Feature Selection Based On Hybrid Technique

Despite the presence of network security devices such as firewalls and other security appliances, today's corporate networks are still vulnerable to both internal and external attacks by hackers intent on creating havoc. By proactively

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

For the purpose of this assignment snort will be used as intrusion detections systems which is an open source IDS, snort has the ability to monitor traffics in real time and packet locking its also inspecting each packets as they enters into the network, Snort can be used as packet sniffer to analyse the network traffic in order to detect any bizarre looking packets or payloads which might have malicious data in it. Snort can also detect payloads attacks against the network or host system including but not limited to stealth port scan, and buffer overflows.

Where coursework, other than a Master’s dissertation or Master’s project, is handed in late and there are no valid mitigating circumstances, the

KDDCup99 dataset was introduced at the Third International Knowledge Discovery and Data Mining Tools Competition which was held by DARPA in 1999 .KDDCup99 is a refined data set from DARPA 1998 dataset as it contains only network data[3]. KDDCup99 is commonly used developers and implementers of new IDS to evaluate their systems. IDS systems take the KDDCup99 dataset as an input to train ,test the system and check performance of the IDS in classifying and detecting attack records. KDDCup99 dataset is used by most researchers because it contains 22 different attack types which could be classified into four main attack categories of the network discussed in the previous section. The full DARPA dataset consists of relatively 4,900,000 lines of connection vectors where each single connection vectors consists of 41 features and is marked as either normal or an attack, with exactly one particular attack type [38]. Among the 41 features of the connection, only sixteen significant attributes are considered which are: A1,A5,A6,A8, A9, A10, A11, A13, A16, A17, A18, A19, A23, A24, A32, A33[38] The KDD 99

 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

CSEC 630 Lab2 -Intrusion Detection System and Protocol Analysis Lab (n.d.). University of Maryland University College. Retrieved from: https://learn.umuc.edu/d2l/common/viewFile.d2lfile/Database/NzkyMzkw/CSEC630_lab2_LEO.pdf?ou=33745

The National Institute of Standards and Technology (NIST) defines Information Security Continuous Monitoring as “maintaining ongoing awareness of information security, vulnerabilities, and threats to support organizational risk management decisions” (Dempsey, et al., 2011). NIST advocates for continuous monitoring of information security systems, by the process of defining the program, establishing it, implementing the program, analyzing and reporting findings, responding to the findings, and reviewing and updating the program. Additionally, they believe that in order to be more cost effective, and to improve efficiency and reliability of the monitoring program, automation would be the way to go. By making use of the latest trends in technology such as machine learning and data mining, algorithms can be developed to detect patterns, which would ordinarily be missed by the human eye. This is especially true of voluminous data where it is time consuming and just plain difficult for personnel to sift through. Such implementations leaves the security professionals with time to tackle the issues which would require human involvement (Dempsey, et al., 2011). One example of an automated monitoring system is a firewall. The job of a firewall is to monitor and regulate the network traffic coming into and out of a system, which could be as small as a single home computer connected to the internet, to several thousands of devices in an organization the size of Amazon or

Evolution is a real thing that happened and is still happening today, there is so much evidence to prove it. We have the fossil record, we know how natural selection works and how and why it happens, and we know how artificial selection works and that we use it all of the time today. There is just too much evidence to call evolution just a theory.

2.4.7 Rapid intrusion detection and response procedures: KIU should have mechanisms in place to reduce the risk of undetected system intrusions. Computing systems are never perfectly secure. When a security failure occurs and an attacker is "in" the institution's system, only rapid detection and reaction can minimize any damage that might occur. Techniques used to identify intrusions include intrusion detection systems (IDS) for the network and individual servers (i.e., host computer), automated log correlation and analysis, and the identification and analysis of operational

With the development of computer technology especial Internet skills, computer has become an important part of human life. On the same time, there are more and more attacks appearing. Hackers attempt to explore the vulnerability of the host to gain unauthorized access privilege and make unauthorized use. However, no all attack can be considered as an incident. Attacks can be classified as incidents only if they meet the following characteristics (Torres, Alissa. Aug, 2014):

The goal of intrusion detection is to monitor network assets, detect anomalous behavior, and identify misuse within a network (Ashoor, Gore, 2011). An intrusion detection system (IDS) is a device or software application that monitors network system activities for malicious activity or policy violations and produces reports to a management station (Kashyap, Agrawal, Pandey, Keshri, 2013), additionally there are three types of IDS:

Firewalls is categorized as a preventive control which is used as a defense shield around IT systems to keep intruders and hacking from occurring, whereas, an Intrusion Detection System (IDS) which is categorized as a detective control is used to detect intrusions that have already occurred (Cavusoglu, Mishra, & Raghunathan, 2005). However, IDSs are not

Anomaly-Based techniques attempt to detect computer intrusions and misuse by monitoring system activity and classifying it as either normal or anomalous [1]. Anomaly based detection techniques helps to solve the issue of detecting unknown bots based on the anomalies such as high network latency, traffic on unusual ports, high volumes of traffic, and unusual system behavior [1]. Nevertheless this technique meets the problem of detecting unknown botnets [1,2]. Anomaly based detection techniques are classified into two categories: host-based and network based techniques [2].