DDoS Defense Method in Software-Defined Space-Air-Ground Network from Dynamic Bayesian Game Perspective

The centralized management of Software-Defined Network (SDN) brings convenience to Space-Air-Ground Integrated Networks (SAGIN), which also makes it vulnerable to Distributed Denial of Service (DDoS). At present, the popular detection methods are based on machine learning, but most of them are fixed detection strategies with high overhead and real-time control, so the efficiency is not high. This paper designs different defense methods for different DDoS attacks and constructs a multitype DDoS defense model based on a dynamic Bayesian game in the Software-Defined Space-Air-Ground Integrated Networks (SD-SAGIN). The proposed game model’s Nash equilibrium is solved based on the different costs and payoffs of each method. We simulated the attack and defense of DDoS in Ryu controller and Mininet. The results show that, under our model, the attacker and defender’s strategies are in a dynamic balance, and the controller can effectively reduce the defense cost while ensuring detection accuracy. Compared with the existing traditional Support Vector Machine (SVM) defense method, the performance of the proposed method is better, and it provides one of the references for DDoS defense in SD-SAGIN.

Price negotiation and coordination in outsourcing supply chain under yield and demand uncertainties

This paper considers the issues of pricing, lot-sizing decisions and coordination in a supply chain consisting of one original equipment manufacturer (OEM) and one contract manufacturer (CM). A Bayesian game accounting for asymmetric information is established to optimize the CM’s outsourcing price and the OEM’s selling price. A Stackelberg game incorporating yield and demand uncertainties is subsequently modeled to optimize the CM’s production quantity and the OEM’s order quantity. Finally, a shortage penalty with surplus purchase contract is proposed to coordinate the supply chain. It is found that the optimal outsourcing price is either the lower limit or the stationary point of the common price domain, while the optimal selling price is the upper limit. Whether the CM adopts a conservative or an aggressive production strategy depends on the threshold of the outsourcing price. Moreover, the coordination contract offers great flexibility in parameter selection. By setting the order quantity, penalty price and surplus purchase price properly, the supply chain can realize a win-win situation.

Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email: [email protected]

A BAYESIAN GAME FOR A PROFIT AND LOSS SHARING CONTRACT

This paper presents a Bayesian Game model for a profit-and-loss sharing (PLS) contract. We develop our model into two parts, namely the model for non-social bank and the model for social bank. We propose the model to reduce adverse selection problem in offering a PLS contract. The Bayesian game starts with an incomplete information. Islamic banks do not know exactly what type of agent is applying for a PLS contract, efficient or non-efficient, the information of the bank is incomplete. In Bayesian game, we assume that the Islamic Bank assigns the agent type with a prior probability. Determination of the profit-sharing ratio of the contract will be discussed. We look for the Bayesian Nash equilibrium of the game in our model which is considered a solution. We show that the bank offers an interesting but risky contract to the agent if the bank assigns that the agent is efficient with a high probability, otherwise the bank offers a less risky contract to the agent if the bank assigns that the agent is a non-efficient agent with high probability. The results can be considered by Islamic banks to reduce the adverse selection problem in PLS contract.

Equilibria Existence in Bayesian Games: Climbing the Countable Borel Equivalence Relation Hierarchy

The solution concept of a Bayesian equilibrium of a Bayesian game is inherently an interim concept. The corresponding ex ante solution concept has been termed a Harsányi equilibrium; examples have appeared in the literature showing that there are Bayesian games with uncountable state spaces that have no Bayesian approximate equilibria but do admit a Harsányi approximate equilibrium, thus exhibiting divergent behaviour in the ex ante and interim stages. Smoothness, a concept from descriptive set theory, has been shown in previous works to guarantee the existence of Bayesian equilibria. We show here that higher rungs in the countable Borel equivalence relation hierarchy can also shed light on equilibrium existence. In particular, hyperfiniteness, the next step above smoothness, is a sufficient condition for the existence of Harsányi approximate equilibria in purely atomic Bayesian games.

Game-Theoretic Decision Support for Cyber Forensic Investigations

The use of anti-forensic techniques is a very common practice that stealthy adversaries may deploy to minimise their traces and make the investigation of an incident harder by evading detection and attribution. In this paper, we study the interaction between a cyber forensic Investigator and a strategic Attacker using a game-theoretic framework. This is based on a Bayesian game of incomplete information played on a multi-host cyber forensics investigation graph of actions traversed by both players. The edges of the graph represent players’ actions across different hosts in a network. In alignment with the concept of Bayesian games, we define two Attacker types to represent their ability of deploying anti-forensic techniques to conceal their activities. In this way, our model allows the Investigator to identify the optimal investigating policy taking into consideration the cost and impact of the available actions, while coping with the uncertainty of the Attacker’s type and strategic decisions. To evaluate our model, we construct a realistic case study based on threat reports and data extracted from the MITRE ATT&CK STIX repository, Common Vulnerability Scoring System (CVSS), and interviews with cyber-security practitioners. We use the case study to compare the performance of the proposed method against two other investigative methods and three different types of Attackers.