Implementation Of The Machine Learning Classifier For Anomaly Detection

In this module, the class label for the testing data is predicted. The n – dimensional feature vector for the testing data is converted from query tree of testing data in the manner similar to the data pre – processing phase. The SQLIA classifier determines the new testing feature vector is normal or malicious, by using optimized SVM classification model.

Thus smarter systems are required to decrease the instances of false positives and false negatives. This paper reviews the existing methods some of the methods are yet unproven but the studies look very encouraging.

It is to be noted that a considerable research has been done on the dataset and support vector machine (SVM) algorithm is known to yield best results [1]. This paper addresses the dataset using supervised M/L

Decision tree models are explanatory models, which are English rules so they are easy to evaluate and understand by people. The decision tree model is considered as a chain of rules that classify records in different bins or classes called nodes [1]. Based on the model 's algorithm, every node may have two or more children or have no child, which is called in this case leaf node [1]. Building decision tree models requires partitioning the pre-classified dataset into three parts,

In 1980, James Anderson’s paper, Computer Security Threat Monitoring and Surveillance, bore the notion of intrusion detection. Through government funding and serious corporate interest allowed for intrusion detection systems(IDS) to develope into their current state. So what exactly is IDS? An IDS is used to detect malicious network traffic and computer usage through attack signatures. The IDS watches for attacks not only from incoming internet traffic but also for attacks that originate in the system. When a potential attack is detected the IDS logs the information and sends an alert to the console. How the alert is detected and handled at is dependent on the type of IDS in place. Through this paper we

Network Security: Here, machine learning can be leveraged to build models to find patterns in traffic that is used to distinguish benign traffic from malicious traffic that signals criminal activity. It is also possible to detect malicious software such as viruses,

Once you 've downloaded all of the things necessary to extract data, you 're going to have to actually extract it. Ultimately, you have to know what you 're looking for in order to be able to search it and figure it out. There are a number of guides out there that will walk you more through the technicalities of this process. That is not my goal, my goal is to outline the steps necessary to analyze data.

To perform adaptive cyber-security analytics using a computer implemented method that includes receiving a report on a network activity, It works as follows, a scoring method is used wherein a score responsive to the network activity is calculated. The score indicates a likelihood of security breach. This score is validated and the scoring model is automatically updated, responsive to the results of the validation. If the score falls within a threshold value of a security violation rule, the network activity is reported as suspicious. This basically relates generally to cyber-security monitoring, and more particularly to monitoring incoming security events to determine if any activity is violating security rules.

In this study we will provide state- of-the-art review in the area of anomaly detection based on non-parametric techniques and will assess different existing techniques and introduce a novel methodology for anomaly detection using dynamic evolving subsequence clustering.

These IDS possess an attacked description that can be matched to sensed attack manifestations. It catches the intrusions in terms of the characteristics of known attacks or system vulnerabilities. This IDS analyzes information it gathers and compares it to a database of known attacks, which are identified by their individual signatures. The rules are pre-defined. It is also known as misuse detection. The drawbacks of this IDS is that they are unable to detect novel or unknown attacks, Suffer from false alarm and have to programmed again for every new pattern to be detected.

All successful and unsuccessful cases are given scores. Unsuccessful cases are taken to learn about the mistakes and to know about machine learning. Ranking methods,

The creation of our graphical and Command Line interface came about as a means of testing our algorithms. Since we were attempting to mine associations from large datasets, it made sense to test our mining algorithms on artificially created datasets so as to check whether or not our algorithms were successful. Therefore, we devised the graphical interface to allow us to create sufficiently large datasets based upon association rules. This allowed us to plant specific rules with set supports and confidence with in our test datasets. We were then able to run our test datasets through our association mining algorithms to verify whether or not the algorithms correctly identified the rules which we had planted with the correct

Intrusion can be defined as the actions that attempt to compromise the integrity, confidentiality, or availability of a computer resource [1]. Intrusion Detection System is a software or device to discover the intrusion activity. Based on detection process, IDS can be classified into two groups: Anomaly detection, and misuse/signature detection. Anomaly detection looks for rare or unusual network activities, while misuse/signature detection uses past well-known attack patterns—signature--to identify illegal activities. This paper deals with only misuse/signature detection in computer networks. Network attack types can be categorized into the following groups:

Abstract - This paper presents the analysis of Kmeans and K-Medians clustering algorithm in detecting outliers. Clustering is generally used in pattern recognition where if a user wants to search for some particular pattern, clustering reduces the searching load. The k-means clustering and kmedians clustering algorithm’s performance in detecting outliers are analysed here. K-means clustering clusters the similar data with the help of the mean value and squared error criterion. Kmedians is similar to k-means algorithm but median values are calculated there. Outliers are the one different from norm. If they are not properly detected and handled, they clustering will be affected in a great manner.

The increase in the usage of the computer networks leads to the huge rise in the threat and attacks. These attackers change, steal and destroy the valuable information and finally cause complete damage to the computer system of the victim. They affect the performance of the computer system through the misconfiguration activities and generation of software bugs from internal and external networks. Irrespective of the existence of various security mechanism, attackers often attempt to harm the computer system of the intended legitimate users. Hence, security is a main factor for the efficient operation of the network in various applications such as healthcare monitoring, military surveillance, etc. The most common security mechanisms are firewalls, antivirus programs and Intrusion Detection System (IDS).