Database IDS using data mining
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The document proposes an intrusion detection model that uses a modified Apriori algorithm to identify frequent patterns in a database transaction log and detect anomalous queries. The model consists of three stages: 1) initialization where Apriori is used to generate a list of trusted rules from the database, 2) periodic follow up where Apriori is rerun to detect new rules which are compared to the trusted list with outliers flagged as suspected intrusions, and 3) action where a database administrator reviews the suspected intrusions. While effective, the model has limitations such as potentially missing patterns for large datasets and reduced performance from multiple scans of transaction logs.
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![[[1] Liu Hong (Liaoning Jianzhu Vocational University), “Based on
the user behavior characteristics of mining database anomaly detection
model design”, 6th International Conference on Information
Management, Innovation Management and Industrial Engineering,
978-1-4799-6594-6/14/$31.00 ©2014 IEEE
.
[2] P.E. Elaziz, M. sobh and HK. Mohamed, “Database Intrusion
Detection Using Sequential Data Mining Approaches”, 978-1-4799-
6594-6/14/$31.00 ©2014 IEEE.
An intrusion-detection model is proposed. In the proposed
model, the modified algorithm is applied to the transaction
log of the database , to identity all the frequent patterns on
the database and detect anomalous queries. Afterwards, it
identifies periodically any new patterns detected..The
model is concentrating mainly on increasing the accuracy
of the patterns detected on the database transactions.
Using Apriori Algorithm for rule mining:
Two steps:
-Find all itemsets that have minimum support (frequent
item-sets, also called large item-sets).
-Use frequent itemsets to generate rules.
The introduced model consists of three stages. First Stage
called initialization, in which the Apriori sequential
Algorithm is selected based upon the database features. It
is applied to the database to get the list of trusted rules.
Second stage called periodic follow up and detection.
Afterwards, the same modified algorithm is applied to get
on the transactions since the last scanned transaction, to
get the strong rules detected on this period of time. After
that those new rules are compared with the trusted list.
The rules that are not in the trusted listed are added to the
suspected list.Finally,comes third stage called action,
where the action is a manual administration action. The
database administrator is supposed to check the list sent
to him/her and confirm whether these rules could cause
an intrusion or not.
Database Intrusion Detection Using Data Mining
Suraj Singh Chauhan
Mentor : Ms. Kritika Mehta
Computer Science Department, Jaypee Institute of Information Technology
LIMITATIONS
METHODOLOGY
INTRODUCTION FLOW CHART OF THE PROJECT CONCLUSIONS
PROCESS
REFERENCES
An intrusion is defined as, any set of actions that
attempt to compromise the integrity, confidentiality or
availability of a resource. Intrusion detection is a
passive approach to security, as it monitors
information systems and raises alarms when security
violations are detected.
A
• Stage1:Initialisation
• Apriori Algorithm is applied to transaction get
set of strong rules.
B
• Stage 2: Detection
• In this phase new queries are detected against
strong rules .
C
• Stage 3:Action
• In this stage action is taken against intrusion if
the new query is detected faulty.
Though the current project is efficiently detecting intrusion, it
has scope of improvement. .The limitations are:
•Patterns may be missed if data set is large.
•Number of database scans may reduce performance.
•Greater processing time for big transaction log to be sorted.](https://image.slidesharecdn.com/poster1-150606063912-lva1-app6892/85/Database-IDS-using-data-mining-1-320.jpg)
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Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
5
This document discusses and compares four main methodologies used in Intrusion Detection and Prevention Systems (IDPS): signature-based, anomaly-based, stateful protocol analysis-based, and hybrid-based. It provides details on how each methodology works and its strengths and weaknesses. A hybrid methodology that combines two or more of the other approaches is described as generally providing the best detection capabilities by taking advantage of the strengths of the individual methodologies. The document concludes by offering a way to evaluate IDPS systems based on parameters like resistance to evasion, accuracy, overhead, and other factors.
Critical analysis of genetic algorithm based IDS and an approach for detecti...
This document discusses and analyzes different approaches to intrusion detection systems (IDS), including genetic algorithm approaches, data mining approaches, and intrusion detection in mobile ad hoc networks (MANETs). It provides an overview of genetic algorithm based IDS and data mining techniques for IDS. It also proposes a hybrid approach that combines anomaly detection and signature-based IDS to detect both known and unknown attacks. The document concludes that a critical analysis of various IDS techniques was performed and a hybrid approach is promising for intrusion detection in MANETs.
Study on Exploring Data Mining Techniques for Network Intrusion Detection
Network intrusion detection is very important mechanism for detecting intrusions in networks. Data mining techniques play very important role in detecting intrusions in networks. Intrusions cause damage to the data and compromise integrity and confidentiality and availability of the data. Though many intrusions preventing software's are developed and installed in network stations and network operating systems still finds vulnerabilities of the systems using network hacking techniques and tries to break the security walls of the system and enters despite the intrusion preventive mechanism built in. Hence network administrators and data management team feels the intrusion detection techniques are essential in-order to know that intrusion occurred or not and track them in and out entries in the network, so that steps can be taken for further for preventing intrusions or to block the intrusions that keep coming in. though In Corporate world many intrusion detection software's and detections techniques of different solutions are being developed still the people are not satisfied with the performance of the IDS. So many researches are still carrying on in this area to seek efficient techniques which are used for Intrusion detection. In this research papers gives the idea of what is network intrusion detection system and what it supposed to do and what are the problems with this technology and finally focus on Data Mining Techniques & data mining process to build more effective intrusion detection systems.
Intrusion Detection System (IDS): Anomaly Detection using Outlier Detection A...
This document describes a proposed approach for anomaly detection in intrusion detection systems using outlier detection. It begins with background on intrusion detection systems and issues with existing approaches. It then presents the proposed two-stage approach using outlier detection: 1) Training with large normal datasets in a distributed storage environment, and 2) Testing intrusion datasets to compute an error value compared to the trained model. If the error value exceeds a threshold, the test data is flagged as anomalous. Experimental results on network packet datasets demonstrate the approach can effectively identify anomalies.
A Study of Intrusion Detection System Methods in Computer Networks
Intrusion detection system (IDS) is an application system monitoring the network for malicious or intrusive activity. In these
systems, malicious or intrusive activities intrusion can be detected by using information like port scanning and detecting unusual traffic,
and then they can be reported to the network. Since intrusion detection systems do not involve predefined detection power and intrusion
detection, they require being intelligent. In this case, systems have the capability of learning. They can analyze packages entering the
network, and detect normal and unusual users. The common intelligent methods are neural networks, fuzzy logic, data mining techniques,
and genetic algorithms. In this research, the purpose is to study various intelligent methods.
Ijarcce 6
This document discusses using data mining techniques to help with crime investigation by analyzing large amounts of crime data. It compares the performance of three data mining algorithms (J48, Naive Bayes, JRip) on a sample criminal database to identify the best performing algorithm. The best algorithm would then be used on the criminal database to help identify possible suspects for a crime based on evidence and attributes. The document provides details on each of the three algorithms and evaluates them based on classification accuracy and other metrics to select the best technique for the criminal investigation application.
Ijcet 06 07_004
This document discusses approaches to avoid potential biases in data mining. It proposes two post-processing methods to prevent biased rules from being generated during data mining. The first method uses post-processing to generate a small set of high-quality predictive rules directly from the dataset to prevent biased rules. The second method uses categorization with the least amount of bias to also prevent biased rules. Previous work on detecting, measuring, and preventing biases during data mining is also reviewed, including direct and indirect bias prevention techniques as well as legal issues around identifying biases in datasets.
Ids 014 anomaly detection
The document discusses different techniques for anomaly detection in intrusion detection systems. It describes four main components of a typical anomaly detection model: data collection, normal system profiling, anomaly detection, and response. It then discusses the advantages and limitations of anomaly detection. The rest of the document summarizes various statistical, rule-based, biological, and learning models used for anomaly detection, including their key principles and examples like Haystack, NIDES, EMERALD, Wisdom & Sense, and Network Security Monitor.
Cyber intrusion
This document discusses intrusion detection systems (IDS). It covers the key components of an IDS, including methods of intrusion detection like audit trail processing, on-the-fly processing, profiles of normal behavior, signatures of abnormal behavior, and parameter pattern matching. The document also discusses building network-based IDS using tools like Snort and host-based IDS. It provides examples of labs to analyze network and wireless intrusion detection using machine learning techniques.
Anomaly detection
This document discusses anomaly detection techniques for intrusion detection systems. It begins by defining anomalies and explaining the principles of anomaly detection models. It then describes some key challenges in anomaly detection and different types of outputs it can provide. The document proceeds to classify anomaly detection techniques into statistical, machine learning and data mining based methods. As examples, it examines several case studies of early statistical anomaly detection systems like Haystack and IDES.
Using metadata in filtered logs for prevention of database intrusion through ...
This document summarizes a paper presented at the International Conference on Emerging Trends in Engineering and Management in 2014. The paper proposes a method to filter log data using frequent attribute values to reduce the data volume for forensic analysis. Metadata is then used to retrieve evidence of intrusions and notify the database owner. Logs are filtered using an algorithm that rates record redundancy, and metadata of filtered data is analyzed to detect intrusion patterns. If an intrusion is found, the database owner is alerted to reverse harmful actions. The framework involves data reduction using frequent attributes, metadata analysis for evidence, and intrusion notification.
Classification Rule Discovery Using Ant-Miner Algorithm: An Application Of N...
This document summarizes an algorithm called Ant-Miner that uses ant colony optimization to discover classification rules for network intrusion detection. Ant-Miner works by having artificial ants explore paths in a data structure representing the classification problem to discover rules. As more ants take the same path, the path is reinforced through pheromone updating, eventually leading to the discovery of classification rules. The authors apply Ant-Miner to a standard intrusion detection dataset and find it outperforms other classification methods in terms of accuracy and classification rate.
C3602021025
This document discusses using a combined approach of K-medoids clustering and Naive Bayes classification for intrusion detection. It begins by introducing intrusion detection systems and common data mining techniques used, such as classification and clustering. It then proposes a hybrid approach where K-medoids clustering is first used to group similar data instances, followed by Naive Bayes classification to classify the resulting clusters. The approach aims to maintain high accuracy and detection rates while lowering false alarm rates. An experiment on a prepared dataset showed the proposed approach performed better in terms of accuracy and detection rate with a reasonable false alarm rate.
The Transpose Technique On Number Of Transactions Of...
The document discusses how data mining techniques can be applied to extract useful knowledge and patterns from large datasets. It notes that raw data needs to be analyzed and manipulated to uncover its value, similar to an unpolished diamond needing to be polished to reveal its worth. The goal of data mining is to systematically analyze stored data to discover hidden patterns that can provide valuable insights and information for the future.
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- 1. [[1] Liu Hong (Liaoning Jianzhu Vocational University), “Based on the user behavior characteristics of mining database anomaly detection model design”, 6th International Conference on Information Management, Innovation Management and Industrial Engineering, 978-1-4799-6594-6/14/$31.00 ©2014 IEEE . [2] P.E. Elaziz, M. sobh and HK. Mohamed, “Database Intrusion Detection Using Sequential Data Mining Approaches”, 978-1-4799- 6594-6/14/$31.00 ©2014 IEEE. An intrusion-detection model is proposed. In the proposed model, the modified algorithm is applied to the transaction log of the database , to identity all the frequent patterns on the database and detect anomalous queries. Afterwards, it identifies periodically any new patterns detected..The model is concentrating mainly on increasing the accuracy of the patterns detected on the database transactions. Using Apriori Algorithm for rule mining: Two steps: -Find all itemsets that have minimum support (frequent item-sets, also called large item-sets). -Use frequent itemsets to generate rules. The introduced model consists of three stages. First Stage called initialization, in which the Apriori sequential Algorithm is selected based upon the database features. It is applied to the database to get the list of trusted rules. Second stage called periodic follow up and detection. Afterwards, the same modified algorithm is applied to get on the transactions since the last scanned transaction, to get the strong rules detected on this period of time. After that those new rules are compared with the trusted list. The rules that are not in the trusted listed are added to the suspected list.Finally,comes third stage called action, where the action is a manual administration action. The database administrator is supposed to check the list sent to him/her and confirm whether these rules could cause an intrusion or not. Database Intrusion Detection Using Data Mining Suraj Singh Chauhan Mentor : Ms. Kritika Mehta Computer Science Department, Jaypee Institute of Information Technology LIMITATIONS METHODOLOGY INTRODUCTION FLOW CHART OF THE PROJECT CONCLUSIONS PROCESS REFERENCES An intrusion is defined as, any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. Intrusion detection is a passive approach to security, as it monitors information systems and raises alarms when security violations are detected. A • Stage1:Initialisation • Apriori Algorithm is applied to transaction get set of strong rules. B • Stage 2: Detection • In this phase new queries are detected against strong rules . C • Stage 3:Action • In this stage action is taken against intrusion if the new query is detected faulty. Though the current project is efficiently detecting intrusion, it has scope of improvement. .The limitations are: •Patterns may be missed if data set is large. •Number of database scans may reduce performance. •Greater processing time for big transaction log to be sorted.