Building adaptive defense against cybercrimes using real-time data mining
Baber Majid Bhatti
Head of IT Planning & Design
Ericsson AB
Adelaide, Australia babermb@gmail.com Nouman Sami
IT Provisioning Specialist
Ericsson Managed Services
Abbottabad, Pakistan noumansami83@hotmail.com Abstract— In today’s fast changing world, cybercrimes are growing at perturbing pace. At the very definition of it, cybercrimes get engendered by capitalizing on threats and exploitation of vulnerabilities. However, recent history reveals that such crimes often come with surprises and seldom follow the trends. This puts the defense systems behind in the race, because of their inability to identify new patters of cybercrime and to ameliorate to the required levels of security. This paper visualizes the empowerment of security systems through real-time data mining by the virtue of which these systems will be able to dynamically identify patterns of cybercrimes. This will help those security systems stepping up their defense capabilities, while adapting to the required levels posed by newly germinating patterns. In order to confine within scope of this paper, the application of this approach is being discussed in the context of selected scenarios of cybercrime.
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);
Hewes, J. A. (2016). Threat and Challenges of Cyber-Crime and the Response. SAM Advanced Management Journal (07497075), 81(2), 4-10.
Cyber security is a major concern for every department, business, and citizen of the United States because technology impacts every aspect of our daily lives. The more we use technology the more complacent, we get with the information that is stored within our cyber networks. The more complacent, we get, the more vulnerable we become to cyber-attacks because we fail to update the mechanisms that safeguard our information. Breaches to security networks are detrimental to personal, economic, and national security information. Many countries, like Russia, China, Israel, France, and the United Kingdom, now have the abilities and technology to launch cyber-attacks on the United States. In the last five years there have been several attacks on cyber systems to gain access to information maintained by major businesses and the United States Government. Cyber-attacks cause serious harm to the United States’ economy, community, and the safety, so we need to build stronger cyber security mechanisms. Based on my theoretical analysis, I recommend the following:
In the United States, local, state, and federal law enforcement agencies, including FBI, Department of Homeland Security, among others, have taken on roles to fight computer crimes and terrorism. The roles and responses of these law enforcement agencies concerning digital crime have created challenges that limit enforcement efforts against digital crime.
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(Perez 2013): The biggest threat of such type is the threat from the insiders and they keep seeking for the right opportunity to commit their cyber crime in order to achieve their illegitimate objectives. Therefore identification of threat becomes another crucial segment needs to be monitored and
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Our capstone project team is working on Prescriptive Analytics for Cyber Security. The project mainly consists of two parts – building a predictive anomaly detection algorithm that detects suspicious cyber anomalies based on multiple cyber datasets, and implementing a prescriptive model which optimizes the output from anomaly detection and recommend the best course of action. We have been closely working with Mr. Eric Chasin from Innvo Solutions LLC, and Prof. Anil Aswani from IEOR department, to together achieve our goal of creating an integrated system or model that would automatically detect and prescribe actions for cyber anomalies.
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