The Science Of Data Mining

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

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

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

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.

These proposals and systems suggestions can minimize the vulnerabilities associated with any compromises or intrusions within the network. Deploying an intrusion detection system is an essential security strategy for monitoring a network information system for abnormal or authorized activity. An intrusion detection system (IDS) is set of tools which monitor a network topology by providing a system administrator with the overall picture of how the system is being utilized. Executing an IDS will make a difference in creating a defense in depth architecture to be more compelling in recognizing any form of malicious activities. The capacity of the IDS is to monitor and survey the network traffic without affecting network activity. IDS tools gather information and analyzes it against a pre-characterized manage set, and against a set of known assault 'marks'. The IDS can scan port numbers and to determine if any breaches or attacks are occurring (Kuipers,

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

The analytical methods applied on security pattern data yield results in the form of pattern clusters and concepts. Pattern applicability can be improved by making this classification and resulting concept knowledge readily available to practitioners. In order to make it readily available, the results must be represented in a suitable format. Further, it must be shared using a tool or technology which is easy to use and does not need any special hardware or software. In this chapter, we present our approach towards representing the gained knowledge and sharing it so that it can be accessed and used. The chapter outline is as follows. In Section 7.1, we present the knowledge discovery and its adoption for the present work. The first mining tool applied is clustering. The knowledge obtained using this mechanism is presented in Section 7.2. The second knowledge discovery technique used in the research is FCA. Section 7.3 gives an overview of the knowledge extracted using FCA. In Section 7.4, we give our views about knowledge sharing, the issues which need to be considered and our solutions to address them. The representation format for various knowledge elements is presented in Section 7.5. The knowledge search framework is presented in Section 7.6 followed by a discussion on the implementation aspects in Section 7.7. The chapter ends with a summary.

of unique attack sources per day. To take a closer look at this phenomenon, we present

First, Chris led our group in learning to integrate different cyber data sources into ElasticSearch, which is a big data analytics platform, using tools such as Amazon Web Services and Logstash. After getting familiar with the data, Siddarth, Aldre and I together summarized time-window based features from the datasets that are helpful in anomaly detection, and Kenneth led the group in preprocessing the data with Python to extract the features we have discussed. At the current stage, Aldre and I are working simultaneously on anomaly detection algorithms for the suspicious network flow patterns. Specifically, Aldre has been working on Transductive

Keywords— Cybercrime; Information Security; Real-time Data Mining; Real-time Data Mining Engine (RTDME); Cybercrime Pattern Recognition (CPR); Threat Prevention and Response Algorithm Generator (TPRAG), Real-time Security Protocol (RTSP);

Compromised machines are one of the key security threats on the internet , project focus on the detection of the compromised machines in the network that are involved in the spamming activities, commonly known as spam zombies. To detect these compromised machines We develop an flourishing spam zombie detection system named SPOT, this can monitor the outgoing messages of a network. This successful SPOT is designed based on a powerful statistical tool called Sequential Probability Ratio Test, which has reduced false positive and false negative error rates.

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.

In the world of cybersecurity, machine learning focuses on making predictions that are based on the thousands of properties gleaned from earlier data. Signatures, behaviors, heuristics and other current techniques rely on data points that are as simplistic as they are easy to evade. The primary differentiator of this new technology is that previously unknown attacks are detectable, even those that are not yet conceived or written, but the detection of previously known attacks is not sacrificed. In essence, this type of machine learning delivers the power to predict future attacks.

Data mining can be used to model crime detection problems. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. About 10% of the criminals commit about 50% of the crimes. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. One of the most challenging problems facing crime analysts is that of identifying “crime series” which are sets of crimes committed by the same individual or group. Detecting series’ of crime can be an important step in predictive policing, as knowledge of an ongoing pattern can be of paramount importance towards stopping it. This easy to implement data mining framework works with the geo spatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for RAW.