Efficient intrusion detection scheme based on SVM

The network intrusion detection problem is the focus of current academic research. In this paper, we propose to use Support Vector Machine (SVM) model to identify and detect the network intrusion problem, and simultaneously introduce a new optimization search method, referred to as Improved Harmony Search (IHS) algorithm, to determine the parameters of the SVM model for better classification accuracy. Taking the general mechanism network system of a growing city in China between 2006 and 2012 as the sample, this study divides the mechanism into normal network system and crisis network system according to the harm extent of network intrusion. We consider a crisis network system coupled with two to three normal network systems as paired samples. Experimental results show that SVMs based on IHS have a high prediction accuracy which can perform prediction and classification of network intrusion detection and assist in guarding against network intrusion

Privacy Violation and Detection Using Pattern Mining Techniques

Privacy, its violations and techniques to bypass privacy violation have grabbed the centre-stage of both academia and industry in recent months. Corporations worldwide have become conscious of the implications of privacy violation and its impact on them and to other stakeholders. Moreover, nations across the world are coming out with privacy protecting legislations to prevent data privacy violations. Such legislations however expose organizations to the issues of intentional or unintentional violation of privacy data. A violation by either malicious external hackers or by internal employees can expose the organizations to costly litigations. In this paper, we propose PRIVDAM; a data mining based intelligent architecture of a Privacy Violation Detection and Monitoring system whose purpose is to detect possible privacy violations and to prevent them in the future. Experimental evaluations show that our approach is scalable and robust and that it can detect privacy violations or chances of violations quite accurately. Please contact the author for full text at [email protected]

Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

It is estimated that between $600 and$850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to$175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

An Event Based Digital Forensic Scheme for Vehicular Networks

The software in today's cars has become increasingly important in recent years. The development of high-tech driver assistance devices has helped fuel this movement. This tendency is anticipated to accelerate with the advent of completely autonomous vehicles. As more modern vehicles incorporate software and security-based solutions, "Event-Based digital forensics," the analysis of digital evidence of accidents and warranty claims, has become increasingly significant. The objective of this study is to ascertain, in a realistic setting, whether or not digital forensics can be successfully applied to a state-of-the-art automobile. We did this by dissecting the procedure of automotive forensics, which is used on in-car systems to track the mysterious activity by means of digital evidence. We did this by applying established methods of digital forensics to a state-of-the-art car.Our research employs specialized cameras installed in the study areas and a log of system activity that may be utilized as future digital proof to examine the effectiveness of security checkpoints and other similar technologies. The goal is to keep an eye on the vehicles entering the checkpoint, look into them if there is any reason to suspect anything, and then take the appropriate measures. The problem with analyzing this data is that it is becoming increasingly complex and time-consuming as the amount of data that has been collected keeps growing. In this paper, we outline a high-level methodology for automotive forensics to fill in the blanks, and we put it through its paces on a network simulator in a state-of-the-art vehicle to simulate a scenario in which devices are tampered with while the car is in motion. Here, we test how well the strategy functions. Diagnostics over IP (Diagnostics over IP), on-board diagnostics interface, and unified diagnostic services are all used during implementation. To work, our solution requires vehicles to be able to exchange diagnostic information wirelessly.These results show that it is possible to undertake automotive forensic analysis on state-of-the-art vehicles without using intrusion detection systems or event data recorders, and they lead the way towards a more fruitful future for automotive forensics. The results also show that modern autos are amenable to forensic automotive analysis

Using response action with Intelligent Intrusion detection and prevention System against web application malware

Findings: After evaluating the new system, a better result was generated in line with detection efficiency and the false alarm rate. This demonstrates the value of direct response action in an intrusion detection system