional adherence based classification of transactions for database intrusion detection and prevention

Indu, S.; Darbari, Vaibhav; Kejriwal, Lakshya; Agarwal, Ashwini

doi:10.1109/icacci.2016.7732023

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…A method recommended by Bertino et al [5] generates the standard outline for each role for inconsistent outline findings. Likewise, they used the Naïve-Bayes classifier [6] to discover inconsistent SQL queries. The analysis exertions are prepared to identify and prevent SQL injection outbreaks along with evaluating the effectiveness of the ModSecurity web application firewall in thwarting SQL injection outbreaks in [7].…”

Section: Related Workmentioning

confidence: 99%

A Preventive Approach Using the Data Mining of Transaction Audit Log for Database Intrusion Detection

Rathod¹

2023

JREAS

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Information is a key component in today’s global business environment. An organization, institute, or business firm uses various database management systems for managing its crucial information. The security mechanism provides by DBMS is not enough to prevent intruders or detect anomalous behavior. Unauthorized users and sometimes authorized users to execute malicious commands intentionally or by mistake, cannot be detected and prevented by a typical security mechanism. Intrusion detection system finds intrusive action and attempts by detecting the behavior of user’s action. Security features can be enhanced by adding intrusive detection technology to the Database management system. Data mining is to identify valid, novel, potentially useful, and ultimately understandable patterns in massive data. It is required to apply data mining techniques to detect various intrusions. In this paper mechanism based on data mining is discussed to detect malicious action in DBMS.

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Section: Related Workmentioning

confidence: 99%

A Preventive Approach Using the Data Mining of Transaction Audit Log for Database Intrusion Detection

Rathod¹

2023

JREAS

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“…Perilaku pengguna di generalisasi pada tingkat peran lalu diperiksa permintaan atribut dari hak istimewa mereka sebagai referensi dan memeriksa aktivitas berbahaya, waktu eksekusi cepat dan kecil penyimpanan memori [2]. Identifikasi perilaku menggunakan teknik penambangan aturan asosiasi dianalisis oleh klaster Classification of Database Cransactions based on Association Rules and Cluster Analysis (CDTARCA) menghasilkan kelompok parameter aktivitas profil pengguna yang digunakan untuk mengklasifikasi transaksi berbahaya [3]. Pengelompokkan lain menggunakan pendekatan berbasis kepadatan yang digabungkan dengan Random Forest memiliki peningkatan dalam nilai false negative.…”

Section: Pendahuluanunclassified

Deteksi Intrusi Pada Basis Data Menggunakan Random Forest

Putri

Zulianto

Suwarningsih

2021

JICT

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More services have been made online in recent years, more and more data is being stored virtually. This important and confidential data becomes an easy target for criminals in the era of digitalization. Database security becomes very necessary to keep data safe. Attacks can come from outside or from within, attacks caused by insiders are the second biggest threat after hacking. Conventional security has not been able to detect anomalies from internal users. This can be anticipated using an intrusion detection mechanism. This mechanism has previously been applied to networks and hosts. However, some actions that are harmful to the database are not necessarily harmful to the network and hosts so that intrusion detection on the database becomes extra security to defend the database from intruders. This system uses the Random Forest algorithm which includes supervised learning to detect anomalous transactions. The dataset used is a transaction log containing 773 records and 9 attributes. Anomalies are determined based on the threshold value of 3 attributes, namely operation, object and field name. The test uses 6 different trees, 10, 20, 40, 60, 80 and 100. The results of the test on 762 records and 5 attributes used, the Random Forest algorithm has the highest accuracy value on the number of trees 80 and 100 which have a test time difference of 0 .03 seconds. In the dataset used, the optimum number of trees is found at number 80 with an accuracy value of 99.56% and an execution time of 0.13183 seconds.

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