Compromised user credentials detection in a digital enterprise using behavioral analytics

Saleh Shah,Babar Shah,Adnan Amin,Feras Al-Obeidat,Francis Chow,Fernando Joaquim Lopes Moreira,Sajid Anwar

doi:10.1016/j.future.2018.09.064

Abstract

In today’s digital age, the digital transformation is necessary for almost every competitive enterprise in terms of having access to the best resources and ensuring customer satisfaction. However, due to such rewards, these enterprises are facing key concerns around the risk of next-generation data security or cybercrime which is continually increasing issue due to the digital transformation four essential pillars—cloud computing, big data analytics, social and mobile computing. Data transformation-driven enterprises should ready to handle this next-generation data security problem, in particular, the compromised user credential (CUC). When an intruder or cybercriminal develops trust relationships as a legitimate account holder and then gain privileged access to the system for misuse. Many state-of-the-art risk mitigation tools are being developed, such as encrypted and secure password policy, authentication, and authorization mechanism. However, the CUC has become more complex and increasingly critical to the digital transformation process of the enterprise’s database by a cybercriminal, we propose a novel technique that effectively detects CUC at the enterprise-level. The proposed technique is learning from the user’s behavior and builds a knowledge base system (KBS) which observe changes in the user’s operational behavior. For that reason, a series of experiments were carried out on the dataset that collected from a sensitive database. All empirical results are validated through well-known evaluation measures, such as (i) accuracy, (ii) sensitivity, (iii) specificity, (iv) prudence accuracy, (v) precision, (vi) f-measure, and (vii) error rate. The experiments show that the proposed approach obtained weighted accuracy up to 99% and overall error of about 1%. The results clearly demonstrate that the proposed model efficiently can detect CUC which may keep an organization safe from major damage in data through cyber-attacks.

Highlights

In the recent decade, due to great paradigm shifts in terms of technological advances, organizations are facing business volatility; requiring business agility and changing the way people work to optimize business performance
The proposed approach will check the user’s behavior with individual-level pattern information., if it is found that user does not match with the existing pattern of the user the system will check the patterns of the rest of the users in the cluster-level pattern information
Organizations that adopt digital transformations are rewarded to improve their relationship with customers, attracts the best talent and set them up for success

Summary

Introduction

Due to great paradigm shifts in terms of technological advances, organizations are facing business volatility; requiring business agility and changing the way people work to optimize business performance. The digital transformation, called digitalization, is the ability to turn existing services or products into digital alternatives and offer benefits over the tangible product. It is known as a business model driven by the changes associated with the presentation of digital technology in every aspect of the human society [1]. Any organization that adopts any of these pillars will have resultantly data that’s bigger in volume, different in variety and velocity If such data of any organization is not safe or can be accessed and altered by an unauthorized person, it will have a greater impact in terms of losses and will negatively affect any business enterprise. Digital transformation-driven enterprises should be ready to face such type of next-generation data security problem

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Results

Conclusion