Large enterprises are increasingly relying on threat detection softwares (e.g., Intrusion Detection Systems) to allow them to spot suspicious activities. These softwares generate alerts which must be investigated by cyber analysts to figure out if they are true attacks. Unfortunately, in practice, there are more alerts than cyber analysts can properly investigate. This leads to a “threat alert fatigue” or information overload problem where cyber analysts miss true attack alerts in the noise of false alarms. In this paper, we present NoDoze to combat this challenge using contextual and historical information of generated threat alert in an enterprise. NoDoze first generates a causal dependency graph of an alert event. Then, it assigns an anomaly score to each event in the dependency graph based on the frequency with which related events have happened before in the enterprise. NoDoze then propagates those scores along the edges of the graph using a novel network diffusion algorithm and generates a subgraph with an aggregate anomaly score which is used to triage alerts. Evaluation on our dataset of 364 threat alerts shows that NoDoze decreases the volume of false alarms by 86%, saving more than 90 hours of analysts’ time, which was required to investigate those false alarms. Furthermore, NoDoze generated dependency graphs of true alerts are 2 orders of magnitude smaller than those generated by traditional tools without sacrificing the vital information needed for the investigation. Our system has a low average runtime overhead and can be deployed with any threat detection software.

/pdf/nodoze-combatting-threat-alert-fatigue-with-automated-4eehf1q1qj.pdf

NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage

Conventional attacks of insider employees and emerging APT are both major threats for the organizational information system. Existing detections mainly concentrate on users' behavior and usually analyze logs recording their operations in an information system. In general, most of these methods consider sequential relationship among log entries and model users' sequential behavior. However, they ignore other relationships, inevitably leading to an unsatisfactory performance on various attack scenarios. We propose log2vec, a heterogeneous graph embedding based modularized method. First, it involves a heuristic approach that converts log entries into a heterogeneous graph in the light of diverse relationships among them. Next, it utilizes an improved graph embedding appropriate to the above heterogeneous graph, which can automatically represent each log entry into a low-dimension vector. The third component of log2vec is a practical detection algorithm capable of separating malicious and benign log entries into different clusters and identifying malicious ones. We implement a prototype of log2vec. Our evaluation demonstrates that log2vec remarkably outperforms state-of-the-art approaches, such as deep learning and hidden markov model (HMM). Besides, log2vec shows its capability to detect malicious events in various attack scenarios.

Log2vec: A Heterogeneous Graph Embedding Based Approach for Detecting Cyber Threats within Enterprise

The Stackelberg Security Game (SSG) model has been immensely influential in security research since it was introduced roughly a decade ago. Furthermore, deployed SSG-based applications are one of most successful examples of game theory applications in the real world. We present a broad survey of recent technical advances in SSG and related literature, and then look to the future by highlighting the new potential applications and open research problems in SSG.

/pdf/stackelberg-security-games-looking-beyond-a-decade-of-1sl50x5c7n.pdf

Stackelberg security games: Looking beyond a decade of success

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed ac...

Adversarial Machine Learning

Phishing and spear phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attacks, considerable research has been conducted on these problems along multiple dimensions. We reexamine the existing research on phishing and spear phishing from the perspective of the unique needs of the security domain, which we call security challenges : real-time detection, active attacker, dataset quality and base-rate fallacy. We explain these challenges and then survey the existing phishing/spear phishing solutions in their light. This viewpoint consolidates the literature and illuminates several opportunities for improving existing solutions. We organize the existing literature based on detection techniques for different attack vectors (e.g., URLs, websites, emails) along with studies on user awareness. For detection techniques we examine properties of the dataset, feature extraction, detection algorithms used, and performance evaluation metrics. This work can help guide the development of more effective defenses for phishing, spear phishing and email masquerade attacks of the future, as well as provide a framework for a thorough evaluation and comparison.

SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

Current Stackelberg security game models primarily focus on isolated systems in which only one defender is present, despite being part of a more complex system with multiple players. However, many real systems such as transportation networks and the power grid exhibit interdependencies among targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security scenarios, the authors investigate security games with multiple defenders. Unlike most prior analyses, they focus on situations in which each defender must protect multiple targets, so even a single defender's best response decision is, in general, nontrivial. Considering interdependencies among targets, the authors develop a novel mixed-integer linear programming formulation to compute a defender's best response, and approximate Nash equilibria of the game using this formulation. Their analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or underinvest in security.

Multidefender Security Games

Spear-phishing attacks pose a serious threat to sensitive computer systems, since they sidestep technical security mechanisms by exploiting the carelessness of authorized users. A common way to mitigate such attacks is to use e-mail filters which block e-mails with a maliciousness score above a chosen threshold. Optimal choice of such a threshold involves a tradeoff between the risk from delivered malicious emails and the cost of blocking benign traffic. A further complicating factor is the strategic nature of an attacker, who may selectively target users offering the best value in terms of likelihood of success and resulting access privileges. Previous work on strategic threshold-selection considered a single organization choosing thresholds for all users. In reality, many organizations are potential targets of such attacks, and their incentives need not be well aligned. We therefore consider the problem of strategic threshold-selection by a collection of independent self-interested users. We characterize both Stackelberg multi-defender equilibria, corresponding to short-term strategic dynamics, as well as Nash equilibria of the simultaneous game between all users and the attacker, modeling long-term dynamics, and exhibit a polynomial-time algorithm for computing short-term (Stackelberg) equilibria. We find that while Stackelberg multi-defender equilibrium need not exist, Nash equilibrium always exists, and remarkably, both equilibria are unique and socially optimal.

Multi-defender strategic filtering against spear-phishing attacks

Stackelberg game models of security have received much attention, with a number of approaches for computing Stackelberg equilibria in games with a single defender protecting a collection of targets. In contrast, multi-defender security games have received significantly less attention, particularly when each defender protects more than a single target. We fill this gap by considering a multidefender security game, with a focus on theoretical characterizations of equilibria and the price of anarchy. We present the analysis of three models of increasing generality, two in which each defender protects multiple targets. In all models, we find that the defenders often have the incentive to overprotect the targets, at times significantly. Additionally, in the simpler models, we find that the price of anarchy is unbounded, linearly increasing both in the number of defenders and the number of targets per defender. Surprisingly, when we consider a more general model, this results obtains only in a "corner" case in the space of parameters; in most cases, however, the price of anarchy converges to a constant when the number of defenders increases.

/pdf/equilibrium-analysis-of-multi-defender-security-games-3a6idcz4s9.pdf

Equilibrium analysis of multi-defender security games

Stackelberg security game models and associated computational tools have seen deployment in a number of high-consequence security settings, such as LAX canine patrols and Federal Air Marshal Service. These models focus on isolated systems with only one defender, despite being part of a more complex system with multiple players. Furthermore, many real systems such as transportation networks and the power grid exhibit interdependencies between targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security, we investigate game theoretic models of security games with multiple defenders. Unlike most prior analysis, we focus on the situations in which each defender must protect multiple targets, so that even a single defender's best response decision is, in general, highly non-trivial. We start with an analytical investigation of multidefender security games with independent targets, offering an equilibrium and price-of-anarchy analysis of three models with increasing generality. In all models, we find that defenders have the incentive to over-protect targets, at times significantly. Additionally, in the simpler models, we find that the price of anarchy is unbounded, linearly increasing both in the number of defenders and the number of targets per defender. Considering interdependencies among targets, we develop a novel mixed-integer linear programming formulation to compute a defender's best response, and make use of this formulation in approximating Nash equilibria of the game. We apply this approach towards computational strategic analysis of several models of networks representing interdependencies, including real-world power networks. Our analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or under-invest in security.

Complex traffic networks include a number of controlled intersections, and, commonly, multiple districts or municipalities. The result is that the overall traffic control problem is extremely complex computationally. Moreover, given that different municipalities may have distinct, non-aligned, interests, traffic light controller design is inherently decentralized, a consideration that is almost entirely absent from related literature. Both complexity and decentralization have great bearing both on the quality of the traffic network overall, as well as on its security. We consider both of these issues in a dynamic traffic network. First, we propose an effective local search algorithm to efficiently design system-wide control logic for a collection of intersections. Second, we propose a game theoretic (Stackelberg game) model of traffic network security in which an attacker can deploy denial-of-service attacks on sensors, and develop a resilient control algorithm to mitigate such threats. Finally, we propose a game theoretic model of decentralization, and investigate this model both in the context of baseline traffic network design, as well as resilient design accounting for attacks. Our methods are implemented and evaluated using a simple traffic network scenario in SUMO.

Decentralization and security in dynamic traffic light control

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Multidefender Security Games

Multi-defender strategic filtering against spear-phishing attacks

Equilibrium analysis of multi-defender security games

Multidefender Security Games

Decentralization and security in dynamic traffic light control