Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends

Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Advances in Information Security and Privacy

With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue

An Embryonics Inspired Architecture for Resilient Decentralised Cloud Service Delivery

Data-driven artificial intelligence applications arising from Internet of Things technologies can have profound wide-reaching societal benefits at the cross-section of the cyber and physical domains. Usecases are expanding rapidly. For example, smart-homes and smart-buildings provide intelligent monitoring, resource optimisation, safety, and security for their inhabitants. Smart cities can manage transport, waste, energy, and crime on large scales. Whilst smart-manufacturing can autonomously produce goods through the self-management of factories and logistics. As these use-cases expand further, the requirement to ensure data is processed accurately and timely is ever crucial, as many of these applications are safety critical. Where loss off life and economic damage is a likely possibility in the event of system failure. While the typical service delivery paradigm, cloud computing, is strong due to operating upon economies of scale, their physical proximity to these applications creates network latency which is incompatible with these safety critical applications. To complicate matters further, the environments they operate in are becoming increasingly hostile. With resource-constrained and mobile wireless networking, commonplace. These issues drive the need for new service delivery architectures which operate closer to, or even upon, the network devices, sensors and actuators which compose these IoT applications at the network edge. These hostile and resource constrained environments require adaptation of traditional cloud service delivery models to these decentralised mobile and wireless environments. Such architectures need to provide persistent service delivery within the face of a variety of internal and external changes or: resilient decentralised cloud service delivery. While the current state of the art proposes numerous techniques to enhance the resilience of services in this manner, none provide an architecture which is capable of providing data processing services in a cloud manner which is inherently resilient. Adopting techniques from autonomic computing, whose characteristics are resilient by nature, this thesis presents a biologically-inspired platform modelled on embryonics. Embryonic systems have an ability to self-heal and self-organise whilst showing capacity to support decentralised data processing. An initial model for embryonics-inspired resilient decentralised cloud service delivery is derived according to both the decentralised cloud, and resilience requirements given for this work. Next, this model is simulated using cellular automata, which illustrate the embryonic concept’s ability to provide self-healing service delivery under varying system component loss. This highlights optimisation techniques, including: application complexity bounds, differentiation optimisation, self-healing aggression, and varying system starting conditions. All attributes of which can be adjusted to vary the resilience performance of the system depending upon different resource capabilities and environmental hostilities. Next, a proof-of-concept implementation is developed and validated which illustrates the efficacy of the solution. This proof-of-concept is evaluated on a larger scale where batches of tests highlighted the different performance criteria and constraints of the system. One key finding was the considerable quantity of redundant messages produced under successful scenarios which were helpful in terms of enabling resilience yet could increase network contention. Therefore balancing these attributes are important according to use-case. Finally, graph-based resilience algorithms were executed across all tests to understand the structural resilience of the system and whether this enabled suitable measurements or prediction of the application’s resilience. Interestingly this study highlighted that although the system was not considered to be structurally resilient, the applications were still being executed in the face of many continued component failures. This highlighted that the autonomic embryonic functionality developed was succeeding in executing applications resiliently. Illustrating that structural and application resilience do not necessarily coincide. Additionally, one graph metric, assortativity, was highlighted as being predictive of application resilience, although not structural resilience

Evolutionary Game Theoretic Modeling and Repetition of Media Distributed Shared in P2P-Based VANET

A significant challenge in vehicular networks is to efficiently provide multimedia services with the constraints of limited resources, high mobility, opportunistic contact, and service time requirements. In order to guarantee the vehicle user satisfaction of multimedia service, with the proliferation of the distributed peer-to-peer (P2P) cooperative transmission technologies, P2P-based VANET has recently received a substantial amount of interest. Using the P2P services thought and at the same time, a set of methods should be designed to avoid the disadvantage of P2P system appearing in VANET. Under such a presupposition, in this paper, we study the media provisions in P2P-based VANET and present a repeated game “More Pay for More Work (RGMPMW)” incentive mechanism based on service evaluation information. We also propose evolutionary game-based veracity (EGV) game model which exploits evolutionary game to guarantee the multimedia service share veracity of all vehicles in VANET. In addition, we provide extensive simulation results that demonstrate the effectiveness of our proposed schemes

Enhancing Computer Network Security through Improved Outlier Detection for Data Streams

V několika posledních letech se metody strojového učení (zvláště ty zabývající se detekcí odlehlých hodnot - OD) v oblasti kyberbezpečnosti opíraly o zjišťování anomálií síťového provozu spočívajících v nových schématech útoků. Detekce anomálií v počítačových sítích reálného světa se ale stala stále obtížnější kvůli trvalému nárůstu vysoce objemných, rychlých a dimenzionálních průběžně přicházejících dat (SD), pro která nejsou k dispozici obecně uznané a pravdivé informace o anomalitě. Účinná detekční schémata pro vestavěná síťová zařízení musejí být rychlá a paměťově nenáročná a musejí být schopna se potýkat se změnami konceptu, když se vyskytnou. Cílem této disertace je zlepšit bezpečnost počítačových sítí zesílenou detekcí odlehlých hodnot v datových proudech, obzvláště SD, a dosáhnout kyberodolnosti, která zahrnuje jak detekci a analýzu, tak reakci na bezpečnostní incidenty jako jsou např. nové zlovolné aktivity. Za tímto účelem jsou v práci navrženy čtyři hlavní příspěvky, jež byly publikovány nebo se nacházejí v recenzním řízení časopisů. Zaprvé, mezera ve volbě vlastností (FS) bez učitele pro zlepšování již hotových metod OD v datových tocích byla zaplněna navržením volby vlastností bez učitele pro detekci odlehlých průběžně přicházejících dat označované jako UFSSOD. Následně odvozujeme generický koncept, který ukazuje dva aplikační scénáře UFSSOD ve spojení s online algoritmy OD. Rozsáhlé experimenty ukázaly, že UFSSOD coby algoritmus schopný online zpracování vykazuje srovnatelné výsledky jako konkurenční metoda upravená pro OD. Zadruhé představujeme nový aplikační rámec nazvaný izolovaný les založený na počítání výkonu (PCB-iForest), jenž je obecně schopen využít jakoukoliv online OD metodu založenou na množinách dat tak, aby fungovala na SD. Do tohoto algoritmu integrujeme dvě varianty založené na klasickém izolovaném lese. Rozsáhlé experimenty provedené na 23 multidisciplinárních datových sadách týkajících se bezpečnostní problematiky reálného světa ukázaly, že PCB-iForest jasně překonává už zavedené konkurenční metody v 61 % případů a dokonce dosahuje ještě slibnějších výsledků co do vyváženosti mezi výpočetními náklady na klasifikaci a její úspěšností. Zatřetí zavádíme nový pracovní rámec nazvaný detekce odlehlých hodnot a rozpoznávání schémat útoku proudovým způsobem (SOAAPR), jenž je na rozdíl od současných metod schopen zpracovat výstup z různých online OD metod bez učitele proudovým způsobem, aby získal informace o nových schématech útoku. Ze seshlukované množiny korelovaných poplachů jsou metodou SOAAPR vypočítány tři různé soukromí zachovávající podpisy podobné otiskům prstů, které charakterizují a reprezentují potenciální scénáře útoku s ohledem na jejich komunikační vztahy, projevy ve vlastnostech dat a chování v čase. Evaluace na dvou oblíbených datových sadách odhalila, že SOAAPR může soupeřit s konkurenční offline metodou ve schopnosti korelace poplachů a významně ji překonává z hlediska výpočetního času . Navíc se všechny tři typy podpisů ve většině případů zdají spolehlivě charakterizovat scénáře útoků tím, že podobné seskupují k sobě. Začtvrté představujeme algoritmus nepárového kódu autentizace zpráv (Uncoupled MAC), který propojuje oblasti kryptografického zabezpečení a detekce vniknutí (IDS) pro síťovou bezpečnost. Zabezpečuje síťovou komunikaci (autenticitu a integritu) kryptografickým schématem s podporou druhé vrstvy kódy autentizace zpráv, ale také jako vedlejší efekt poskytuje funkcionalitu IDS tak, že vyvolává poplach na základě porušení hodnot nepárového MACu. Díky novému samoregulačnímu rozšíření algoritmus adaptuje svoje vzorkovací parametry na základě zjištění škodlivých aktivit. Evaluace ve virtuálním prostředí jasně ukazuje, že schopnost detekce se za běhu zvyšuje pro různé scénáře útoku. Ty zahrnují dokonce i situace, kdy se inteligentní útočníci snaží využít slabá místa vzorkování.ObhájenoOver the past couple of years, machine learning methods - especially the Outlier Detection (OD) ones - have become anchored to the cyber security field to detect network-based anomalies rooted in novel attack patterns. Due to the steady increase of high-volume, high-speed and high-dimensional Streaming Data (SD), for which ground truth information is not available, detecting anomalies in real-world computer networks has become a more and more challenging task. Efficient detection schemes applied to networked, embedded devices need to be fast and memory-constrained, and must be capable of dealing with concept drifts when they occur. The aim of this thesis is to enhance computer network security through improved OD for data streams, in particular SD, to achieve cyber resilience, which ranges from the detection, over the analysis of security-relevant incidents, e.g., novel malicious activity, to the reaction to them. Therefore, four major contributions are proposed, which have been published or are submitted journal articles. First, a research gap in unsupervised Feature Selection (FS) for the improvement of off-the-shell OD methods in data streams is filled by proposing Unsupervised Feature Selection for Streaming Outlier Detection, denoted as UFSSOD. A generic concept is retrieved that shows two application scenarios of UFSSOD in conjunction with online OD algorithms. Extensive experiments have shown that UFSSOD, as an online-capable algorithm, achieves comparable results with a competitor trimmed for OD. Second, a novel unsupervised online OD framework called Performance Counter-Based iForest (PCB-iForest) is being introduced, which generalized, is able to incorporate any ensemble-based online OD method to function on SD. Two variants based on classic iForest are integrated. Extensive experiments, performed on 23 different multi-disciplinary and security-related real-world data sets, revealed that PCB-iForest clearly outperformed state-of-the-art competitors in 61 % of cases and even achieved more promising results in terms of the tradeoff between classification and computational costs. Third, a framework called Streaming Outlier Analysis and Attack Pattern Recognition, denoted as SOAAPR is being introduced that, in contrast to the state-of-the-art, is able to process the output of various online unsupervised OD methods in a streaming fashion to extract information about novel attack patterns. Three different privacy-preserving, fingerprint-like signatures are computed from the clustered set of correlated alerts by SOAAPR, which characterize and represent the potential attack scenarios with respect to their communication relations, their manifestation in the data's features and their temporal behavior. The evaluation on two popular data sets shows that SOAAPR can compete with an offline competitor in terms of alert correlation and outperforms it significantly in terms of processing time. Moreover, in most cases all three types of signatures seem to reliably characterize attack scenarios to the effect that similar ones are grouped together. Fourth, an Uncoupled Message Authentication Code algorithm - Uncoupled MAC - is presented which builds a bridge between cryptographic protection and Intrusion Detection Systems (IDSs) for network security. It secures network communication (authenticity and integrity) through a cryptographic scheme with layer-2 support via uncoupled message authentication codes but, as a side effect, also provides IDS-functionality producing alarms based on the violation of Uncoupled MAC values. Through a novel self-regulation extension, the algorithm adapts its sampling parameters based on the detection of malicious actions on SD. The evaluation in a virtualized environment clearly shows that the detection rate increases over runtime for different attack scenarios. Those even cover scenarios in which intelligent attackers try to exploit the downsides of sampling

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI