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Son, W., Jung, B. C., Kim, C., Kim, J. M..  2018.  Pseudo-Random Beamforming with Beam Selection for Improving Physical-Layer Security. 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN). :382–384.
In this paper, we propose a novel pseudo-random beamforming technique with beam selection for improving physical-layer security (PLS) in a downlink cellular network where consists of a base station (BS) with Ntantennas, NMSlegitimate mobile stations (MSs), and NEeavesdroppers. In the proposed technique, the BS generates multiple candidates of beamforming matrix each of which consists of orthogonal beamforming vectors in a pseudo-random manner. Each legitimate MS opportunistically feeds back the received signal-to-interference-and-noise ratio (SINR) value for all beamforming vectors to the BS. The BS transmits data to the legitimate MSs with the optimal beamforming matrix among multiple beam forming matrices that maximizes the secrecy sum-rate. Simulation results show that the proposed technique outperforms the conventional random beamforming technique in terms of the achievable secrecy sum-rate.
Pawlenka, T., Škuta, J..  2018.  Security system based on microcontrollers. 2018 19th International Carpathian Control Conference (ICCC). :344–347.
The article describes design and realization of security system based on single-chip microcontrollers. System includes sensor modules for unauthorized entrance detection based on magnetic contact, measuring carbon monoxide level, movement detection and measuring temperature and humidity. System also includes control unit, control panel and development board Arduino with ethernet interface connected for web server implementation.
Mamdouh, M., Elrukhsi, M. A. I., Khattab, A..  2018.  Securing the Internet of Things and Wireless Sensor Networks via Machine Learning: A Survey. 2018 International Conference on Computer and Applications (ICCA). :215–218.

The Internet of Things (IoT) is the network where physical devices, sensors, appliances and other different objects can communicate with each other without the need for human intervention. Wireless Sensor Networks (WSNs) are main building blocks of the IoT. Both the IoT and WSNs have many critical and non-critical applications that touch almost every aspect of our modern life. Unfortunately, these networks are prone to various types of security threats. Therefore, the security of IoT and WSNs became crucial. Furthermore, the resource limitations of the devices used in these networks complicate the problem. One of the most recent and effective approaches to address such challenges is machine learning. Machine learning inspires many solutions to secure the IoT and WSNs. In this paper, we survey the different threats that can attack both IoT and WSNs and the machine learning techniques developed to counter them.

Ferres, E., Immler, V., Utz, A., Stanitzki, A., Lerch, R., Kokozinski, R..  2018.  Capacitive Multi-Channel Security Sensor IC for Tamper-Resistant Enclosures. 2018 IEEE SENSORS. :1–4.
Physical attacks are a serious threat for embedded devices. Since these attacks are based on physical interaction, sensing technology is a key aspect in detecting them. For highest security levels devices in need of protection are placed into tamper-resistant enclosures. In this paper we present a capacitive multi-channel security sensor IC in a 350 nm CMOS technology. This IC measures more than 128 capacitive sensor nodes of such an enclosure with an SNR of 94.6 dB across a 16×16 electrode matrix in just 19.7 ms. The theoretical sensitivity is 35 aF which is practically limited by noise to 460 aF. While this is similar to capacitive touch technology, it outperforms available solutions of this domain with respect to precision and speed.
Obert, J., Chavez, A., Johnson, J..  2018.  Behavioral Based Trust Metrics and the Smart Grid. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1490-1493.

To ensure reliable and predictable service in the electrical grid it is important to gauge the level of trust present within critical components and substations. Although trust throughout a smart grid is temporal and dynamically varies according to measured states, it is possible to accurately formulate communications and service level strategies based on such trust measurements. Utilizing an effective set of machine learning and statistical methods, it is shown that establishment of trust levels between substations using behavioral pattern analysis is possible. It is also shown that the establishment of such trust can facilitate simple secure communications routing between substations.

Duan, J., Zeng, Z., Oprea, A., Vasudevan, S..  2018.  Automated Generation and Selection of Interpretable Features for Enterprise Security. 2018 IEEE International Conference on Big Data (Big Data). :1258-1265.

We present an effective machine learning method for malicious activity detection in enterprise security logs. Our method involves feature engineering, or generating new features by applying operators on features of the raw data. We generate DNF formulas from raw features, extract Boolean functions from them, and leverage Fourier analysis to generate new parity features and rank them based on their highest Fourier coefficients. We demonstrate on real enterprise data sets that the engineered features enhance the performance of a wide range of classifiers and clustering algorithms. As compared to classification of raw data features, the engineered features achieve up to 50.6% improvement in malicious recall, while sacrificing no more than 0.47% in accuracy. We also observe better isolation of malicious clusters, when performing clustering on engineered features. In general, a small number of engineered features achieve higher performance than raw data features according to our metrics of interest. Our feature engineering method also retains interpretability, an important consideration in cyber security applications.

Alavizadeh, H., Jang-Jaccard, J., Kim, D. S..  2018.  Evaluation for Combination of Shuffle and Diversity on Moving Target Defense Strategy for Cloud Computing. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :573-578.

Moving Target Defence (MTD) has been recently proposed and is an emerging proactive approach which provides an asynchronous defensive strategies. Unlike traditional security solutions that focused on removing vulnerabilities, MTD makes a system dynamic and unpredictable by continuously changing attack surface to confuse attackers. MTD can be utilized in cloud computing to address the cloud's security-related problems. There are many literature proposing MTD methods in various contexts, but it still lacks approaches to evaluate the effectiveness of proposed MTD method. In this paper, we proposed a combination of Shuffle and Diversity MTD techniques and investigate on the effects of deploying these techniques from two perspectives lying on two groups of security metrics (i) system risk: which is the cloud providers' perspective and (ii) attack cost and return on attack: which are attacker's point of view. Moreover, we utilize a scalable Graphical Security Model (GSM) to enhance the security analysis complexity. Finally, we show that combining MTD techniques can improve both aforementioned two groups of security metrics while individual technique cannot.

Terzi, D. S., Arslan, B., Sagiroglu, S..  2018.  Smart Grid Security Evaluation with a Big Data Use Case. 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018). :1-6.

Technological developments in the energy sector while offering new business insights, also produces complex data. In this study, the relationship between smart grid and big data approaches have been investigated. After analyzing where the big data techniques and technologies are used in which areas of smart grid systems, the big data technologies used to detect attacks on smart grids have been focused on. Big data analytics produces efficient solutions, but it is more critical to choose which algorithm and metric. For this reason, an application prototype has been proposed using big data approaches to detect attacks on smart grids. The algorithms with high accuracy were determined as 92% with Random Forest and 87% with Decision Tree.

Sheikh, Abdullah, Munro, Malcolm, Budgen, David.  2018.  SSM: Scheduling Security Model for a Cloud Environment. Proceedings of the 2018 2Nd International Conference on Cloud and Big Data Computing. :11-15.

Scheduling in the cloud is a complex task due to the number and variety of resources available and the volatility of usage-patterns of resources considering that the resource setting is on the service provider. This complexity is compounded further when Security issues and Quality of Service (QoS) are also factored in. The aim of this paper is to describe a model that based on Security (SSM) as a key element that cloud services rely on which affects the performance, cost and time concerns within the security constraints of the cloud service. Definition of the Scheduling Security Model (SSM), and evaluation through worked example that can meet the customer requirements of cost and the quality of service in the required time.

bt Yusof Ali, Hazirah Bee, bt Abdullah, Lili Marziana, Kartiwi, Mira, Nordin, Azlin.  2018.  Risk Assessment for Big Data in Cloud: Security, Privacy and Trust. Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference. :63-67.

The alarming rate of big data usage in the cloud makes data exposed easily. Cloud which consists of many servers linked to each other is used for data storage. Having owned by third parties, the security of the cloud needs to be looked at. Risks of storing data in cloud need to be checked further on the severity level. There should be a way to access the risks. Thus, the objective of this paper is to use SLR so that we can have extensive background of literatures on risk assessment for big data in cloud computing environment from the perspective of security, privacy and trust.

Mohammedi, M., Omar, M., Aitabdelmalek, W., Mansouri, A., Bouabdallah, A..  2018.  Secure and Lightweight Biometric-Based Remote Patient Authentication Scheme for Home Healthcare Systems. 2018 International Symposium on Programming and Systems (ISPS). :1-6.

Recently, the home healthcare system has emerged as one of the most useful technology for e-healthcare. Contrary to classical recording methods of patient's medical data, which are, based on paper documents, nowadays all this sensitive data can be managed and forwarded through digital systems. These make possible for both patients and healthcare workers to access medical data or receive remote medical treatment using wireless interfaces whenever and wherever. However, simplifying access to these sensitive and private data can directly put patient's health and life in danger. In this paper, we propose a secure and lightweight biometric-based remote patient authentication scheme using elliptic curve encryption through which two mobile healthcare system communication parties could authenticate each other in public mobile healthcare environments. The security and performance analysis demonstrate that our proposal achieves better security than other concurrent schemes, with lower storage, communication and computation costs.

Azzaz, M. S., Tanougast, C., Maali, A., Benssalah, M..  2018.  Hardware Implementation of Multi-Scroll Chaos Based Architecture for Securing Biometric Templates. 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT). :227-231.

In spite of numerous advantages of biometrics-based personal authentication systems over traditional security systems based on token or knowledge, they are vulnerable to attacks that can decrease their security considerably. In this paper, we propose a new hardware solution to protect biometric templates such as fingerprint. The proposed scheme is based on chaotic N × N grid multi-scroll system and it is implemented on Xilinx FPGA. The hardware implementation is achieved by applying numerical solution methods in our study, we use EM (Euler Method). Simulation and experimental results show that the proposed scheme allows a low cost image encryption for embedded systems while still providing a good trade-off between performance and hardware resources. Indeed, security analysis performed to the our scheme, is strong against known different attacks, such as: brute force, statistical, differential, and entropy. Therefore, the proposed chaos-based multiscroll encryption algorithm is suitable for use in securing embedded biometric systems.

Chen, L., Liu, J., Ha, W..  2018.  Cloud Service Risk in the Smart Grid. 2018 14th International Conference on Computational Intelligence and Security (CIS). :242–244.

Smart grid utilizes cloud service to realize reliable, efficient, secured, and cost-effective power management, but there are a number of security risks in the cloud service of smart grid. The security risks are particularly problematic to operators of power information infrastructure who want to leverage the benefits of cloud. In this paper, security risk of cloud service in the smart grid are categorized and analyzed characteristics, and multi-layered index system of general technical risks is established, which applies to different patterns of cloud service. Cloud service risk of smart grid can evaluate according indexes.

Albarakati, A., Moussa, B., Debbabi, M., Youssef, A., Agba, B. L., Kassouf, M..  2018.  OpenStack-Based Evaluation Framework for Smart Grid Cyber Security. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1–6.

The rapid evolution of the power grid into a smart one calls for innovative and compelling means to experiment with the upcoming expansions, and analyze their behavioral response under normal circumstances and when targeted by attacks. Such analysis is fundamental to setting up solid foundations for the smart grid. Smart grid Hardware-In-the-Loop (HIL) co-simulation environments serve as a key approach to answer questions on the systems components, functionality, security concerns along with analysis of the system outcome and expected behavior. In this paper, we introduce a HIL co-simulation framework capable of simulating the smart grid actions and responses to attacks targeting its power and communication components. Our testbed is equipped with a real-time power grid simulator, and an associated OpenStack-based communication network. Through the utilized communication network, we can emulate a multitude of attacks targeting the power system, and evaluating the grid response to those attacks. Moreover, we present different illustrative cyber attacks use cases, and analyze the smart grid behavior in the presence of those attacks.

Yongdong, C., Wei, W., Yanling, Z., Jinshuai, W..  2018.  Lightweight Security Signaling Mechanism in Optical Network for Smart Power Grid. 2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET). :110–113.

The communication security issue brought by Smart Grid is of great importance and should not be ignored in backbone optical networks. With the aim to solve this problem, this paper firstly conducts deep analysis into the security challenge of optical network under smart power grid environment and proposes a so-called lightweight security signaling mechanism of multi-domain optical network for Energy Internet. The proposed scheme makes full advantage of current signaling protocol with some necessary extensions and security improvement. Thus, this lightweight security signaling protocol is designed to make sure the end-to-end trusted connection. Under the multi-domain communication services of smart power grid, evaluation simulation for the signaling interaction is conducted. Simulation results show that this proposed approach can greatly improve the security level of large-scale multi-domain optical network for smart power grid with better performance in term of connection success rate performance.

Zhou, Liang, Ouyang, Xuan, Ying, Huan, Han, Lifang, Cheng, Yushi, Zhang, Tianchen.  2018.  Cyber-Attack Classification in Smart Grid via Deep Neural Network. Proceedings of the 2Nd International Conference on Computer Science and Application Engineering. :90:1–90:5.
Smart grid1 is a modern power transmission network. With its development, the computing, communication and physical processes is getting more and more connected. However, an adversary can destroy power production by attacking the power secondary equipment. Accurate and fast response to cyber-attacks is a prerequisite for stable grid operation. Therefore, it is critical to identify and classify attacks in the smart grid. In this paper, we propose a novel approach that utilizes machine learning algorithms to help classify cyber-attacks. We built a deep neural network (DNN) model and select the global optimal parameters to achieve high generalization performance. The evaluation result demonstrates that the proposed method can effectively identify cyber-attacks in smart grid with an accuracy as high as 96%.
Cui, X., Wu, K., Karri, R..  2018.  Hardware Trojan Detection Using Path Delay Order Encoding with Process Variation Tolerance. 2018 IEEE 23rd European Test Symposium (ETS). :1-2.

The outsourcing for fabrication introduces security threats, namely hardware Trojans (HTs). Many design-for-trust (DFT) techniques have been proposed to address such threats. However, many HT detection techniques are not effective due to the dependence on golden chips, limitation of useful information available and process variations. In this paper, we data-mine on path delay information and propose a variation-tolerant path delay order encoding technique to detect HTs.

Kostyria, O., Storozhenko, V., Naumenko, V., Romanov, Y..  2018.  Mathematical Models of Blocks for Compensation Multipath Distortion in Spatially Separated Passive Time-Frequency Synchronization Radio System. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S T). :104-108.
Multipath propagation of radio waves negatively affects to the performance of telecommunications and radio navigation systems. When performing time and frequency synchronization tasks of spatially separated standards, the multipath signal propagation aggravates the probability of a correct synchronization and introduces an error. The presence of a multipath signal reduces the signal-to-noise ratio in the received signal, which in turn causes an increase in the synchronization error. If the time delay of the additional beam (s) is less than the useful signal duration, the reception of the useful signal is further complicated by the presence of a partially correlated interference, the level and correlation degree of which increases with decreasing time delay of the interference signals. The article considers with the method of multi-path interference compensation in a multi-position (telecommunication or radio navigation system) or a time and frequency synchronization system for the case if at least one of the receiving positions has no noise signal or does not exceed the permissible level. The essence of the method is that the interference-free useful signal is transmitted to other points in order to pick out the interference component from the signal / noise mix. As a result, an interference-free signal is used for further processing. The mathematical models of multipath interference suppressors in the temporal and in the frequency domain are presented in the article. Compared to time processing, processing in the frequency domain reduces computational costs. The operation of the suppressor in the time domain has been verified experimentally.
Yazicigil, R. T., Nadeau, P., Richman, D., Juvekar, C., Vaidya, K., Chandrakasan, A. P..  2018.  Ultra-Fast Bit-Level Frequency-Hopping Transmitter for Securing Low-Power Wireless Devices. 2018 IEEE Radio Frequency Integrated Circuits Symposium (RFIC). :176-179.
Current BLE transmitters are susceptible to selective jamming due to long dwell times in a channel. To mitigate these attacks, we propose physical-layer security through an ultra-fast bit-level frequency-hopping (FH) scheme by exploiting the frequency agility of bulk acoustic wave resonators (BAW). Here we demonstrate the first integrated bit-level FH transmitter (TX) that hops at 1$μ$s period and uses data-driven random dynamic channel selection to enable secure wireless communications with additional data encryption. This system consists of a time-interleaved BAW-based TX implemented in 65nm CMOS technology with 80MHz coverage in the 2.4GHz ISM band and a measured power consumption of 10.9mW from 1.1V supply.
Park, Jungmin, Xu, Xiaolin, Jin, Yier, Forte, Domenic, Tehranipoor, Mark.  2018.  Power-Based Side-Channel Instruction-Level Disassembler. Proceedings of the 55th Annual Design Automation Conference. :119:1-119:6.
Modern embedded computing devices are vulnerable against malware and software piracy due to insufficient security scrutiny and the complications of continuous patching. To detect malicious activity as well as protecting the integrity of executable software, it is necessary to monitor the operation of such devices. In this paper, we propose a disassembler based on power-based side-channel to analyze the real-time operation of embedded systems at instruction-level granularity. The proposed disassembler obtains templates from an original device (e.g., IoT home security system, smart thermostat, etc.) and utilizes machine learning algorithms to uniquely identify instructions executed on the device. The feature selection using Kullback-Leibler (KL) divergence and the dimensional reduction using PCA in the time-frequency domain are proposed to increase the identification accuracy. Moreover, a hierarchical classification framework is proposed to reduce the computational complexity associated with large instruction sets. In addition, covariate shifts caused by different environmental measurements and device-to-device variations are minimized by our covariate shift adaptation technique. We implement this disassembler on an AVR 8-bit microcontroller. Experimental results demonstrate that our proposed disassembler can recognize test instructions including register names with a success rate no lower than 99.03% with quadratic discriminant analysis (QDA).
Jourdan, Théo, Boutet, Antoine, Frindel, Carole.  2018.  Toward Privacy in IoT Mobile Devices for Activity Recognition. Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. :155-165.
Recent advances in wireless sensors for personal healthcare allow to recognise human real-time activities with mobile devices. While the analysis of those datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this paper, we propose a privacy-preserving framework for activity recognition. This framework relies on a machine learning technique to efficiently recognise the user activity pattern, useful for personal healthcare monitoring, while limiting the risk of re-identification of users from biometric patterns that characterizes each individual. To achieve that, we first deeply analysed different features extraction schemes in both temporal and frequency domain. We show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. On the basis of this observation, we second design a novel protection mechanism that processes the raw signal on the user's smartphone and transfers to the application server only the relevant features unlinked to the identity of users. In addition, a generalisation-based approach is also applied on features in frequency domain before to be transmitted to the server in order to limit the risk of re-identification. We extensively evaluate our framework with a reference dataset: results show an accurate activity recognition (87%) while limiting the re-identifation rate (33%). This represents a slightly decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.
Keshishzadeh, Sarineh, Fallah, Ali, Rashidi, Saeid.  2018.  Electroencephalogram Based Biometrics: A Fractional Fourier Transform Approach. Proceedings of the 2018 2Nd International Conference on Biometric Engineering and Applications. :1-5.
The non-stationary nature of the human Electroencephalogram (EEG) has caused problems in EEG based biometrics. Stationary signals analysis is done simply with Discrete Fourier Transform (DFT), while it is not possible to analyze non-stationary signals with DFT, as it does not have the ability to show the occurrence time of different frequency components. The Fractional Fourier Transform (FrFT), as a generalization of Fourier Transform (FT), has the ability to exhibit the variable frequency nature of non-stationary signals. In this paper, Discrete Fractional Fourier Transform (DFrFT) with different fractional orders is proposed as a novel feature extraction technique for EEG based human verification with different number of channels. The proposed method in its' best performance achieved 0.22% Equal Error Rate (EER) with three EEG channels of 104 subjects.
Zhang, Sheng, Tang, Adrian, Jiang, Zhewei, Sethumadhavan, Simha, Seok, Mingoo.  2018.  Blacklist Core: Machine-Learning Based Dynamic Operating-Performance-Point Blacklisting for Mitigating Power-Management Security Attacks. Proceedings of the International Symposium on Low Power Electronics and Design. :5:1-5:6.
Most modern computing devices make available fine-grained control of operating frequency and voltage for power management. These interfaces, as demonstrated by recent attacks, open up a new class of software fault injection attacks that compromise security on commodity devices. CLKSCREW, a recently-published attack that stretches the frequency of devices beyond their operational limits to induce faults, is one such attack. Statically and permanently limiting frequency and voltage modulation space, i.e., guard-banding, could mitigate such attacks but it incurs large performance degradation and long testing time. Instead, in this paper, we propose a run-time technique which dynamically blacklists unsafe operating performance points using a neural-net model. The model is first trained offline in the design time and then subsequently adjusted at run-time by inspecting a selected set of features such as power management control registers, timing-error signals, and core temperature. We designed the algorithm and hardware, titled a BlackList (BL) core, which is capable of detecting and mitigating such power management-based security attack at high accuracy. The BL core incurs a reasonably small amount of overhead in power, delay, and area.
Lakshminarayana, Subhash, Karachiwala, Jabir Shabbir, Chang, Sang-Yoon, Revadigar, Girish, Kumar, Sristi Lakshmi Sravana, Yau, David K.Y., Hu, Yih-Chun.  2018.  Signal Jamming Attacks Against Communication-Based Train Control: Attack Impact and Countermeasure. Proceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks. :160-171.
We study the impact of signal jamming attacks against the communication based train control (CBTC) systems and develop the countermeasures to limit the attacks' impact. CBTC supports the train operation automation and moving-block signaling, which improves the transport efficiency. We consider an attacker jamming the wireless communication between the trains or the train to wayside access point, which can disable CBTC and the corresponding benefits. In contrast to prior work studying jamming only at the physical or link layer, we study the real impact of such attacks on end users, namely train journey time and passenger congestion. Our analysis employs a detailed model of leaky medium-based communication system (leaky waveguide or leaky feeder/coaxial cable) popularly used in CBTC systems. To counteract the jamming attacks, we develop a mitigation approach based on frequency hopping spread spectrum taking into account domain-specific structure of the leaky-medium CBTC systems. Specifically, compared with existing implementations of FHSS, we apply FHSS not only between the transmitter-receiver pair but also at the track-side repeaters. To demonstrate the feasibility of implementing this technology in CBTC systems, we develop a FHSS repeater prototype using software-defined radios on both leaky-medium and open-air (free-wave) channels. We perform extensive simulations driven by realistic running profiles of trains and real-world passenger data to provide insights into the jamming attack's impact and the effectiveness of the proposed countermeasure.
Queiroz, Diego V., Gomes, Ruan D., Benavente-Peces, Cesar, Fonseca, Iguatemi E., Alencar, Marcelo S..  2018.  Evaluation of Channels Blacklists in TSCH Networks with Star and Tree Topologies. Proceedings of the 14th ACM International Symposium on QoS and Security for Wireless and Mobile Networks. :116-123.
The Time-Slotted Channel Hopping (TSCH) mode, defined by the IEEE 802.15.4e protocol, aims to reduce the effects of narrowband interference and multipath fading on some channels through the frequency hopping method. To work satisfactorily, this method must be based on the evaluation of the channel quality through which the packets will be transmitted to avoid packet losses. In addition to the estimation, it is necessary to manage channel blacklists, which prevents the sensors from hopping to bad quality channels. The blacklists can be applied locally or globally, and this paper evaluates the use of a local blacklist through simulation of a TSCH network in a simulated harsh industrial environment. This work evaluates two approaches, and both use a developed protocol based on TSCH, called Adaptive Blacklist TSCH (AB-TSCH), that considers beacon packets and includes a link quality estimation with blacklists. The first approach uses the protocol to compare a simple version of TSCH to configurations with different sizes of blacklists in star topology. In this approach, it is possible to analyze the channel adaption method that occurs when the blacklist has 15 channels. The second approach uses the protocol to evaluate blacklists in tree topology, and discusses the inherent problems of this topology. The results show that, when the estimation is performed continuously, a larger blacklist leads to an increase of performance in star topology. In tree topology, due to the simultaneous transmissions among some nodes, the use of smaller blacklist showed better performance.