Selfish Stations Research Articles

Selfish Behavior in IEEE 802.11ah Networks: A Detection Algorithm and Mitigation Strategies.

One of the latest protocols developed for the Internet of Things networks is IEEE 802.11ah, proposed by the WiFi Alliance. The new channel access mechanism in IEEE 802.11ah, which is called Restricted Access Window, aims at reducing the contention between the stations by allowing only selected stations to transmit data at certain time slots. Stations may exhibit selfish behavior to maximize their own throughput. This will come at the cost of the overall network quality of service. In this paper, we first analyze the default behavior of the IEEE 802.11ah protocol in terms of fairness. We then introduce various percentages of selfish stations and observe how the network’s quality of service in terms of fairness, throughput and packet-loss are affected. After establishing the inherent fairness of IEEE 802.11ah, we analyze applicability of two existing selfish behavior detection algorithms designed for IEEE 802.11 to the IEEE 802.11ah protocol. Due to their poor performance, we propose a new definition of ’selfish behavior’ specifically for IEEE 802.11ah, based on which we present a new algorithm for detecting selfish behavior. To combat selfish behavior and to create a better fairness, throughput and lower packet loss, we consequently present a novel mitigation algorithm called Selfish Stations Quarantine Punishment Algorithm (SSQPA). The proposed algorithm takes advantage of the RAW grouping to isolate selfish stations from the honest stations, thus mitigating the effect of the selfish behavior. SSQPA comes in two variants: honest stations-centric and network-centric. Our experimental results show that both variants can successfully mitigate selfish behavior effects in IEEE 802.11ah networks and either one can be used depending on the goal of the network.

An Adaptive EDCA Selfishness-Aware Scheme for Dense WLANs in 5G Networks

To keep pace with the current rapid evolution of mobile data requirements, IEEE 802.11 was evolved to provide more desirable performance to fulfill the needs of fifth-generation (5G) and Internet of Things (IoT) networks. It provides two different access contention-based schemes; Distributed Coordination Function (DCF) which not differentiates between different services, and Enhanced Distributed Channel Access (EDCA) which provides differentiation between various services through four priority Access Categories (ACs). The dilemma of the conventional IEEE 802.11 networks is the static assignation of parameters in DCF and EDCA regardless of the number of associated stations and no matter what kind of service is required by each station (i.e., the activity of ACs). Consequently, this led to a significant degradation in the performance of the network, especially in the case of ultra-dense load network. Therefore, in this paper, we introduce a novel algorithm for EDCA considering a dynamic assignation of Arbitration Inter-Frame Space Number (AIFSN) and guidance Contention Window (CW) depending on the number of associated stations and ACs activeness status. Based on the analytical models of EDCA, a game-theoretic method is proposed to make each associated station adapts its transmission probability within the guidance CW. The purpose of guidance CW is a pre-stage to detect the selfish stations which pick up a very low CW to maximize its throughput regardless of the overall network throughput. Simulation results show that the proposed game-based algorithm can obtain higher performance than the standard 802.11 networks in terms of normalized throughput, data dropped during retransmissions limit threshold exceeding, and mean average delay for sensitive delay applications.

Selfish Attacks in Two-Hop IEEE 802.11 Relay Networks: Impact and Countermeasures

In IEEE 802.11 networks, selfish stations can pursue a better quality of service (QoS) through selfish MAC-layer attacks. Such attacks are easy to perform, secure routing protocols do not prevent them, and their detection may be complex. Two-hop relay topologies allow a new angle of attack: a selfish relay can tamper with either source traffic, transit traffic, or both. We consider the applicability of selfish attacks and their variants in the two-hop relay topology, quantify their impact, and study defense measures.

Credibility of Threats to Jam Anonymous Traffic Remapping Attacks in Ad Hoc WLANs

In ad hoc networks, selfish stations can pursue a better quality of service (QoS) by performing traffic remapping attacks (TRAs), i.e., by falsely assigning their traffic to a higher priority class, which can hurt honest stations’ QoS. To discourage the attackers, honest stations can announce their dissatisfaction with the perceived QoS. If such a threat fails, a costly data frame jamming defense can be launched. We analyze the arising noncooperative game in which the attackers decide whether to continue a TRA when threatened and honest stations decide whether to start jamming when the TRA is continued. Using a Maynard Smith setting, we prove that the threats are credible to a rational attacker, who will then refrain from playing the game and remain honest.

Discouraging Traffic Remapping Attacks in Local Ad Hoc Networks

Quality of Service (QoS) is usually provided in ad hoc networks using a class-based approach which, without dedicated security measures in place, paves the way to various abuses by selfish stations. Such actions include traffic remapping attacks (TRAs), which consist in claiming a higher traffic priority, i.e., false designation of the intrinsic traffic class so that it can be mapped onto a higher-priority class. In practice, TRAs can be executed in IEEE 802.11 ad hoc networks using the Enhanced Distributed Channel Access (EDCA) function. This attack is easy to perform yet hard to prevent. We propose a distributed discouragement scheme based on the threat of TRA detection and punishment. The scheme does not rely on station identities or a trusted third party, nor does it require tampering with the MAC protocol. We analyze an arising non-cooperative TRA game and find that under certain realistic assumptions it only incentivizes TRAs if they are harmless to other stations; otherwise the selfish stations are induced to learn that TRAs are counterproductive.

A Game-Theoretic Approach to Distributed Opportunistic Scheduling

Distributed opportunistic scheduling (DOS) is inherently more difficult than conventional opportunistic scheduling due to the absence of a central entity that knows the channel state of all stations. With DOS, stations use random access to contend for the channel and, upon winning a contention, they measure the channel conditions. After measuring the channel conditions, a station only transmits if the channel quality is good; otherwise, it gives up the transmission opportunity. The distributed nature of DOS makes it vulnerable to selfish users: By deviating from the protocol and using more transmission opportunities, a selfish user can gain a greater share of wireless resources at the expense of well-behaved users. In this paper, we address the problem of selfishness in DOS from a game-theoretic standpoint. We propose an algorithm that satisfies the following properties: 1) When all stations implement the algorithm, the wireless network is driven to the optimal point of operation; and 2) one or more selfish stations cannot obtain any gain by deviating from the algorithm. The key idea of the algorithm is to react to a selfish station by using a more aggressive configuration that (indirectly) punishes this station. We build on multivariable control theory to design a mechanism for punishment that is sufficiently severe to prevent selfish behavior, yet not so severe as to render the system unstable. We conduct a game-theoretic analysis based on repeated games to show the algorithm's effectiveness against selfish stations. These results are confirmed by extensive simulations.

Detecting selfish configurations in 802.11 WLANs

Lately, there has been an increase in the number of IEEE 802.11 devices that provide users with the ability to modify the MAC parameters or do not conform to the standard specification. This increases the risk of having a WLAN with selfish stations that, through the CSMA/CA parameters, obtain a larger share of the resources at the expense of well-behaved users. In this letter we propose a mechanism to detect these selfish stations that, unlike previous approaches, is not based on heuristics nor makes any assumption about radio conditions.

A Game-Theoretic Study of CSMA/CA Under a Backoff Attack

CSMA/CA, the contention mechanism of the IEEE 802.11 DCF medium access protocol, has recently been found vulnerable to selfish backoff attacks consisting in nonstandard configuration of the constituent backoff scheme. Such attacks can greatly increase a selfish station's bandwidth share at the expense of honest stations applying a standard configuration. The paper investigates the distribution of bandwidth among anonymous network stations, some of which are selfish. A station's obtained bandwidth share is regarded as a payoff in a noncooperative CSMA/CA game. Regardless of the IEEE 802.11 parameter setting, the payoff function is found similar to a multiplayer Prisoners' Dilemma; moreover, the number (though not the identities) of selfish stations can be inferred by observation of successful transmission attempts. Further, a repeated CSMA/CA game is defined, where a station can toggle between standard and nonstandard backoff configurations with a view of maximizing a long-term utility. It is argued that a desirable station strategy should yield a fair, Pareto efficient, and subgame perfect Nash equilibrium. One such strategy, called CRISP, is described and evaluated

MAC Contention in a Wireless LAN with Noncooperative Anonymous Stations

In ad hoc wireless LANs populated by mutually impenetrable groups of anonymous stations, honest stations are prone to stealing by selfish stations. The problem is addressed at the MAC level by postulating that (i) honest stations use a carefully designed contention strategy teaching selfish stations that the best reply is to stick to the same strategy, and (ii) a verifiable winner policy be designed so that such a strategy can indeed be found and yields high bandwidth shares for honest stations. For a class of random token winner policies, a number of cycle-by-cycle reinforcement learning strategies are evaluated via simulation using an introduced notion formally akin to evolutionary stability.