Multiple Defendants Research Articles

Cooperative defense of a territorial-constrained target in a target-attacker-defender game

Abstract Multi-player pursuit-evasion games are crucial for addressing the maneuver decision problem arising in the cooperative control of multi-agent systems. This paper presents a cooperative defense strategy involving cooperation and confrontation among the target, attacker, and multiple defenders based on location information only. The primary objective of the attacker is to capture the target while avoiding being captured by multiple defenders. Meanwhile, the target is confined to a restricted area and can only move within its boundaries. The proposed cooperative defense strategy aims to prevent the attacker from capturing the target while minimizing the time required to neutralize the threat. Therefore, the multiple defenders are classified into two categories: the primary defender and the auxiliary defenders. The primary defender is to prevent the attacker from approaching the target by predicting the intention of the attacker. On the other hand, the auxiliary defenders adopt a surround-shrink-capture strategy to reduce the time consumption to capture the attacker. Numerical simulations have been conducted to validate the effectiveness of the proposed strategy.

Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing

Edge computing brings computation and storage resources to the edge of the mobile network to solve the problems of low latency and high real-time demand. However, edge computing is more vulnerable to malicious attacks due to its open and dynamic environments. In this article, we investigate security defense strategies in edge computing systems, focusing on scenarios with one attacker and multiple defenders to determine optimal defense strategies with minimal resource allocation. Firstly, we formulate the interactions between the defenders and the attackers as the mean-field Stackelberg game model, where the state and the objective functions of the defenders are coupled through the mean-field term, and are strongly influenced by the strategy of the attacker. Then, we analyze the local optimal strategies of the defenders given an arbitrary strategy of the attackers. We demonstrate the Nash equilibrium and the mean-field equilibrium for both the defenders and the attackers. Finally, simulation analysis will illustrate the dynamic evolution of the defense strategy of the defenders and the trajectory of the attackers based on the proposed Stackelberg game model.

Industrial action in private international law

This contribution deals with both jurisdiction and applicable law with regard to cross-border collective actions in labour law. It demonstrates that the European conflicts rule embodied in Article 9 of the Rome II Regulation is open to diverging interpretations. This can, to a large extent, be explained by the very diverse legal characterisation of industrial action in the national systems of the EU Member States. The connecting factors used in the Rome II Regulation also create specific challenges when applied in the context of industrial action. As a result of these complications, Article 9 Rome II currently fails to fulfil its function of creating legal certainty around the legality and the legal consequences of industrial action with a cross-border element. A further clarification of the scope of Article 9 and the role played by the law of the country in which the industrial action is taken would help to reduce the current confusion and uncertainty. The uncertainty as to the applicable law is exacerbated by the rules on jurisdiction in the Brussels I bis Regulation which allow, to some extent, for forum shopping. Two provisions of the Brussels I bis Regulation might warrant revision to reduce their negative impact on the exercise of the right to industrial action: the rule on multiple defendants (Article 8(1)) and the rule granting jurisdiction to the place where the damage caused by the industrial action is sustained (Article 7(2)).

The impact of AI errors in a human-in-the-loop process

Automated decision-making is becoming increasingly common in the public sector. As a result, political institutions recommend the presence of humans in these decision-making processes as a safeguard against potentially erroneous or biased algorithmic decisions. However, the scientific literature on human-in-the-loop performance is not conclusive about the benefits and risks of such human presence, nor does it clarify which aspects of this human–computer interaction may influence the final decision. In two experiments, we simulate an automated decision-making process in which participants judge multiple defendants in relation to various crimes, and we manipulate the time in which participants receive support from a supposed automated system with Artificial Intelligence (before or after they make their judgments). Our results show that human judgment is affected when participants receive incorrect algorithmic support, particularly when they receive it before providing their own judgment, resulting in reduced accuracy. The data and materials for these experiments are freely available at the Open Science Framework: https://osf.io/b6p4z/ Experiment 2 was preregistered.

Learning Games for Defending Advanced Persistent Threats in Cyber Systems

A cyber system may face to multiple attackers from diverse adversaries, who usually employ sophisticated techniques to both continuously steal sensitive data and avoid being detected by defense strategies. This continuous process is typically involved in an advanced persistent threat (APT). Since the game theory is an ideal mathematical model for investigating continuous decision making of competing players, it is broadly used to research the interaction between defenders and APT attackers. Although many researchers are now using the game theory to defend against APT attacks, most of the existing solutions are limited to single-defender, single-attacker scenarios. In the real world, threats by multiple attackers are not uncommon and multiple defenders can be put in place. Therefore, to overcome the limitation of the existing solutions, we develop a multiagent deep reinforcement learning (MADRL) method with a novel sampling approach. The MADRL method allows defenders to create strategies on the fly and share their experience with other defenders. To develop this method, we create a multidefender, multiattacker game model and analyze the equilibrium of this model. The results of a series of experiments demonstrate that, with MADRL, defenders can quickly learn efficient strategies against attackers.

Dynamic Network Analysis of a Target Defense Differential Game With Limited Observations

In this paper, we study a Target-Attacker-Defender (TAD) differential game involving one attacker, one target and multiple defenders. We consider two variations where (a) the attacker and the target have unlimited observation range and the defenders are visibility constrained (b) only the attacker has unlimited observation range and the remaining players are visibility constrained. We model the players' interactions as a dynamic game with asymmetric information. Here, the visibility constraints of the players induce a visibility network which encapsulates the visibility information during the evolution of the game. Based on this observation, we introduce network adapted feedback or implementable strategies for visibility constrained players. Using inverse game theory approach we obtain network adapted feedback Nash equilibrium strategies. We introduce a consistency criterion for selecting a subset (or refinement) of network adapted feedback Nash strategies, and provide an optimization based approach for computing them. Finally, we illustrate our results with numerical experiments.

The Government as Plaintiff: An Analysis of Medical Litigation Against Healthcare Providers in the Eastern Province of the Kingdom of Saudi Arabia.

The Saudi healthcare and medicolegal systems have seen several developments to improve quality of services and ensure patient safety. However, violation claims have seen an upward trend in the past few years. Several studies have analyzed medical litigation to better understand violations within healthcare sectors and address them accordingly. Such studies have focused extensively on medical litigations filed by patients against healthcare providers, overlooking other plaintiffs, such as governments. In this study, we examined violation claims filed by governmental entities against healthcare providers in the Eastern Province of the Kingdom of Saudi Arabia. From 2016 to 2019, 718 healthcare providers were involved in violation claims that were referred to the Medical Offenses Committee. Two logistic regression analyses were performed to identify significant predictors of receiving a guilty verdict and the verdict type (for guilty verdicts), given defendant-related factors. Eighty-two percent of healthcare providers received a guilty verdict. Guilty verdicts were prevalent among non-Saudis and healthcare providers in the private sector. Healthcare providers working in the private sector were significantly more likely to receive a guilty verdict than healthcare providers working in the public sector. Nurses were significantly less likely to receive a guilty verdict than physicians. Being involved in violation claims with 5 or more codefendants decreased the likelihood of receiving a guilty verdict. In addition, non-Saudis being involved in violation claims with multiple defendants, working in the private sector, and working in pharmacies and medical complexes were significant predictors of the verdict type. Results of the study extend the extant literature by analyzing medical litigation including plaintiffs other than patients. In addition, the study introduces implications for the government and policymakers as well as areas for future research The study's findings emphasized the importance of employment sector and number of defendants as significant predictors of violation claims' verdicts. Policymakers should take into consideration significant predictors to ensure healthcare providers' compliance with respective rules and regulations. Tailored attention toward these predictors could minimize the prevalence of medical violation claims, ensure patient safety, and avoid associated negative consequences.

Defense coordination in security games: Equilibrium analysis and mechanism design

Real-world security scenarios sometimes involve multiple defenders: security agencies of two or more countries might patrol the same border areas, and domestic security agencies might also operate in the same locations when their areas of jurisdiction overlap. Motivated by these scenarios and the observation that uncoordinated movements of the defenders may lead to an inefficient defense, we introduce a model of multi-defender security games and explore the possibility of improving efficiency by coordinating the defenders — specifically, by pooling the defenders' resources and allocating them jointly. The model generalizes the standard model of Stackelberg security games, where a defender (now a group of defenders) allocates security resources to protect a set of targets, and an attacker picks the best target to attack. In particular, we are interested in the situation with heterogeneous defenders, who may value the same target differently. Our task is twofold. First, we need to develop a good understanding of the uncoordinated situation, as the baseline to be improved. To this end we formulate a new equilibrium concept, and prove that an equilibrium under this concept always exists and can be computed efficiently. Second, to coordinate the heterogeneous defenders we take a mechanism design perspective and aim to find a mechanism to generate joint resource allocation strategies. We seek a mechanism that improves the defenders' utilities upon the uncoordinated baseline, achieves Pareto efficiency, and incentivizes the defenders to report their true incentives and execute the recommended strategies. Our analysis establishes several impossibility results, which indicate the intrinsic difficulties of defense coordination. Specifically, we show that even the basic properties listed above are in conflict with each other: no mechanism can simultaneously satisfy them all, or even some proper subsets of them. In terms of positive results, we present mechanisms that satisfy all combinations of the properties that are not ruled out by our impossibility results, thereby providing a comprehensive profile of the mechanism design problem with respect to the properties considered.

Make or Buy? The Provision of Indigent Defense Services in the United States

Abstract Most criminal defendants cannot afford to hire an attorney. To provide constitutionally mandated legal services, states commonly use either private court-appointed attorneys or a public defender organization. This paper investigates the relative efficacy of these two modes of indigent defense by comparing outcomes of codefendants assigned to different types of attorneys within the same case. Using data from San Francisco, I show that in multiple defendant cases, public defender assignment is plausibly as good as random. I find that public defenders reduce the probability of any prison sentence by 22% and the length of prison sentences by 10%.

A VNS-based metaheuristic approach for escape interdiction on transportation networks

Containment of dynamic crimes such as kidnapping, bank robbery, hit-and-run, etc., is a challenging issue for law enforcement agencies as the criminal changes his location with time. This type of scenario is generally modelled as an escape interdiction game. We consider two types of players, namely multiple defenders and a single attacker. The defenders choose a sequence of junctions to protect, while the attacker chooses an escape path and the travel time on distinct edges along the path. Hence, both the defender strategy space and the continuous attacker strategy space suffer an exponential growth. This leads to major challenges in the computation of the optimal solution with respect to each player. We develop a double oracle Variable Neighborhood Search (VNS) based meta-heuristic algorithm for finding near-optimal defender and attacker strategies in a time efficient manner. We test the efficiency of this approach against an established exact algorithm named Escape Interdiction Game Solver (EIGS). We use EIGS to generate the benchmark results to check the performance of our heuristic. We also show that the performance of our approach is reasonably good under strict time limitations, as observed in practice.

Efficient defense strategy against spam and phishing email: An evolutionary game model

Spam and phishing emails are never-ending problems, and they are increasingly distributed with the help of the botnet. Existing spam and phishing email defense depends heavily on the spam filtering technique. However, using the spam filtering technique needs many defensive resources, which are usually limited. Therefore, to achieve the maximum effect with limited defense resources, we answer the following questions in this paper: How many user nodes we should protect in a target network? What factors affect this and how? To solve these problems, we propose an evolutional game model between multiple attackers and multiple defenders and analyze the evolution process of their strategies. The optimal number of users protected is first obtained as an efficient defense strategy, which can reduce resource consumption and lower attack density. Then we discover that this defense strategy is affected by the email opening probability of users, the attack cost, and the attack loss. Our results can help email network managers deploy the spam filtering technique according to the email opening probability, of which the defender has an asymmetric information advantage. Furthermore, we study the influences of the attack cost and loss on the defense strategy. It is revealed that the attack cost has a more marked effect on the defense strategy compared to the attack loss. Finally, we experiment on a large real-world dataset of emails sent in 2-year time duration. The defense strategy is proven to be effective for maximizing defense resources.

Revenue maximizing markets for zero-day exploits

Markets for zero-day exploits (software vulnerabilities unknown to the software vendor) have a long history and a growing popularity. We study these markets from a revenue-maximizing mechanism design perspective. We first propose a theoretical model for zero-day exploits markets. In our model, one exploit is being sold to multiple buyers. There are two kinds of buyers, which we call the defenders and the offenders. The defenders are buyers who buy vulnerabilities in order to fix them (e.g., software vendors). The offenders, on the other hand, are buyers who intend to utilize the exploits (e.g., national security agencies and police). We study the problem of selling one zero-day exploit to multiple defenders and offenders. Our model has a few unique features that make it different from single-item auctions. First, an exploit is a piece of information, so one exploit can be sold to multiple buyers. Second, buyers have externalities. If any defender wins, then the exploit becomes worthless to the offenders. Third, if the auctioneer discloses the details of the exploit to the buyers before the auction, then they may leave with the information without paying. On the other hand, if the auctioneer does not disclose enough details, then the buyers cannot determine how valuable the exploit is. Considering the above, our proposed mechanism discloses the details of the exploit to all offenders at the beginning of the auction. The defenders will receive the information slightly delayed. The offenders bid to prolong the delay and the defenders bid to shorten the delay. We derive the optimal mechanism for single-parameter valuations. For general valuations, we propose three numerical solution techniques. One is based on iterative linear programming and the other two are based on neural networks and evolutionary computation.

Security‐aware optimal actuator placement in vehicle platooning

AbstractVehicle platooning, as a large class of cyber‐physical systems, is prone to be under the risk of cyber attacks. One (or more) external intelligent intruder(s) might attack one (or more) of the vehicles participating in a platoon. This paper proposes a general approach to find an optimal actuator placement strategy according to the Stackelberg game between the attacker and the defender. The game payoff is the energy needed by the attacker to steer the consensus follower–leader dynamics of the system towards his desired direction. The attacker tries to minimize this energy while the defender attempts to maximize it. Thus, based on the defined game and its optimal equilibrium point, the defender(s) selects optimal actuator placement action to face the attacker(s). Both cases of single attacker–single defender and multiple attackers–multiple defenders cases are investigated. Furthermore, we study the effects of different information flow topologies, namely, the unidirectional and bidirectional data transfer structures. Besides, the impacts of increasing the connectivity among the nodes on the security level of the platoon are presented. Simulation results for h–nearest neighbor platoon formations along with experimental results using the scaled cars governed by robotic operating system (ROS) verify the effectiveness of the method.

Coalition Formation in Multi-defender Security Games

We study Stackelberg security game (SSG) with multiple defenders, where heterogeneous defenders need to allocate security resources to protect a set of targets against a strategic attacker. In such games, coordination and cooperation between the defenders can increase their ability to protect their assets, but the heterogeneous preferences of the self-interested defenders often make such cooperation very difficult. In this paper, we approach the problem from the perspective of cooperative game theory and study coalition formation among the defenders. Our main contribution is a number of algorithmic results for the computation problems that arise in this model. We provide a poly-time algorithm for computing a solution in the core of the game and show that all of the elements in the core are Pareto efficient. We show that the problem of computing the entire core is NP-hard and then delve into a special setting where the size of a coalition is limited up to some threshold. We analyse the parameterized complexity of deciding if a coalition structure is in the core under this special setting, and provide a poly-time algorithm for computing successful deviation strategies for a given coalition.

Disrupting Frivolous Defenses

Judge Milton I. Shadur was a disrupter of frivolous defenses. In 2018, Judge Shadur died at the age of 93 after 37 years as a judge of the United States District Court for the Northern District of Illinois. Sua sponte, Judge Shadur reviewed civil answers, and disrupted the pleading of frivolous defenses. Sua Sponte Shadur—as some lawyers called him—rejected answers that departed from or ignored Rule 8 of the Federal Rules of Civil Procedure. In 2001, Judge Shadur issued an Appendix to an order in State Farm v. Riley, 199 F.R.D. 276 (N.D. Ill. 2001) with his expectations regarding how defense lawyers were to answer according to Rule 8 of the Federal Rules of Civil Procedure. As the Appendix makes clear, Judge Shadur disallowed pleaders to dodge Rule 8 by 1) claiming that an allegation called for a legal conclusion, 2) claiming that documents speak for themselves; and 3) demanding strict proof. Judge Shadur also expected that affirmative defenses (Or ADs, as he called them) would be true affirmative defenses and that defense attorneys would support affirmative defenses with foundational or predicate facts. The Appendix, now 20 years old, is living a rich life. Judges have cited Judge Shadur’s order and appendix 233 times. Litigants have cited the case 452 times in trial-court motions and filings as well as 14 times in appellate matters. Westlaw reports 52 citations in secondary sources and another 13 in a directory West calls “Practical Law.” After presenting a minibiography of Judge Shadur, the backstory of State Farm v. Riley, and a gloss on the Appendix, this article tracks Appendix use by federal judges throughout the United States. Specifically, the article focuses on legal conclusions, speaking documents, and affirmative defenses. The article organizes the cases by federal circuit. The organization should be interesting to academics and useful to attorneys preparing F.R.C.P. 12 motions to strike affirmative defenses, motions to deem allegations admitted, or motions for more definite statement. The article first covers the citation of the Appendix first by Judge Shadur himself, next by Northern District of Illinois colleagues, then within the broader Seventh Circuit, and finally outside his Circuit. Last are the few citations in state courts finishing, though, with the epitome of Shadur’s Appendix, which was a rule change by the Arizona Supreme Court in 2018, the year of Shadur’s death. Arizona’s Rule 8 now aligns with Shadur’s Appendix. This article meshes with the empirical analysis of my related article Frivolous Defenses. In that piece, I examine car-crash answers and complaints in the state courts of Colorado, where I live, teach, and have practiced law. For 298 lawsuits, I retrieved and read the complaints and corresponding 356 answers, which number more than 298 because a number of suits had multiple defendants. Frivolous Defenses examines the departures from Colorado’s Rule 8 using, among other things, Judge Shadur’s Appendix and analysis. I welcome comment and suggestions by professors, attorneys, and judges concerning either of these articles.

Multi-Agent Cooperative Pursuit-Defense Strategy Against One Single Attacker

Multiple-player games involving cooperative and adversarial agents are a type of problems of great practical significance. In this letter, we consider an attack-defense game with a single attacker and multiple defenders. The attacker attempts to enter a protected region, while the defenders attempt to defend the same region and capture the attacker outside the region. We propose a distributed pursuit-defense strategy for the defenders' cooperative defense against the attacker. Inside a bounded, convex, two-dimensional space, the defenders choose among an area-decreasing, a distance-decreasing, or a pursuing strategy. We prove that our strategy guarantees the attacker to be captured before entering the protected region in a finite time. We also demonstrate with simulations that a human-controlled attacker is unable to enter a protected region when multiple defenders are using our pursuit-defense strategy.

Behavioral and Game-Theoretic Security Investments in Interdependent Systems Modeled by Attack Graphs

In this article, we consider a system consisting of multiple interdependent assets, and a set of defenders, each responsible for securing a subset of the assets against an attacker. The interdependencies between the assets are captured by an attack graph, where an edge from one asset to another indicates that if the former asset is compromised, an attack can be launched on the latter asset. Each edge has an associated probability of successful attack, which can be reduced via security investments by the defenders. In such scenarios, we investigate the security investments that arise under certain features of human decision making that have been identified in behavioral economics. In particular, humans have been shown to perceive probabilities in a nonlinear manner, typically overweighting low probabilities and underweighting high probabilities. We show that suboptimal investments can arise under such weighting in certain network topologies. We also show that pure strategy Nash equilibria exist in settings with multiple (behavioral) defenders, and study the inefficiency of the equilibrium investments by behavioral defenders compared to a centralized socially optimal solution.

The European multiple patent defendants rule as a legal constraint for start-ups and growth companies

The European multiple patent defendants rule as a legal constraint for start-ups and growth companies

Apportionment of liability by the stochastic Shapley value

If multiple defendants are jointly liable for a plaintiff's harm, the court must determine the apportionments of the damages among them. Recently, in a series of papers, Dehez and Ferey (2013) and Ferey and Dehez (2016a,b) took a cooperative game theory approach, and used the Shapley value and the weighted Shapley value to determine the shares especially in the case of sequential acts. In this paper, we argue that their allocation rule is not strictly fair if we take even a small random error into account. We alternatively propose the stochastic Shapley value which extends the definition of the Shapley value to stochastic cooperative games and show that it satisfies ex post efficiency, symmetry, dummy, feasibility and fairness.

Report on the Bronx 120 Mass 'Gang' Prosecution

In recent years, takedowns of gangs and crews in New York City have led to mass prosecutions of multiple defendants for conspiracy and RICO (Racketeering Influenced Corrupt Organization Act) conspiracy charges. While the takedowns are generally accompanied by intensive media coverage, information about the charges, process, and allegations against individuals caught up in these takedowns is not readily accessible. This report looks into the court records of the largest of the mass gang prosecutions — the takedown of the Bronx 120 in April of 2016. In the pre-dawn hours of April 27, 2016, nearly 700 officers in riot gear descended upon Eastchester Gardens and the adjoining neighborhood along White Plains Road. The officers were from the NYPD, ATF, DEA, and Homeland Security. Helicopters hovered overhead as armed SWAT teams used battering rams to execute no-knock warrants. They were arresting defendants charged in twin conspiracy indictments. The defendants and the raid have come to be known as the Bronx 120 for the 120 defendants named in the indictments. Preet Bharara, then-United States Attorney for the Southern District of New York, announced that the raid was believed to be the “largest gang takedown in New York City history.” The report concludes that nearly half of the 120 individuals targeted in these “gang” indictments were not alleged to be gang members by the prosecution, two-thirds of the defendants were not convicted based on violent conduct (including 35 for who were convicted based on marijuana sales), and two-thirds of defendants had no prior felony record, and nearly half were re-prosecuted for conduct previously adjudicated in state courts. Although many of the defendants were not violent gang members, the vast majority (101 of the 120) were held without the possibility of bail, and 15 of the 19 who were released were subject to house arrest. Further, these mass indictments fail to safeguard the public’s interest in transparency and speedy trial.