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21 pages, 9165 KiB

Open AccessArticle

An Improved CSMA/CA Protocol Anti-Jamming Method Based on Reinforcement Learning

by Zidong Ming, Xin Liu, Xiaofei Yang and Mei Wang

Electronics 2023, 12(17), 3547; https://doi.org/10.3390/electronics12173547 - 22 Aug 2023

Cited by 3 | Viewed by 2066

The CSMA/CA algorithm uses the binary backoff mechanism to solve the multi-user channel access problem, but this mechanism is vulnerable to jamming attacks. Existing research uses channel-hopping to avoid jamming, but this method fails when the channel is limited or hard to hop. To address this problem, we first propose a Markov decision process (MDP) model with contention window (CW) as the state, throughput as the reward value, and backoff action as the control variable. Based on this, we design an intelligent CSMA/CA protocol based on distributed reinforcement learning. Specifically, each node adopts distributed learning decision-making, which needs to query and update information from a central status collection equipment (SCE). It improves its anti-jamming ability by learning from different environments and adapting to them. Simulation results show that the proposed algorithm is significantly better than CSMA/CA and SETL algorithms in both jamming and non-jamming environments. And it has little performance difference with the increase in the number of nodes, effectively improving the anti-jamming performance. When the communication node is 10, the normalized throughputs of the proposed algorithm in non-jamming, intermittent jamming, and random jamming are increased by 28.45%, 21.20%, and 17.07%, respectively, and the collision rates are decreased by 83.93%, 95.71%, and 81.58% respectively. Full article

(This article belongs to the Section Networks)

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15 pages, 2533 KiB

Open AccessFeature PaperArticle

Exposure of Botnets in Cloud Environment by Expending Trust Model with CANFES Classification Approach

by Nagendra Prabhu Selvaraj, Sivakumar Paulraj, Parthasarathy Ramadass, Rajesh Kaluri, Mohammad Shorfuzzaman, Abdulmajeed Alsufyani and Mueen Uddin

Electronics 2022, 11(15), 2350; https://doi.org/10.3390/electronics11152350 - 28 Jul 2022

Cited by 6 | Viewed by 2383

Abstract

Many cloud service providers offer access to versatile, dependable processing assets following a compensation as-you-go display. Investigation into the security of the cloud focusses basically on shielding genuine clients of cloud administrations from assaults by outer, vindictive clients. Little consideration is given to restrict malicious clients from utilizing the cloud to dispatch assaults, for example, those as of now done by botnets. These assaults incorporate propelling a DDoS attack, sending spam and executing click extortion. Bots’ detection in the cloud environment is a complex process. The purpose of this study was to create a multi-layered architecture that could detect a variety of existing and emerging botnets. The goal is to be able to detect a larger range of bots and botnets by relying on several techniques called trust model. On this work, the port access verification in trust model is achieved by a Heuristic factorizing algorithm which verifies the port accessibility between client-end-user and client server. Further, back-off features are extracted from the particular node and all these structures are trained and categorized with a Co-Active Neuro Fuzzy Expert System (CANFES) classifier. The performance of the proposed bot detection system in the internet environment is analyzed latency, detection rate, packet delivery ration, energy availability and precision. Full article

(This article belongs to the Section Computer Science & Engineering)

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21 pages, 660 KiB

Open AccessFeature PaperArticle

On the Detection of Low-Rate Denial of Service Attacks at Transport and Application Layers

by Vasudha Vedula, Palden Lama, Rajendra V. Boppana and Luis A. Trejo

Electronics 2021, 10(17), 2105; https://doi.org/10.3390/electronics10172105 - 30 Aug 2021

Cited by 19 | Viewed by 4658

Abstract

Distributed denial of service (DDoS) attacks aim to deplete the network bandwidth and computing resources of targeted victims. Low-rate DDoS attacks exploit protocol features such as the transmission control protocol (TCP) three-way handshake mechanism for connection establishment and the TCP congestion-control induced backoffs to attack at a much lower rate and still effectively bring down the targeted network and computer systems. Most of the statistical and machine/deep learning-based detection methods proposed in the literature require keeping track of packets by flows and have high processing overheads for feature extraction. This paper presents a novel two-stage model that uses Long Short-Term Memory (LSTM) and Random Forest (RF) to detect the presence of attack flows in a group of flows. This model has a very low data processing overhead; it uses only two features and does not require keeping track of packets by flows, making it suitable for continuous monitoring of network traffic and on-the-fly detection. The paper also presents an LSTM Autoencoder to detect individual attack flows with high detection accuracy using only two features. Additionally, the paper presents an analysis of a support vector machine (SVM) model that detects attack flows in slices of network traffic collected for short durations. The low-rate attack dataset used in this study is made available to the research community through GitHub. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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20 pages, 404 KiB

Open AccessArticle

Deep Reinforcement Learning for Attacking Wireless Sensor Networks

by Juan Parras, Maximilian Hüttenrauch, Santiago Zazo and Gerhard Neumann

Sensors 2021, 21(12), 4060; https://doi.org/10.3390/s21124060 - 12 Jun 2021

Cited by 10 | Viewed by 3605

Abstract

Recent advances in Deep Reinforcement Learning allow solving increasingly complex problems. In this work, we show how current defense mechanisms in Wireless Sensor Networks are vulnerable to attacks that use these advances. We use a Deep Reinforcement Learning attacker architecture that allows having one or more attacking agents that can learn to attack using only partial observations. Then, we subject our architecture to a test-bench consisting of two defense mechanisms against a distributed spectrum sensing attack and a backoff attack. Our simulations show that our attacker learns to exploit these systems without having a priori information about the defense mechanism used nor its concrete parameters. Since our attacker requires minimal hyper-parameter tuning, scales with the number of attackers, and learns only by interacting with the defense mechanism, it poses a significant threat to current defense procedures. Full article

(This article belongs to the Special Issue Deep Learning, Deep Reinforcement Learning for Computer Networking)

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21 pages, 962 KiB

Open AccessArticle

A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks

by Bader A. Aldawsari and Jafar Haadi Jafarian

Appl. Sci. 2021, 11(3), 1156; https://doi.org/10.3390/app11031156 - 27 Jan 2021

Cited by 4 | Viewed by 2487

Abstract

Multiple access channel (MAC) networks use a broadcasting algorithm called the Binary Exponential Backoff (BEB) to mediate access to the shared communication channel by competing nodes and resolve their collisions. While the BEB achieves fair throughput and average packet latency in jamming-free environments and relatively small networks, its performance noticeably degrades when the network is exposed to jamming or its size increases. This paper presents an alternative broadcasting algorithm called the K-tuple Full Withholding (KTFW), which significantly increases MAC networks’ resilience to jamming attacks and network growth. Through simulation, we compare the KTFW with both the BEB and the Queue Backoff (QB), an efficient and high-throughput broadcasting algorithm. We compare the three approaches against two different traffic injection models, each approximating a different environment type. Our results show that the KTFW achieves higher throughput and lower average packet latency against jamming attacks than both the BEB and the QB algorithms. The results also show that the KTFW outperforms the BEB for larger networks with or without jamming. Full article

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22 pages, 1549 KiB

Open AccessArticle

Repeated Game Analysis of a CSMA/CA Network under a Backoff Attack

by Juan Parras and Santiago Zazo

Sensors 2019, 19(24), 5393; https://doi.org/10.3390/s19245393 - 6 Dec 2019

Cited by 6 | Viewed by 2717

Abstract

We study a CSMA/CA (Carrier Sense Medium Access with Collision Avoidance) wireless network where some stations deviate from the defined contention mechanism. By using Bianchi’s model, we study how this deviation impacts the network throughput and show that the fairness of the network is seriously affected, as the stations that deviate achieve a larger share of the resources than the rest of stations. Previously, we modeled this situation using a static game and now, we use repeated games, which, by means of the Folk theorem, allow all players to have better outcomes. We provide analytical solutions to this game for the two player case using subgame perfect and correlated equilibria concepts. We also propose a distributed algorithm based on communicating candidate equilibrium points for learning the equilibria of this game for an arbitrary number of players. We validate approach using numerical simulations, which allows comparing the solutions we propose and discussing the advantages of using each of the methods we propose. Full article

(This article belongs to the Section Sensor Networks)

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19 pages, 696 KiB

Open AccessArticle

Wireless Networks under a Backoff Attack: A Game Theoretical Perspective

by Juan Parras and Santiago Zazo

Sensors 2018, 18(2), 404; https://doi.org/10.3390/s18020404 - 30 Jan 2018

Cited by 8 | Viewed by 3257

Abstract

We study a wireless sensor network using CSMA/CA in the MAC layer under a backoff attack: some of the sensors of the network are malicious and deviate from the defined contention mechanism. We use Bianchi’s network model to study the impact of the malicious sensors on the total network throughput, showing that it causes the throughput to be unfairly distributed among sensors. We model this conflict using game theory tools, where each sensor is a player. We obtain analytical solutions and propose an algorithm, based on Regret Matching, to learn the equilibrium of the game with an arbitrary number of players. Our approach is validated via simulations, showing that our theoretical predictions adjust to reality. Full article

(This article belongs to the Section Sensor Networks)

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