A Data Driven Anomaly Based Behavior Detection Method for Advanced Persistent Threats (Apt)

Description

Download A Data Driven Anomaly Based Behavior Detection Method for Advanced Persistent Threats (Apt). Computer Science students who are writing their projects can get this material to aid their research work.

Abstract

Advanced Persistent Threats (APTs), represent sophisticated and enduring network intrusion campaigns targeting sensitive information from targeted organizations and operating over a long period. These types of threats are much harder to detect using signature-based methods.

Anomaly based methods consist of monitoring system activity to determine whether an observed activity is normal or abnormal. This is done according to heuristic or statistical analysis, and can be used to detect unknown attacks.

Despite all significant research efforts, such techniques still suffer from a high number of false positive detections. Detecting APTs is complex because it tends to follow a “low and slow” attack profile that is very difficult to distinguish from normal, legitimate activity.

The volume of data that must be analyzed is overwhelming. One technology that holds promise for detecting this kind of attack that is nearly invisible is Big data analytics.

In this work, I propose a data-driven anomaly-based behavior detection method which aims to leverage big data methods, and capable of processing significant amounts of data from diverse or several data sources.

Introduction

With the rapid development of computer networks, new and sophisticated types of attacks have emerged which require novel and more sophisticated defense mechanisms. Advanced Persistent Threats (APTs) are one of the most fast-growing cyber security threats that organizations face today.

They are carried out by knowledgeable, very skilled and well-funded hackers, targeting sensitive information from specific organizations.  

The objective of an APT attack is to steal sensitive data from the targeted organization, to gain access to sensitive customer data, or to access strategic or important business information that could be used for financial gain, blackmail, embarrassment, data poisoning, “illegal insider trading or disrupting an organization’s business”.

APT attackers target organizations in sectors with high-value information, such as national defense or military, manufacturing, and the financial industry.  

The technologies and methods employed in APT attacks are stealthy and difficult to detect, for instance, they can employ “social engineering which involves tricking people into breaking normal security procedures”.

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