اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدUNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats
243
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Advanced Persistent Threats (APTs) are difficult to detect due to their low-and-slow attack patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based APT detector that effectively leverages data provenance analysis. From modeling to detection, UNICORN tailors its design specifically for the unique characteristics of APTs. Through extensive yet time-efficient graph analysis, UNICORN explores provenance graphs that provide rich contextual and historical information to identify stealthy anomalous activities without pre-defined attack signatures. Using a graph sketching technique, it summarizes long-running system execution with space efficiency to combat slow-acting attacks that take place over a long time span. UNICORN further improves its detection capability using a novel modeling approach to understand long-term behavior as the system evolves. Our evaluation shows that UNICORN outperforms an existing state-of-the-art APT detection system and detects real-life APT scenarios with high accuracy.
قيم البحث
اقرأ أيضاً
Advanced persistent threats (APT) are stealthy cyber-attacks that are aimed at stealing valuable information from target organizations and tend to extend in time. Blocking all APTs is impossible, security experts caution, hence the importance of rese
arch on early detection and damage limitation. Whole-system provenance-tracking and provenance trace mining are considered promising as they can help find causal relationships between activities and flag suspicious event sequences as they occur. We introduce an unsupervised method that exploits OS-independent features reflecting process activity to detect realistic APT-like attacks from provenance traces. Anomalous processes are ranked using both frequent and rare event associations learned from traces. Results are then presented as implications which, since interpretable, help leverage causality in explaining the detected anomalies. When evaluated on Transparent Computing program datasets (DARPA), our method outperformed competing approaches.
Advanced persistent threats (APT) are stealthy, sophisticated, and unpredictable cyberattacks that can steal intellectual property, damage critical infrastructure, or cause millions of dollars in damage. Detecting APTs by monitoring system-level acti
vity is difficult because manually inspecting the high volume of normal system activity is overwhelming for security analysts. We evaluate the effectiveness of unsupervised batch and streaming anomaly detection algorithms over multiple gigabytes of provenance traces recorded on four different operating systems to determine whether they can detect realistic APT-like attacks reliably and efficiently. This report is the first detailed study of the effectiveness of generic unsupervised anomaly detection techniques in this setting.
Advanced Persistent Threats (APTs) are stealthy customized attacks by intelligent adversaries. This paper deals with the detection of APTs that infiltrate cyber systems and compromise specifically targeted data and/or infrastructures. Dynamic informa
tion flow tracking is an information trace-based detection mechanism against APTs that taints suspicious information flows in the system and generates security analysis for unauthorized use of tainted data. In this paper, we develop an analytical model for resource-efficient detection of APTs using an information flow tracking game. The game is a nonzero-sum, turn-based, stochastic game with asymmetric information as the defender cannot distinguish whether an incoming flow is malicious or benign and hence has only partial state observation. We analyze equilibrium of the game and prove that a Nash equilibrium is given by a solution to the minimum capacity cut set problem on a flow-network derived from the system, where the edge capacities are obtained from the cost of performing security analysis. Finally, we implement our algorithm on the real-world dataset for a data exfiltration attack augmented with false-negative and false-positive rates and compute an optimal defender strategy.
Advanced persistent threats (APTs) are organized prolonged cyberattacks by sophisticated attackers. Although APT activities are stealthy, they interact with the system components and these interactions lead to information flows. Dynamic Information F
low Tracking (DIFT) has been proposed as one of the effective ways to detect APTs using the information flows. However, wide range security analysis using DIFT results in a significant increase in performance overhead and high rates of false-positives and false-negatives generated by DIFT. In this paper, we model the strategic interaction between APT and DIFT as a non-cooperative stochastic game. The game unfolds on a state space constructed from an information flow graph (IFG) that is extracted from the system log. The objective of the APT in the game is to choose transitions in the IFG to find an optimal path in the IFG from an entry point of the attack to an attack target. On the other hand, the objective of DIFT is to dynamically select nodes in the IFG to perform security analysis for detecting APT. Our game model has imperfect information as the players do not have information about the actions of the opponent. We consider two scenarios of the game (i) when the false-positive and false-negative rates are known to both players and (ii) when the false-positive and false-negative rates are unknown to both players. Case (i) translates to a game model with complete information and we propose a value iteration-based algorithm and prove the convergence. Case (ii) translates to a game with unknown transition probabilities. In this case, we propose Hierarchical Supervised Learning (HSL) algorithm that integrates a neural network, to predict the value vector of the game, with a policy iteration algorithm to compute an approximate equilibrium. We implemented our algorithms on real attack datasets and validated the performance of our approach.
Advanced Persistent Threats (APTs) infiltrate cyber systems and compromise specifically targeted data and/or resources through a sequence of stealthy attacks consisting of multiple stages. Dynamic information flow tracking has been proposed to detect
APTs. In this paper, we develop a dynamic information flow tracking game for resource-efficient detection of APTs via multi-stage dynamic games. The game evolves on an information flow graph, whose nodes are processes and objects (e.g. file, network endpoints) in the system and the edges capture the interaction between different processes and objects. Each stage of the game has pre-specified targets which are characterized by a set of nodes of the graph and the goal of the APT is to evade detection and reach a target node of that stage. The goal of the defender is to maximize the detection probability while minimizing performance overhead on the system. The resource costs of the players are different and the information structure is asymmetric resulting in a nonzero-sum imperfect information game. We first calculate the best responses of the players and characterize the set of Nash equilibria for single stage attacks. Subsequently, we provide a polynomial-time algorithm to compute a correlated equilibrium for the multi-stage attack case. Finally, we experiment our model and algorithms on real-world nation state attack data obtained from Refinable Attack Investigation system.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
