In-Application Defense Against Evasive Web Scans through Behavioral Analysis

Web traffic has evolved to include both human users and automated agents, ranging from benign web crawlers to adversarial scanners such as those capable of credential stuffing, command injection, and account hijacking at the web scale. The estimated financial costs of these adversarial activities are estimated to exceed tens of billions of dollars in 2023. In this work, we introduce WebGuard, a low-overhead in-application forensics engine, to enable robust identification and monitoring of automated web scanners, and help mitigate the associated security risks. WebGuard focuses on the following design criteria: (i) integration into web applications without any changes to the underlying software components or infrastructure, (ii) minimal communication overhead, (iii) capability for real-time detection, e.g., within hundreds of milliseconds, and (iv) attribution capability to identify new behavioral patterns and detect emerging agent categories. To this end, we have equipped WebGuard with multi-modal behavioral monitoring mechanisms, such as monitoring spatio-temporal data and browser events. We also design supervised and unsupervised learning architectures for real-time detection and offline attribution of human and automated agents, respectively. Information theoretic analysis and empirical evaluations are provided to show that multi-modal data analysis, as opposed to uni-modal analysis which relies solely on mouse movement dynamics, significantly improves time-to-detection and attribution accuracy. Various numerical evaluations using real-world data collected via WebGuard are provided achieving high accuracy in hundreds of milliseconds, with a communication overhead below 10 KB per second.

An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks

The intrusion detection system (IDS) is an essential element of security monitoring in computer networks. An IDS distinguishes the malicious traffic from the benign one and determines the attack types targeting the assets of the organization. The main challenge of an IDS is facing new (i.e., zero-day) attacks and separating them from benign traffic and existing types of attacks. Along with the power of the deep learning-based IDSes in auto-extracting high-level features and its independence from the time-consuming and costly signature extraction process, the mentioned challenge still exists in this new generation of IDSes. In this paper, we propose a framework for deep learning-based IDSes addressing new attacks. This framework is the first approach using both deep novelty-based classifiers besides the traditional clustering based on the specialized layer of deep structures, in the security scope. Additionally, we introduce DOC++ as a newer version of DOC as a deep novelty-based classifier. We also employ the Deep Intrusion Detection (DID) framework for the preprocessing phase, which improves the ability of deep learning algorithms to detect content-based attacks. We compare four different algorithms (including DOC, DOC++, OpenMax, and AutoSVM) as the novelty classifier of the framework and use both the CIC-IDS2017 and CSE-CIC-IDS2018 datasets for the evaluation. Our results show that DOC++ is the best implementation of the open set recognition module. Besides, the completeness and homogeneity of the clustering and post-training phase prove that this model is good enough for the supervised labeling and updating phase.