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2021-06-28
Liu, Jia, Fu, Hongchuan, Chen, Yunhua, Shi, Zhiping.  2020.  A Trust-based Message Passing Algorithm against Persistent SSDF. 2020 IEEE 20th International Conference on Communication Technology (ICCT). :1112–1115.
As a key technology in cognitive radio, cooperative spectrum sensing has been paid more and more attention. In cooperative spectrum sensing, multi-user cooperative spectrum sensing can effectively alleviate the performance degradation caused by multipath effect and shadow fading, and improve the spectrum utilization. However, as there may be malicious users in the cooperative sensing users, sending forged false messages to the fusion center or neighbor nodes to mislead them to make wrong judgments, which will greatly reduce the spectrum utilization. To solve this problem, this paper proposes an intelligent anti spectrum sensing data falsification (SSDF) attack algorithm using trust-based non consensus message passing algorithm. In this scheme, only one perception is needed, and the historical propagation path of each message is taken as the basis to calculate the reputation of each cognitive user. Every time a node receives different messages from the same cognitive user, there must be malicious users in its propagation path. We reward the nodes that appear more times in different paths with reputation value, and punish the nodes that appear less. Finally, the real value of the tampered message is restored according to the calculated reputation value. The MATLAB results show that the proposed scheme has a high recovery rate for messages and can identify malicious users in the network at the same time.
2021-03-15
Toma, A., Krayani, A., Marcenaro, L., Gao, Y., Regazzoni, C. S..  2020.  Deep Learning for Spectrum Anomaly Detection in Cognitive mmWave Radios. 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications. :1–7.
Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection.
Wang, F., Zhang, X..  2020.  Secure Resource Allocation for Polarization-Based Non-Linear Energy Harvesting Over 5G Cooperative Cognitive Radio Networks. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1–6.
We address secure resource allocation for the energy harvesting (EH) based 5G cooperative cognitive radio networks (CRNs). To guarantee that the size-limited secondary users (SUs) can simultaneously send the primary user's and their own information, we assume that SUs are equipped with orthogonally dual-polarized antennas (ODPAs). In particular, we propose, develop, and analyze an efficient resource allocation scheme under a practical non-linear EH model, which can capture the nonlinear characteristics of the end-to-end wireless power transfer (WPT) for radio frequency (RF) based EH circuits. Our obtained numerical results validate that a substantial performance gain can be obtained by employing the non-linear EH model.
Bouzegag, Y., Teguig, D., Maali, A., Sadoudi, S..  2020.  On the Impact of SSDF Attacks in Hard Combination Schemes in Cognitive Radio Networks. 020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP). :19–24.
One of the critical threats menacing the Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) is the Spectrum Sensing Data Falsification (SSDF) reports, which can deceive the decision of Fusion Center (FC) about the Primary User (PU) spectrum accessibility. In CSS, each CR user performs Energy Detection (ED) technique to detect the status of licensed frequency bands of the PU. This paper investigates the performance of different hard-decision fusion schemes (OR-rule, AND-rule, and MAJORITY-rule) in the presence of Always Yes and Always No Malicious User (AYMU and ANMU) over Rayleigh and Gaussian channels. More precisely, comparative study is conducted to evaluate the impact of such malicious users in CSS on the performance of various hard data combining rules in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are carried out to show that the hard-decision fusion scheme with MAJORITY-rule is the best among hard-decision combination under AYMU attacks, OR-rule has the best detection performance under ANMU.
Joykutty, A. M., Baranidharan, B..  2020.  Cognitive Radio Networks: Recent Advances in Spectrum Sensing Techniques and Security. 2020 International Conference on Smart Electronics and Communication (ICOSEC). :878–884.
Wireless networks are very significant in the present world owing to their widespread use and its application in domains like disaster management, smart cities, IoT etc. A wireless network is made up of a group of wireless nodes that communicate with each other without using any formal infrastructure. The topology of the wireless network is not fixed and it can vary. The huge increase in the number of wireless devices is a challenge owing to the limited availability of wireless spectrum. Opportunistic spectrum access by Cognitive radio enables the efficient usage of limited spectrum resources. The unused channels assigned to the primary users may go waste in idle time. Cognitive radio systems will sense the unused channel space and assigns it temporarily for secondary users. This paper discusses about the recent trends in the two most important aspects of Cognitive radio namely spectrum sensing and security.
Morozov, M. Y., Perfilov, O. Y., Malyavina, N. V., Teryokhin, R. V., Chernova, I. V..  2020.  Combined Approach to SSDF-Attacks Mitigation in Cognitive Radio Networks. 2020 Systems of Signals Generating and Processing in the Field of on Board Communications. :1–4.
Cognitive radio systems aim to solve the issue of spectrum scarcity through implementation of dynamic spectrum management and cooperative spectrum access. However, the structure of such systems introduced unique types of vulnerabilities and attacks, one of which is spectrum sensing data falsification attack (SSDF). In such attacks malicious users provide incorrect observations to the fusion center of the system, which may result in severe quality of service degradation and interference for licensed users. In this paper we investigate this type of attacks and propose a combined approach to their mitigation. On the first step a reputational method is used to isolate the initially untrustworthy nodes, on the second step specialized q-out-of-m fusion rule is utilized to mitigate the remains of attack. In this paper we present theoretical analysis of the proposed combined method.
Akter, S., Rahman, M. S., Mansoor, N..  2020.  An Efficient Routing Protocol for Secured Communication in Cognitive Radio Sensor Networks. 2020 IEEE Region 10 Symposium (TENSYMP). :1713–1716.
This paper introduces an efficient reactive routing protocol considering the mobility and the reliability of a node in Cognitive Radio Sensor Networks (CRSNs). The proposed protocol accommodates the dynamic behavior of the spectrum availability and selects a stable transmission path from a source node to the destination. Outlined as a weighted graph problem, the proposed protocol measures the weight for an edge the measuring the mobility patterns of the nodes and channel availability. Furthermore, the mobility pattern of a node is defined in the proposed routing protocol from the viewpoint of distance, speed, direction, and node's reliability. Besides, the spectrum awareness in the proposed protocol is measured over the number of shared common channels and the channel quality. It is anticipated that the proposed protocol shows efficient routing performance by selecting stable and secured paths from source to destination. Simulation is carried out to assess the performance of the protocol where it is witnessed that the proposed routing protocol outperforms existing ones.
Nieto-Chaupis, H..  2020.  Hyper Secure Cognitive Radio Communications in an Internet of Space Things Network Based on the BB84 Protocol. 2020 Intermountain Engineering, Technology and Computing (IETC). :1–5.
Once constellation of satellites are working in a collaborative manner, the security of their messages would have to be highly secure from all angles of scenarios by which the praxis of eavesdropping constitutes a constant thread for the instability of the different tasks and missions. In this paper we employ the Bennet-Brassard commonly known as the BB84 protocol in conjunction to the technique of Cognitive Radio applied to the Internet of Space Things to build a prospective technology to guarantee the communications among geocentric orbital satellites. The simulations have yielded that for a constellation of 5 satellites, the probability of successful of completion the communication might be of order of 75% ±5%.
Khalid, W., Yu, H..  2020.  Residual Energy Analysis with Physical-Layer Security for Energy-Constrained UAV Cognitive Radio Systems. 2020 International Conference on Electronics, Information, and Communication (ICEIC). :1–3.
Unmanned aerial vehicles (UAVs) based cognitive radio (CR) systems improve the sensing performance. However, such systems demand secure communication with lower power consumption. Motivated by these observations, we consider an energy-constraint yet energy harvesting (EH) drone flying periodically in the circular track around primary transmitter in the presence of an eavesdropper with an aim to use the licensed band opportunistically. Considering the trade-off between the residual energy and secondary link performance, we formulate the constrained optimization problem, i.e., maximizing residual energy under the constraint of secondary secrecy outage. Simulation results verify the proposed theoretical analysis.
Thanuja, T. C., Daman, K. A., Patil, A. S..  2020.  Optimized Spectrum sensing Techniques for Enhanced Throughput in Cognitive Radio Network. 2020 International Conference on Emerging Smart Computing and Informatics (ESCI). :137–141.
The wireless communication is a backbone for a development of a nation. But spectrum is finite resource and issues like spectrum scarcity, loss of signal quality, transmission delay, raised in wireless communication system due to growth of wireless applications and exponentially increased number of users. Secondary use of a spectrum using Software Defined Radio (SDR) is one of the solutions which is also supported by TRAI. The spectrum sensing is key process in communication based on secondary use of spectrum. But energy consumption, added delay, primary users security are some threats in this system. Here in this paper we mainly focused on throughput optimization in secondary use of spectrum based on optimal sensing time and number of Secondary users during cooperative spectrum sensing in Cognitive radio networks.
Salama, G. M., Taha, S. A..  2020.  Cooperative Spectrum Sensing and Hard Decision Rules for Cognitive Radio Network. 2020 3rd International Conference on Computer Applications Information Security (ICCAIS). :1–6.
Cognitive radio is development of wireless communication and mobile computing. Spectrum is a limited source. The licensed spectrum is proposed to be used only by the spectrum owners. Cognitive radio is a new view of the recycle licensed spectrum in an unlicensed manner. The main condition of the cognitive radio network is sensing the spectrum hole. Cognitive radio can be detect unused spectrum. It shares this with no interference to the licensed spectrum. It can be a sense signals. It makes viable communication in the middle of multiple users through co-operation in a self-organized manner. The energy detector method is unseen signal detector because it reject the data of the signal.In this paper, has implemented Simulink Energy Detection of spectrum sensing cognitive radio in a MATLAB Simulink to Exploit spectrum holes and avoid damaging interference to licensed spectrum and unlicensed spectrum. The hidden primary user problem will happened because fading or shadowing. Ithappens when cognitive radio could not be detected by primer users because of its location. Cooperative sensing spectrum sensing is the best-proposed method to solve the hidden problem.
Shekhawat, G. K., Yadav, R. P..  2020.  Sparse Code Multiple Access based Cooperative Spectrum Sensing in 5G Cognitive Radio Networks. 2020 5th International Conference on Computing, Communication and Security (ICCCS). :1–6.
Fifth-generation (5G) network demands of higher data rate, massive user connectivity and large spectrum can be achieve using Sparse Code Multiple Access (SCMA) scheme. The integration of cognitive feature spectrum sensing with SCMA can enhance the spectrum efficiency in a heavily dense 5G wireless network. In this paper, we have investigated the primary user detection performance using SCMA in Centralized Cooperative Spectrum Sensing (CCSS). The developed model can support massive user connectivity, lower latency and higher spectrum utilization for future 5G networks. The simulation study is performed for AWGN and Rayleigh fading channel. Log-MPA iterative receiver based Log-Likelihood Ratio (LLR) soft test statistic is passed to Fusion Center (FC). The Wald-hypothesis test is used at FC to finalize the PU decision.
2021-03-09
Venkataramana, B., Jadhav, A..  2020.  Performance Evaluation of Routing Protocols under Black Hole Attack in Cognitive Radio Mesh Network. 2020 International Conference on Emerging Smart Computing and Informatics (ESCI). :98–102.
Wireless technology is rapidly proliferating. Devices such as Laptops, PDAs and cell-phones gained a lot of importance due to the use of wireless technology. Nowadays there is also a huge demand for spectrum allocation and there is a need to utilize the maximum available spectrum in efficient manner. Cognitive Radio (CR) Network is one such intelligent radio network, designed to utilize the maximum licensed bandwidth to un-licensed users. Cognitive Radio has the capability to understand unused spectrum at a given time at a specific location. This capability helps to minimize the interference to the licensed users and improves the performance of the network. Routing protocol selection is one of the main strategies to design any wireless or wired networks. In Cognitive radio networks the selected routing protocol should be best in terms of establishing an efficient route, addressing challenges in network topology and should be able to reduce bandwidth consumption. Performance analysis of the protocols helps to select the best protocol in the network. Objective of this study is to evaluate performance of various cognitive radio network routing protocols like Spectrum Aware On Demand Routing Protocol (SORP), Spectrum Aware Mesh Routing in Cognitive Radio Networks (SAMER) and Dynamic Source Routing (DSR) with and without black hole attack using various performance parameters like Throughput, E2E delay and Packet delivery ratio with the help of NS2 simulator.
2020-12-02
Tsiligkaridis, T., Romero, D..  2018.  Reinforcement Learning with Budget-Constrained Nonparametric Function Approximation for Opportunistic Spectrum Access. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). :579—583.

Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the environment to find the best channel access actions. This approach allows learning and planning over the intrinsic state-action space and extends well to large state spaces. We apply our methods to evaluate coexistence of a reinforcement learning-based radio with a multi-channel adversarial radio and a single-channel carrier-sense multiple-access with collision avoidance (CSMA-CA) radio. Numerical experiments show the performance gains over carrier-sense systems.

2020-09-18
Taggu, Amar, Marchang, Ningrinla.  2019.  Random-Byzantine Attack Mitigation in Cognitive Radio Networks using a Multi-Hidden Markov Model System. 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA). :1—5.
Cognitive Radio Networks (CRN) are opportunistic networks which aim to harness the white space in the television frequency spectrum, on a need-to-need basis, without interfering the incumbent, called the Primary User (PU). Cognitive radios (CR) that sense the spectrum periodically for sensing the PU activity, are called Secondary Users (SU). CRNs are susceptible to two major attacks, Byzantine attacks and Primary User Emulation Attack (PUEA). Both the attacks are capable of rendering a CRN useless, by either interfering with the PU itself or capturing the entire channel for themselves. Byzantine attacks detection and mitigation is an important security issue in CRN. Hence, the current work proposes using a multi-Hidden Markov Model system with an aim to detect different types of random-Byzantine attacks. Simulation results show good detection rate across all the attacks.
Ling, Mee Hong, Yau, Kok-Lim Alvin.  2019.  Can Reinforcement Learning Address Security Issues? an Investigation into a Clustering Scheme in Distributed Cognitive Radio Networks 2019 International Conference on Information Networking (ICOIN). :296—300.

This paper investigates the effectiveness of reinforcement learning (RL) model in clustering as an approach to achieve higher network scalability in distributed cognitive radio networks. Specifically, it analyzes the effects of RL parameters, namely the learning rate and discount factor in a volatile environment, which consists of member nodes (or secondary users) that launch attacks with various probabilities of attack. The clusterhead, which resides in an operating region (environment) that is characterized by the probability of attacks, countermeasures the malicious SUs by leveraging on a RL model. Simulation results have shown that in a volatile operating environment, the RL model with learning rate α= 1 provides the highest network scalability when the probability of attacks ranges between 0.3 and 0.7, while the discount factor γ does not play a significant role in learning in an operating environment that is volatile due to attacks.

Pham-Thi-Dan, Ngoc, Do-Dac, Thiem, Ho-Van, Khuong, Vo-Que, Son, Pham-Ngoc, Son.  2019.  On Security Capability of Cooperative Communications in Energy Scavenging Cognitive Radio Networks. 2019 International Conference on Advanced Technologies for Communications (ATC). :89—93.
In this paper, secrecy outage probability (SOP) of cooperative communications in ESCRNs is numerically evaluated by the recommended precise closed-form formula which is corroborated by numerous computer simulations. Results expose that the relay's location, energy scavenging time, message recovering time, and power division for energy scavenging and message recovering dramatically impact the SOP of the cooperative communications in ESCRNs. Moreover, results infer that the SOP is constant either in energy scavenging non-cognitive networks (ES-nonCRNs) or in ESCRNs with infinite power transmitters.
Sureka, N., Gunaseelan, K..  2019.  Detection Defense against Primary User Emulation Attack in Dynamic Cognitive Radio Networks. 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 1:505—510.
Cognitive radio is a promising technology that intends on solving the spectrum scarcity problem by allocating free spectrum dynamically to the unlicensed Secondary Users (SUs) in order to establish coexistence between the licensed Primary User (PU) & SUs, without causing any interference to the incumbent transmission. Primary user emulation attack (PUEA) is one such major threat posed on spectrum sensing, which decreases the spectrum access probability. Detection and defense against PUEA is realized using Yardstick based Threshold Allocation technique (YTA), by assigning threshold level to the base station thereby efficiently enhancing the spectrum sensing ability in a dynamic CR network. The simulation is performed using NS2 and analysis by using X-graph. The results shows minimum interference to primary transmissions by letting SUs spontaneously predict the prospective spectrum availability and aiding in effective prevention of potential emulation attacks along with proficient improvement of throughput in a dynamic cognitive radio environment.
Simpson, Oluyomi, Sun, Yichuang.  2019.  A Stochastic based Physical Layer Security in Cognitive Radio Networks: Cognitive Relay to Fusion Center. 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC). :1—7.
Cognitive radio networks (CRNs) are found to be, without difficulty wide-open to external malicious threats. Secure communication is an important prerequisite for forthcoming fifth-generation (5G) systems, and CRs are not exempt. A framework for developing the accomplishable benefits of physical layer security (PLS) in an amplify-and-forward cooperative spectrum sensing (AF-CSS) in a cognitive radio network (CRN) using a stochastic geometry is proposed. In the CRN the spectrum sensing data from secondary users (SU) are collected by a fusion center (FC) with the assistance of access points (AP) as cognitive relays, and when malicious eavesdropping SU are listening. In this paper we focus on the secure transmission of active APs relaying their spectrum sensing data to the FC. Closed expressions for the average secrecy rate are presented. Analytical formulations and results substantiate our analysis and demonstrate that multiple antennas at the APs is capable of improving the security of an AF-CSSCRN. The obtained numerical results also show that increasing the number of FCs, leads to an increase in the secrecy rate between the AP and its correlated FC.
Pham-Thi-Dan, Ngoc, Ho-Van, Khuong, Do-Dac, Thiem, Vo-Que, Son, Pham-Ngoc, Son.  2019.  Security Analysis for Cognitive Radio Network with Energy Scavenging Capable Relay over Nakagami-m Fading Channels. 2019 International Symposium on Electrical and Electronics Engineering (ISEE). :68—72.
In this paper, we propose an exact closed-form expression of secrecy outage probability (SOP) for underlay cognitive network with energy scavenging capable relay over Nakagami-m fading channels and under both (maximum transmit and interference) power constraints. Various results validated the proposed expression and shed insights into the security performance of this network in key specifications.
Torabi, Mohammad, Pouri, Alireza Baghaei.  2019.  Physical Layer Security of a Two-Hop Mixed RF-FSO System in a Cognitive Radio Network. 2019 2nd West Asian Colloquium on Optical Wireless Communications (WACOWC). :167—170.
In this paper, the physical layer (PHY)security performance of a dual-hop cooperative relaying in a cognitive-radio system in the presence of an eavesdropper is investigated. The dual-hop transmission is composed of an asymmetric radio frequency (RF)link and a free space optical (FSO)link. In the considered system, an unlicensed secondary user (SU)uses the spectrum which is shared by a licensed primary user (PU)in a controlled manner to keep the interference at PU receiver, below a predefined value. Furthermore, among M available relays, one relay with the best end-to-end signal-to-noise-ratio (SNR)is selected for transmission. It is assumed that all of the RF links follow Rayleigh fading and all of the FSO links follow Gamma-Gamma distribution. Simulations results for some important security metrics, such as the average secrecy capacity (SC), and secrecy outage probability (SOP)are presented, where some practical issues of FSO links such as atmospheric turbulence, and pointing errors are taken into consideration.
2020-09-14
Chandrala, M S, Hadli, Pooja, Aishwarya, R, Jejo, Kevin C, Sunil, Y, Sure, Pallaviram.  2019.  A GUI for Wideband Spectrum Sensing using Compressive Sampling Approaches. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–6.
Cognitive Radio is a prominent solution for effective spectral resource utilization. The rapidly growing device to device (D2D) communications and the next generation networks urge the cognitive radio networks to facilitate wideband spectrum sensing in order to assure newer spectral opportunities. As Nyquist sampling rates are formidable owing to complexity and cost of the ADCs, compressive sampling approaches are becoming increasingly popular. One such approach exploited in this paper is the Modulated Wideband Converter (MWC) to recover the spectral support. On the multiple measurement vector (MMV) framework provided by the MWC, threshold based Orthogonal Matching Pursuit (OMP) and Sparse Bayesian Learning (SBL) algorithms are employed for support recovery. We develop a Graphical User Interface (GUI) that assists a beginner to simulate the RF front-end of a MWC and thereby enables the user to explore support recovery as a function of Signal to Noise Ratio (SNR), number of measurement vectors and threshold. The GUI enables the user to explore spectrum sensing in DVB-T, 3G and 4G bands and recovers the support using OMP or SBL approach. The results show that the performance of SBL is better than that of OMP at a lower SNR values.
2020-04-10
Simpson, Oluyomi, Sun, Yichuang.  2019.  A Stochastic Method to Physical Layer Security of an Amplify-and-Forward Spectrum Sensing in Cognitive Radio Networks: Secondary User to Relay. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :197—202.
In this paper, a framework for capitalizing on the potential benefits of physical layer security in an amplify-and-forward cooperative spectrum sensing (AF-CSS) in a cognitive radio network (CRN) using a stochastic geometry is proposed. In the CRN network the sensing data from secondary users (SUs) are collected by a fusion center (FC) with the help of access points (AP) as relays, and when malicious eavesdropping secondary users (SUs) are listening. We focus on the secure transmission of active SUs transmitting their sensing data to the AP. Closed expressions for the average secrecy rate are presented. Numerical results corroborate our analysis and show that multiple antennas at the APs can enhance the security of the AF-CSS-CRN. The obtained numerical results show that average secrecy rate between the AP and its correlated FC decreases when the number of AP is increased. Nevertheless, we find that an increase in the number of AP initially increases the overall average secrecy rate, with a perilous value at which the overall average secrecy rate then decreases. While increasing the number of active SUs, there is a decrease in the secrecy rate between the sensor and its correlated AP.
Srinu, Sesham, Reddy, M. Kranthi Kumar, Temaneh-Nyah, Clement.  2019.  Physical layer security against cooperative anomaly attack using bivariate data in distributed CRNs. 2019 11th International Conference on Communication Systems Networks (COMSNETS). :410—413.
Wireless communication network (WCN) performance is primarily depends on physical layer security which is critical among all other layers of OSI network model. It is typically prone to anomaly/malicious user's attacks owing to openness of wireless channels. Cognitive radio networking (CRN) is a recently emerged wireless technology that is having numerous security challenges because of its unlicensed access of wireless channels. In CRNs, the security issues occur mainly during spectrum sensing and is more pronounced during distributed spectrum sensing. In recent past, various anomaly effects are modelled and developed detectors by applying advanced statistical techniques. Nevertheless, many of these detectors have been developed based on sensing data of one variable (energy measurement) and degrades their performance drastically when the data is contaminated with multiple anomaly nodes, that attack the network cooperatively. Hence, one has to develop an efficient multiple anomaly detection algorithm to eliminate all possible cooperative attacks. To achieve this, in this work, the impact of anomaly on detection probability is verified beforehand in developing an efficient algorithm using bivariate data to detect possible attacks with mahalanobis distance measure. Result discloses that detection error of cooperative attacks by anomaly has significant impact on eigenvalue-based sensing.
2020-02-26
Nowak, Mateusz, Nowak, Sławomir, Domańska, Joanna.  2019.  Cognitive Routing for Improvement of IoT Security. 2019 IEEE International Conference on Fog Computing (ICFC). :41–46.

Internet of Things is nowadays growing faster than ever before. Operators are planning or already creating dedicated networks for this type of devices. There is a need to create dedicated solutions for this type of network, especially solutions related to information security. In this article we present a mechanism of security-aware routing, which takes into account the evaluation of trust in devices and packet flows. We use trust relationships between flows and network nodes to create secure SDN paths, not ignoring also QoS and energy criteria. The system uses SDN infrastructure, enriched with Cognitive Packet Networks (CPN) mechanisms. Routing decisions are made by Random Neural Networks, trained with data fetched with Cognitive Packets. The proposed network architecture, implementing the security-by-design concept, was designed and is being implemented within the SerIoT project to demonstrate secure networks for the Internet of Things (IoT).