Hosseinpourpia, M., Oskoei, M. A..
2017.
GA Based Parameter Estimation for Multi-Faceted Trust Model of Recommender Systems. 2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). :160–165.
Recommender system is to suggest items that might be interest of the users in social networks. Collaborative filtering is an approach that works based on similarity and recommends items liked by other similar users. Trust model adopts users' trust network in place of similarity. Multi-faceted trust model considers multiple and heterogeneous trust relationship among the users and recommend items based on rating exist in the network of trustees of a specific facet. This paper applies genetic algorithm to estimate parameters of multi-faceted trust model, in which the trust weights are calculated based on the ratings and the trust network for each facet, separately. The model was built on Epinions data set that includes consumers' opinion, rating for items and the web of trust network. It was used to predict users' rating for items in different facets and root mean squared of prediction error (RMSE) was considered as a measure of performance. Empirical evaluations demonstrated that multi-facet models improve performance of the recommender system.
Wu, J. M.-T., Srivastava, G., Pirouz, M., Lin, J. C.-W..
2020.
A GA-based Data Sanitization for Hiding Sensitive Information with Multi-Thresholds Constraint. 2020 International Conference on Pervasive Artificial Intelligence (ICPAI). :29—34.
In this work, we propose a new concept of multiple support thresholds to sanitize the database for specific sensitive itemsets. The proposed method assigns a stricter threshold to the sensitive itemset for data sanitization. Furthermore, a genetic-algorithm (GA)-based model is involved in the designed algorithm to minimize side effects. In our experimental results, the GA-based PPDM approach is compared with traditional compact GA-based model and results clearly showed that our proposed method can obtain better performance with less computational cost.
Chen, T., Lin, T., Hong, Y.- P..
2020.
Gait Phase Segmentation Using Weighted Dynamic Time Warping and K-Nearest Neighbors Graph Embedding. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1180–1184.
Gait phase segmentation is the process of identifying the start and end of different phases within a gait cycle. It is essential to many medical applications, such as disease diagnosis or rehabilitation. This work utilizes inertial measurement units (IMUs) mounted on the individual's foot to gather gait information and develops a gait phase segmentation method based on the collected signals. The proposed method utilizes a weighted dynamic time warping (DTW) algorithm to measure the distance between two different gait signals, and a k-nearest neighbors (kNN) algorithm to obtain the gait phase estimates. To reduce the complexity of the DTW-based kNN search, we propose a neural network-based graph embedding scheme that is able to map the IMU signals associated with each gait cycle into a distance-preserving low-dimensional representation while also producing a prediction on the k nearest neighbors of the test signal. Experiments are conducted on self-collected IMU gait signals to demonstrate the effectiveness of the proposed scheme.
Li, Yan, Zhu, Ting.
2016.
Gait-Based Wi-Fi Signatures for Privacy-Preserving. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :571–582.
With the advent of the Internet of Things (IoT) and big data, high fidelity localization and tracking systems that employ cameras, RFIDs, and attached sensors intrude on personal privacy. However, the benefit of localization information sharing enables trend forecasting and automation. To address this challenge, we introduce Wobly, an attribute based signature (ABS) that measures gait. Wobly passively receives Wi-Fi beacons and produces human signatures based on the Doppler Effect and multipath signals without attached devices and out of direct line-of-sight. Because signatures are specific to antenna placement and room configuration and do not require sensor attachments, the identities of the individuals can remain anonymous. However, the gait based signatures are still unique, and thus Wobly is able to track individuals in a building or home. Wobly uses the physical layer channel and the unique human gait as a means of encoding a person's identity. We implemented Wobly on a National Instruments Radio Frequency (RF) test bed. Using a simple naive Bayes classifier, the correct identification rate was 87% with line-of-sight (LoS) and 77% with non-line-of-sight (NLoS).
Taha, Mohammad Bany, Chowdhury, Rasel.
2020.
GALB: Load Balancing Algorithm for CP-ABE Encryption Tasks in E-Health Environment. 2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). :165–170.
Security of personal data in the e-healthcare has always been challenging issue. The embedded and wearable devices used to collect these personal and critical data of the patients and users are sensitive in nature. Attribute-Based Encryption is believed to provide access control along with data security for distributed data among multiple parties. These resources limited devices do have the capabilities to secure the data while sending to the cloud but instead it increases the overhead and latency of running the encryption algorithm. On the top of if confidentiality is required, which will add more latency. In order to reduce latency and overhead, we propose a new load balancing algorithm that will distribute the data to nearby devices with available resources to encrypt the data and send it to the cloud. In this article, we are proposing a load balancing algorithm for E-Health system called (GALB). Our algorithm is based on Genetic Algorithm (GA). Our algorithm (GALB) distribute the tasks that received to the main gateway between the devices on E-health environment. The distribution strategy is based on the available resources in the devices, the distance between the gateway and the those devices, and the complexity of the task (size) and CP-ABE encryption policy length. In order to evaluate our algorithm performance, we compare the near optimal solution proposed by GALB with the optimal solution proposed by LP.
Apolinarski, W., Iqbal, U., Parreira, J.X..
2014.
The GAMBAS middleware and SDK for smart city applications. Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on. :117-122.
The concept of smart cities envisions services that provide distraction-free support for citizens. To realize this vision, the services must adapt to the citizens' situations, behaviors and intents at runtime. This requires services to gather and process the context of their users. Mobile devices provide a promising basis for determining context in an automated manner on a large scale. However, despite the wide availability of versatile programmable mobile platforms such as Android and iOS, there are only few examples of smart city applications. One reason for this is that existing software platforms primarily focus on low-level resource management which requires application developers to repeatedly tackle many challenging tasks. Examples include efficient data acquisition, secure and privacy-preserving data distribution as well as interoperable data integration. In this paper, we describe the GAMBAS middleware which tries to simplify the development of smart city applications. To do this, GAMBAS introduces a Java-based runtime system with an associated software development kit (SDK). To clarify how the runtime system and the SDK can be used for application development, we describe two simple applications that highlight different middleware functions.
Portaluri, G., Giordano, S..
2020.
Gambling on fairness: a fair scheduler for IIoT communications based on the shell game. 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). :1–6.
The Industrial Internet of Things (IIoT) paradigm represents nowadays the cornerstone of the industrial automation since it has introduced new features and services for different environments and has granted the connection of industrial machine sensors and actuators both to local processing and to the Internet. One of the most advanced network protocol stack for IoT-IIoT networks that have been developed is 6LoWPAN which supports IPv6 on top of Low-power Wireless Personal Area Networks (LoWPANs). 6LoWPAN is usually coupled with the IEEE 802.15.4 low-bitrate and low-energy MAC protocol that relies on the time-slotted channel hopping (TSCH) technique. In TSCH networks, a coordinator node synchronizes all end-devices and specifies whether (and when) they can transmit or not in order to improve their energy efficiency. In this scenario, the scheduling strategy adopted by the coordinator plays a crucial role that impacts dramatically on the network performance. In this paper, we present a novel scheduling strategy for time-slot allocation in IIoT communications which aims at the improvement of the overall network fairness. The proposed strategy mimics the well-known shell game turning the totally unfair mechanics of this game into a fair scheduling strategy. We compare our proposal with three allocation strategies, and we evaluate the fairness of each scheduler showing that our allocator outperforms the others.
Bian, Yuan, Lin, Haitao, Song, Yuecai.
2022.
Game model of attack and defense for underwater wireless sensor networks. 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 10:559–563.
At present, the research on the network security problem of underwater wireless sensors is still few, and since the underwater environment is exposed, passive security defense technology is not enough to deal with unknown security threats. Aiming at this problem, this paper proposes an offensive and defensive game model from the finite rationality of the network attack and defense sides, combined with evolutionary game theory. The replicated dynamic equation is introduced to analyze the evolution trend of strategies under different circumstances, and the selection algorithm of optimal strategy is designed, which verifies the effectiveness of this model through simulation and provides guidance for active defense technology.
ISSN: 2693-2865
Nasr, Milad, Houmansadr, Amir.
2016.
GAME OF DECOYS: Optimal Decoy Routing Through Game Theory. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :1727–1738.
Decoy routing is a promising new approach for censorship circumvention that relies on traffic re-direction by volunteer autonomous systems. Decoy routing is subject to a fundamental censorship attack, called routing around decoy (RAD), in which the censors re-route their clients' Internet traffic in order to evade decoy routing autonomous systems. Recently, there has been a heated debate in the community on the real-world feasibility of decoy routing in the presence of the RAD attack. Unfortunately, previous studies rely their analysis on heuristic-based mechanisms for decoy placement strategies as well as ad hoc strategies for the implementation of the RAD attack by the censors. In this paper, we perform the first systematic analysis of decoy routing in the presence of the RAD attack. We use game theory to model the interactions between decoy router deployers and the censors in various settings. Our game-theoretic analysis finds the optimal decoy placement strategies–-as opposed to heuristic-based placements–-in the presence of RAD censors who take their optimal censorship actions–-as opposed to some ad hoc implementation of RAD. That is, we investigate the best decoy placement given the best RAD censorship. We consider two business models for the real-world deployment of decoy routers: a central deployment that resembles that of Tor and a distributed deployment where autonomous systems individually decide on decoy deployment based on their economic interests. Through extensive simulation of Internet routes, we derive the optimal strategies in the two models for various censoring countries and under different assumptions about the budget and preferences of the censors and decoy deployers. We believe that our study is a significant step forward in understanding the practicality of the decoy routing circumvention approach.
Rullo, Antonino, Midi, Daniele, Serra, Edoardo, Bertino, Elisa.
2017.
A Game of Things: Strategic Allocation of Security Resources for IoT. Proceedings of the Second International Conference on Internet-of-Things Design and Implementation. :185–190.
In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost, as many IoT devices may not have sufficient resources to host expensive security tools. In this paper, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security tools. We model our problem according to game theory, and provide a Pareto-optimal solution, in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack, are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies.
Hu, Qin, Wang, Shengling, Cheng, Xiuzhen.
2019.
A Game Theoretic Analysis on Block Withholding Attacks Using the Zero-Determinant Strategy. 2019 IEEE/ACM 27th International Symposium on Quality of Service (IWQoS). :1–10.
In Bitcoin's incentive system that supports open mining pools, block withholding attacks incur huge security threats. In this paper, we investigate the mutual attacks among pools as this determines the macroscopic utility of the whole distributed system. Existing studies on pools' interactive attacks usually employ the conventional game theory, where the strategies of the players are considered pure and equal, neglecting the existence of powerful strategies and the corresponding favorable game results. In this study, we take advantage of the Zero-Determinant (ZD) strategy to analyze the block withholding attack between any two pools, where the ZD adopter has the unilateral control on the expected payoffs of its opponent and itself. In this case, we are faced with the following questions: who can adopt the ZD strategy? individually or simultaneously? what can the ZD player achieve? In order to answer these questions, we derive the conditions under which two pools can individually or simultaneously employ the ZD strategy and demonstrate the effectiveness. To the best of our knowledge, we are the first to use the ZD strategy to analyze the block withholding attack among pools.
Lakhdhar, Y., Rekhis, S., Sabir, E..
2020.
A Game Theoretic Approach For Deploying Forensic Ready Systems. 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). :1–6.
Cyber incidents are occurring every day using various attack strategies. Deploying security solutions with strong configurations will reduce the attack surface and improve the forensic readiness, but will increase the security overhead and cost. In contrast, using moderate or low security configurations will reduce that overhead, but will inevitably decrease the investigation readiness. To avoid the use of cost-prohibitive approaches in developing forensic-ready systems, we present in this paper a game theoretic approach for deploying an investigation-ready infrastructure. The proposed game is a non-cooperative two-player game between an adaptive cyber defender that uses a cognitive security solution to increase the investigation readiness and reduce the attackers' untraceability, and a cyber attacker that wants to execute non-provable attacks with a low cost. The cognitive security solution takes its strategic decision, mainly based on its ability to make forensic experts able to differentiate between provable identifiable, provable non-identifiable, and non-provable attack scenarios, starting from the expected evidences to be generated. We study the behavior of the two strategic players, looking for a mixed Nash equilibrium during competition and computing the probabilities of attacking and defending. A simulation is conducted to prove the efficiency of the proposed model in terms of the mean percentage of gained security cost, the number of stepping stones that an attacker creates and the rate of defender false decisions compared to two different approaches.
Sahabandu, Dinuka, Moothedath, Shana, Bushnell, Linda, Poovendran, Radha, Aller, Joey, Lee, Wenke, Clark, Andrew.
2019.
A Game Theoretic Approach for Dynamic Information Flow Tracking with Conditional Branching. 2019 American Control Conference (ACC). :2289–2296.
In this paper, we study system security against Advanced Persistent Threats (APTs). APTs are stealthy and persistent but APTs interact with system and introduce information flows in the system as data-flow and control-flow commands. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism against APTs which taints suspicious input sources in the system and performs online security analysis when a tainted information is used in unauthorized manner. Our objective in this paper is to model DIFT that handle data-flow and conditional branches in the program that arise from control-flow commands. We use game theoretic framework and provide the first analytical model of DIFT with data-flow and conditional-branch tracking. Our game model which is an undiscounted infinite-horizon stochastic game captures the interaction between APTs and DIFT and the notion of conditional branching. We prove that the best response of the APT is a maximal reachability probability problem and provide a polynomial-time algorithm to find the best response by solving a linear optimization problem. We formulate the best response of the defense as a linear optimization problem and show that an optimal solution to the linear program returns a deterministic optimal policy for the defense. Since finding Nash equilibrium for infinite-horizon undiscounted stochastic games is computationally difficult, we present a nonlinear programming based polynomial-time algorithm to find an E-Nash equilibrium. Finally, we perform experimental analysis of our algorithm on real-world data for NetRecon attack augmented with conditional branching.
Belavagi, Manjula C, Muniyal, Balachandra.
2016.
Game theoretic approach towards intrusion detection. 2016 International Conference on Inventive Computation Technologies (ICICT). 1:1–5.
Today's network is distributed and heterogeneous in nature and has numerous applications which affect day to day life, such as e-Banking, e-Booking of tickets, on line shopping etc. Hence the security of the network is crucial. Threats in the network can be due to intrusions. Such threats can be observed and handled using Intrusion Detection System. The security can be achieved using intrusion detection system, which observes the data traffic and identifies it as an intrusion or not. The objective of this paper is to design a model using game theoretic approach for intrusion detection. Game model is designed by defining players, strategies and utility functions to identify the Probe attacks. This model is tested with NSLKDD data set. The model is the Probe attacks are identified by dominated strategies elimination method. Experimental results shows that game model identifies the attacks with good detection rate.
Zhu, Ziming.
2019.
Game theoretic framework for cyber-physical system security incorporating bounded rationality. 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :360–365.
This paper presents a novel game theoretic attack-defence decision making framework for cyber-physical system (CPS) security. Game theory is a powerful tool to analyse the interaction between the attacker and the defender in such scenarios. In the formulation of games, participants are usually assumed to be rational. They will always choose the action to pursuit maximum payoff according to the knowledge of the strategic situation they are in. However, in reality the capacity of rationality is often bounded by the level of intelligence, computational resources and the amount of available information. This paper formulates the concept of bounded rationality into the decision making process, in order to optimise the defender's strategy considering that the defender and the attacker have incomplete information of each other and limited computational capacity. Under the proposed framework, the defender can often benefit from deviating from the minimax Nash Equilibrium strategy, the theoretically expected outcome of rational game playing. Numerical results are presented and discussed in order to demonstrate the proposed technique.
Kamhoua, C. A..
2018.
Game theoretic modeling of cyber deception in the Internet of Battlefield Things. 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :862—862.
Internet of Battlefield Things (IoBT) devices such as actuators, sensors, wearable devises, robots, drones, and autonomous vehicles, facilitate the Intelligence, Surveillance and Reconnaissance (ISR) to Command and Control and battlefield services. IoBT devices have the ability to collect operational field data, to compute on the data, and to upload its information to the network. Securing the IoBT presents additional challenges compared with traditional information technology (IT) systems. First, IoBT devices are mass produced rapidly to be low-cost commodity items without security protection in their original design. Second, IoBT devices are highly dynamic, mobile, and heterogeneous without common standards. Third, it is imperative to understand the natural world, the physical process(es) under IoBT control, and how these real-world processes can be compromised before recommending any relevant security counter measure. Moreover, unprotected IoBT devices can be used as “stepping stones” by attackers to launch more sophisticated attacks such as advanced persistent threats (APTs). As a result of these challenges, IoBT systems are the frequent targets of sophisticated cyber attack that aim to disrupt mission effectiveness.
Guo, Zhen, Cho, Jin–Hee.
2021.
Game Theoretic Opinion Models and Their Application in Processing Disinformation. 2021 IEEE Global Communications Conference (GLOBECOM). :01–07.
Disinformation, fake news, and unverified rumors spread quickly in online social networks (OSNs) and manipulate people's opinions and decisions about life events. The solid mathematical solutions of the strategic decisions in OSNs have been provided under game theory models, including multiple roles and features. This work proposes a game-theoretic opinion framework to model subjective opinions and behavioral strategies of attackers, users, and a defender. The attackers use information deception models to disseminate disinformation. We investigate how different game-theoretic opinion models of updating people's subject opinions can influence a way for people to handle disinformation. We compare the opinion dynamics of the five different opinion models (i.e., uncertainty, homophily, assertion, herding, and encounter-based) where an opinion is formulated based on Subjective Logic that offers the capability to deal with uncertain opinions. Via our extensive experiments, we observe that the uncertainty-based opinion model shows the best performance in combating disinformation among all in that uncertainty-based decisions can significantly help users believe true information more than disinformation.
Xu, D., Xiao, L., Sun, L., Lei, M..
2017.
Game theoretic study on blockchain based secure edge networks. 2017 IEEE/CIC International Conference on Communications in China (ICCC). :1–5.
Blockchain has been applied to study data privacy and network security recently. In this paper, we propose a punishment scheme based on the action record on the blockchain to suppress the attack motivation of the edge servers and the mobile devices in the edge network. The interactions between a mobile device and an edge server are formulated as a blockchain security game, in which the mobile device sends a request to the server to obtain real-time service or launches attacks against the server for illegal security gains, and the server chooses to perform the request from the device or attack it. The Nash equilibria (NEs) of the game are derived and the conditions that each NE exists are provided to disclose how the punishment scheme impacts the adversary behaviors of the mobile device and the edge server.
Ding, Hongfa, Peng, Changgen, Tian, Youliang, Xiang, Shuwen.
2019.
A Game Theoretical Analysis of Risk Adaptive Access Control for Privacy Preserving. 2019 International Conference on Networking and Network Applications (NaNA). :253–258.
More and more security and privacy issues are arising as new technologies, such as big data and cloud computing, are widely applied in nowadays. For decreasing the privacy breaches in access control system under opening and cross-domain environment. In this paper, we suggest a game and risk based access model for privacy preserving by employing Shannon information and game theory. After defining the notions of Privacy Risk and Privacy Violation Access, a high-level framework of game theoretical risk based access control is proposed. Further, we present formulas for estimating the risk value of access request and user, construct and analyze the game model of the proposed access control by using a multi-stage two player game. There exists sub-game perfect Nash equilibrium each stage in the risk based access control and it's suitable to protect the privacy by limiting the privacy violation access requests.
Narayanan, G., Das, J. K., Rajeswari, M., Kumar, R. S..
2018.
Game Theoretical Approach with Audit Based Misbehavior Detection System. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). :1932-1935.
Mobile Ad-hoc Networks are dynamic in nature and do not have fixed infrastructure to govern nodes in the networks. The mission lies ahead in coordinating among such dynamically shifting nodes. The root problem of identifying and isolating misbehaving nodes that refuse to forward packets in multi-hop ad hoc networks is solved by the development of a comprehensive system called Audit-based Misbehavior Detection (AMD) that can efficiently isolates selective and continuous packet droppers. AMD evaluates node behavior on a per-packet basis, without using energy-expensive overhearing techniques or intensive acknowledgment schemes. Moreover, AMD can detect selective dropping attacks even in end-to-end encrypted traffic and can be applied to multi-channel networks. Game theoretical approaches are more suitable in deciding upon the reward mechanisms for which the mobile nodes operate upon. Rewards or penalties have to be decided by ensuring a clean and healthy MANET environment. A non-routine yet surprise alterations are well required in place in deciding suitable and safe reward strategies. This work focuses on integrating a Audit-based Misbehaviour Detection (AMD)scheme and an incentive based reputation scheme with game theoretical approach called Supervisory Game to analyze the selfish behavior of nodes in the MANETs environment. The proposed work GAMD significantly reduces the cost of detecting misbehavior nodes in the network.
Nassar, Mohamed, Khoury, Joseph, Erradi, Abdelkarim, Bou-Harb, Elias.
2021.
Game Theoretical Model for Cybersecurity Risk Assessment of Industrial Control Systems. 2021 11th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1—7.
Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) use advanced computing, sensors, control systems, and communication networks to monitor and control industrial processes and distributed assets. The increased connectivity of these systems to corporate networks has exposed them to new security threats and made them a prime target for cyber-attacks with the potential of causing catastrophic economic, social, and environmental damage. Recent intensified sophisticated attacks on these systems have stressed the importance of methodologies and tools to assess the security risks of Industrial Control Systems (ICS). In this paper, we propose a novel game theory model and Monte Carlo simulations to assess the cybersecurity risks of an exemplary industrial control system under realistic assumptions. We present five game enrollments where attacker and defender agents make different preferences and we analyze the final outcome of the game. Results show that a balanced defense with uniform budget spending is the best strategy against a look-ahead attacker.
Homsi, Soamar, Quan, Gang, Wen, Wujie, Chapparo-Baquero, Gustavo A., Njilla, Laurent.
2019.
Game Theoretic-Based Approaches for Cybersecurity-Aware Virtual Machine Placement in Public Cloud Clusters. 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). :272–281.
Allocating several Virtual Machines (VMs) onto a single server helps to increase cloud computing resource utilization and to reduce its operating expense. However, multiplexing VMs with different security levels on a single server gives rise to major VM-to-VM cybersecurity interdependency risks. In this paper, we address the problem of the static VM allocation with cybersecurity loss awareness by modeling it as a two-player zero-sum game between an attacker and a provider. We first obtain optimal solutions by employing the mathematical programming approach. We then seek to find the optimal solutions by quickly identifying the equilibrium allocation strategies in our formulated zero-sum game. We mean by "equilibrium" that none of the provider nor the attacker has any incentive to deviate from one's chosen strategy. Specifically, we study the characteristics of the game model, based on which, to develop effective and efficient allocation algorithms. Simulation results show that our proposed cybersecurity-aware consolidation algorithms can significantly outperform the commonly used multi-dimensional bin packing approaches for large-scale cloud data centers.
Jain, Ashima, Tripathi, Khushboo, Jatain, Aman, Chaudhary, Manju.
2022.
A Game Theory based Attacker Defender Model for IDS in Cloud Security. 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). :190–194.
Cloud security has become a serious challenge due to increasing number of attacks day-by-day. Intrusion Detection System (IDS) requires an efficient security model for improving security in the cloud. This paper proposes a game theory based model, named as Game Theory Cloud Security Deep Neural Network (GT-CSDNN) for security in cloud. The proposed model works with the Deep Neural Network (DNN) for classification of attack and normal data. The performance of the proposed model is evaluated with CICIDS-2018 dataset. The dataset is normalized and optimal points about normal and attack data are evaluated based on the Improved Whale Algorithm (IWA). The simulation results show that the proposed model exhibits improved performance as compared with existing techniques in terms of accuracy, precision, F-score, area under the curve, False Positive Rate (FPR) and detection rate.
Ma, Yaodong, Liu, Kai, Luo, Xiling.
2022.
Game Theory Based Multi-agent Cooperative Anti-jamming for Mobile Ad Hoc Networks. 2022 IEEE 8th International Conference on Computer and Communications (ICCC). :901–905.
Currently, mobile ad hoc networks (MANETs) are widely used due to its self-configuring feature. However, it is vulnerable to the malicious jammers in practice. Traditional anti-jamming approaches, such as channel hopping based on deterministic sequences, may not be the reliable solution against intelligent jammers due to its fixed patterns. To address this problem, we propose a distributed game theory-based multi-agent anti-jamming (DMAA) algorithm in this paper. It enables each user to exploit all information from its neighboring users before the network attacks, and derive dynamic local policy knowledge to overcome intelligent jamming attacks efficiently as well as guide the users to cooperatively hop to the same channel with high probability. Simulation results demonstrate that the proposed algorithm can learn an optimal policy to guide the users to avoid malicious jamming more efficiently and rapidly than the random and independent Q-learning baseline algorithms,
Wu, Xiaotong, Dou, Wanchun, Ni, Qiang.
2017.
Game Theory Based Privacy Preserving Analysis in Correlated Data Publication. Proceedings of the Australasian Computer Science Week Multiconference. :73:1–73:10.
Privacy preserving on data publication has been an important research field over the past few decades. One of the fundamental challenges in privacy preserving data publication is the trade-off problem between privacy and utility of the single and independent data set. However, recent research works have shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which the payoff of each player is dependent not only on his privacy parameter, but also on his neighbors' privacy parameters. In this paper, we firstly present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, who each publishes the data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium and demonstrate the sufficient conditions in the game. Finally, we refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium.