The Science Of Data Mining

In the next step of catching the fraud, the application helps to narrow down interesting traffic worked like its diagnosing networking issues by logging events from all providers. When assisted support or further troubleshooting is necessary, the improved network trace logs can enable developers and support professionals to quickly isolate network activities and view the combined event data and packet captures in a single file, grouped by a network task and the related network activity. The output file, the messages will provide the Log Analytics where it offers predefined of common event log names. You can find the full name of user, time of access, privilege used. The logon type field indicates the kind of logon that occurred. The most common types are 2 (interactive) and 3 (network). Using Event ID is just one way, where the EventlD is a rich database of logging events. However, it’s not the only way of detecting and monitoring network traffic for fraud or intrusion. There are an open source tools and paid software, nevertheless we offer our suggestion for whom love programming and creative way. This solution came based on variety researches and experiment that held back in our school Bowie State University, as we are pursuing the perspective of being updated with technology, we also welcoming of the old research cause all what we have today its based of invested yesterday mistakes and flows.

tools will help to detect intrusions and other suspicious activities on the network. The third challenge is to improve the

 Identify at least two types of security events and baseline anomalies that might indicate suspicious activity.

When the GCU gathers evidence for later use for the court, sources of evidence can be monitored to detect threatened incidents in a timely manner. The GCU employee’s needs to be aware of suspicious transaction related to any activity in the customer account. Securing intrusion detection systems (IDS) components are important because IDS are often targeted by attackers that want to prevent the IDS from detecting attacks or want to gain access to sensitive information on the IDS, such as host configurations and known vulnerabilities. In monitoring and auditing, the types of activities recognized as suspicious will be different from different business needs. For example, a forensic accountant may look for specific patterns of financial data to trigger suspicion of fraud or theft. A suspicious event might be multiple emails on a sensitive subject from a person that is not involved in the subject. Recommend resources that can be used

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.

Today we are doing a project report on Costco. For Sol and Robert Price in 1976 they asked friends and family to help out with an opening price of two point five millon to open Price Club on July twelfth, they open their shop in an air hanger on Boulevard in San Diego, California. They were originally going to serve only small business. Mr. Price found out that it will be more beneficial to serve select customers. Costco was founded by James Sinegal and Jeffery H. Brotman. Costco opened its doors in 1983 in Seattle, Washington. Price Club and Costco later merged and

Abstract- For traditional computer vision methods the analysis of motion and behaviors in crowded scenes constitutes a challenging task, as barriers like occlusions, varying crowd densities and complex stochastic nature of their motions are difficult to overcome. As it has to be kept within reasonable limits, the one more complicating factor is the computational cost. It is very crucial to analyze crowded scenes in real time, or at least fast as possible, in many practical situations, considering the fact that security personnel should act quickly if something seems to be “unusual”. Anomalous is a problem which is not fitting into a familiar type, classification or pattern. HOS (Histogram of Oriented Swarm) is used for detecting and localizing anomalous events in videos of crowded scenes. HOS together with the HOG (Histogram of Oriented Gradient) are combined to give a descriptor that helps to effectively characterize each scene. The occurrences of gradient orientation in localized portion of images can be count by this technique. The HOS descriptor analyses and localizes the anomalous and normal events separately.

Abstract: In this real world, protecting the information of an organization that is present in a software and hardware or data present on them is important. Here comes the point of Threat Intelligence, where it recognises the disruption or misleading of the service provide by these data present on the software and hardware in the form of accessing through network, code injection, data injection, hacking of the sites, controlling through physical access or by any means of taking control over the data. Simply, Threat intelligence is the set of data collected, assessed and applied regarding security threats, threat actors, exploits, vulnerabilities and compromise indicators. It is usually presented in either the form of strategic or tactical intelligence. Strategic intelligence involves broader and higher-level abstracts of data to identify threats and how the organization needs to react where Tactical intelligence involves collecting the network information, analyzing it, identifying the threats and responding. By using of this it makes cost effective to organization by reducing security incidents, which increases responsive time by finding solution in a least possible time. It also shows the security incidents, attacks and events. It provides decision support to the organization and possibly a strategic advantage. Threat intelligence also involves series of steps which make the data to be gone through several phases starting with collection, then planning, process, produce

Now a day’s electricity theft is a major issue face by all electricity companies. Since electricity theft directly affect the profit made by electricity companies, detection and prevention of electricity theft is necessary. In this paper we are proposing a hybrid approach to detect the electricity theft. We will use SVM and ELM for our approach.

However, in our age we have resources that haven’t always been available to solve these problems. Electronic health care records, the internet, mobile phones and apps are a few of the many technologies that contribute to the copious amount of data we have access to today. This large amount of data can be overwhelming. While it once seemed impossible to sort through and extract meaningful patterns from these enormous amounts of data, automated data processing techniques have significantly advanced.

Incident response has evolved dramatically over the last decade to meet the challenges posed by an increase in talented cybercriminals who have developed new methods of attack. Many of the new attack modes target technologies that did not exist or were extremely rare 10 or 15 years ago. Smart homes, cloud computing, smartphone apps and smart cars are just a few points on the threat surface that are relatively new. Fortunately, innovative strategies and better tools have been developed in recent years to assist cybersecurity professionals, but no one is ready to claim a victory over the criminals. Cybersecurity will continue to evolve in what may well be a never-ending struggle to

Using big data analytics brings the capability of delivering vital information for detecting the formation of an attack prior to it striking and penetrating network defenses. By identifying and containing the exfiltration of data by malicious insiders, an organization can build a stronger cyber defense posture. Possessing the knowledge of the differences between acceptable and nefarious behavior is the key to identifying and stopping zero-day exploits. Big data analytics expands the knowledge base for anomaly detections by applying innovative pattern recognition techniques with machine learning algorithms identifying malicious behavior.

and analyzing enormous sets of data and then extracting the meaning of the data. Data mining

This way, the system will achieve the advantage of misuse detection to have a high detection rate on known attacks as well as the ability of anomaly detectors in detecting unknown attacks. Despite the inherent potential of hybrid detection, there are still two important issues that highly affect the performance of these hybrid systems. First, anomaly-based methods cannot achieve an outstanding performance without a comprehensive labeled and up-to-date training set with all different attack types, which is very costly and time consuming to create if not impossible. Second, efficient and effective fusion of several detection technologies becomes a big challenge for building an operational hybrid intrusion detection

Data stream mining is a stimulating field of study that has raised challenges and research issues to be addressed by the database and data mining communities. The following is a discussion of both addressed and open research issues [19].