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A wide variety of mechanisms, such as alert triggers and auditing routines, have been developed to notify administra- tors about types of suspicious activities in the daily use of large databases of personal and sensitive information. However, such mechanisms are limited in that: 1) the volume of such alerts is often substantially greater than the capabilities of resource- constrained organizations and 2) strategic attackers may disguise their actions or carefully choose which records they touch, thus evading auditing routines. To address these problems, we introduce a novel approach to database auditing that explicitly accounts for adversarial behavior by 1) prioritizing the order in which types of alerts are investigated and 2) providing an upper bound on how much resource to allocate for auditing each alert type. We model the interaction between a database auditor and potential attackers as a Stackelberg game in which the auditor chooses an auditing policy and attackers choose which records in a database to target. We further introduce an efficient approach that combines linear programming, column generation, and heuristic search to derive an auditing policy, in the form of a mixed strategy. We assess the performance of the policy selection method using a publicly available credit card application dataset, the results of which indicate that our method produces high-quality database audit policies, significantly outperforming baselines that are not based in a game theoretic framing. 

Election control considers the problem of an adversary who attempts to tamper with a voting process, in order to either ensure that their favored candidate wins (constructive control) or another candidate loses (destructive control). As online social networks have become significant sources of information for potential voters, a new tool in an attacker’s arsenal is to effect control by harnessing social influence, for example, by spreading fake news and other forms of misinformation through online social media. We consider the computational problem of election control via social influence, studying the conditions under which finding good adversarial strategies is computationally feasible. We consider two objectives for the adversary in both the constructive and destructive control settings: probability and margin of victory (POV and MOV, respectively). We present several strong negative results, showing, for example, that the problem of maximizing POV is inapproximable for any constant factor. On the other hand, we present approxima- tion algorithms which provide somewhat weaker approximation guarantees, such as bicriteria approximations for the POV objective and constant-factor approximations for MOV. Finally, we present mixed integer programming formulations for these problems. Ex- perimental results show that our approximation algorithms often find near-optimal control strategies, indicating that election control through social influence is a salient threat to election integrity. 

An important way cyber adversaries ind vulnerabilities in mod- ern networks is through reconnaissance, in which they attempt to identify coniguration speciics of network hosts. To increase un- certainty of adversarial reconnaissance, the network administrator (henceforth, defender) can introduce deception into responses to network scans, such as obscuring certain system characteristics. We introduce a novel game theoretic model of deceptive interac- tions of this kind between a defender and a cyber attacker, which we call the Cyber Deception Game. We consider both a powerful (rational) attacker, who is aware of the defender’s exact deception strategy, and a naive attacker who is not. We show that computing the optimal deception strategy is NP-hard for both types of attackers. For the case with a powerful attacker, we provide a mixed-integer linear program solution as well as a fast and efective greedy algo- rithm. Similarly, we provide complexity results and propose exact and heuristic approaches when the attacker is naive. Our exten- sive experimental analysis demonstrates the efectiveness of our approaches. 

The spread of unwanted or malicious content through social me- dia has become a major challenge. Traditional examples of this include social network spam, but an important new concern is the propagation of fake news through social media. A common ap- proach for mitigating this problem is by using standard statistical classi cation to distinguish malicious (e.g., fake news) instances from benign (e.g., actual news stories). However, such an approach ignores the fact that malicious instances propagate through the network, which is consequential both in quantifying consequences (e.g., fake news di using through the network), and capturing de- tection redundancy (bad content can be detected at di erent nodes). An additional concern is evasion attacks, whereby the generators of malicious instances modify the nature of these to escape detection. We model this problem as a Stackelberg game between the defender who is choosing parameters of the detection model, and an attacker, who is choosing both the node at which to initiate malicious spread, and the nature of malicious entities. We develop a novel bi-level programming approach for this problem, as well as a novel solution approach based on implicit function gradients, and experimentally demonstrate the advantage of our approach over alternatives which ignore network structure. 

Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the ROSETTA software suite with machine learning and integer linear programming to overcome limitations in the ROSETTA sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in ROSETTA and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems.