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Journal articles on the topic 'Wi-Fi attacks'

Author: Grafiati
Published: 28 June 2021
Last updated: 9 February 2022

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1

Dagelić, Ante, Toni Perković, Bojan Vujatović, and Mario Čagalj. "SSID Oracle Attack on Undisclosed Wi-Fi Preferred Network Lists." Wireless Communications and Mobile Computing 2018 (July 22, 2018): 1–15. http://dx.doi.org/10.1155/2018/5153265.

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User’s location privacy concerns have been further raised by today’s Wi-Fi technology omnipresence. Preferred Network Lists (PNLs) are a particularly interesting source of private location information, as devices are storing a list of previously used hotspots. Privacy implications of a disclosed PNL have been covered by numerous papers, mostly focusing on passive monitoring attacks. Nowadays, however, more and more devices no longer transmit their PNL in clear, thus mitigating passive attacks. Hidden PNLs are still vulnerable against active attacks whereby an attacker mounts a fake SSID hotspot set to one likely contained within targeted PNL. If the targeted device has this SSID in the corresponding PNL, it will automatically initiate a connection with the fake hotspot thus disclosing this information to the attacker. By iterating through different SSIDs (from a predefined dictionary) the attacker can eventually reveal a big part of the hidden PNL. Considering user mobility, executing active attacks usually has to be done within a short opportunity window, while targeting nontrivial SSIDs from user’s PNL. The existing work on active attacks against hidden PNLs often neglects both of these challenges. In this paper we propose a simple mathematical model for analyzing active SSID dictionary attacks, allowing us to optimize the effectiveness of the attack under the above constraints (limited window of opportunity and targeting nontrivial SSIDs). Additionally, we showcase an example method for building an effective SSID dictionary using top-N recommender algorithm and validate our model through simulations and extensive real-life tests.
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Reyes-Moncayo, Hector Ivan, Luis Daniel Malaver- Mendoza, and Andrea Lorena Ochoa-Murillo. "Survey of the security risks of Wi-Fi networks based on the information elements of beacon and probe response frames." Scientia et Technica 25, no. 3 (September 30, 2020): 351–57. http://dx.doi.org/10.22517/23447214.23781.

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Wi-Fi networks have become prevalent in homes, businesses, and public places. Wi-Fi is one of the most common means that people use to access digital services like Facebook, WhatsApp, Instagram, email, and even payment platforms. Equipment for deploying Wi-Fi networks is affordable and its basic features are easy to manipulate. In many cases Wi-Fi users do not even have to buy any communication equipment, since Wi-Fi routers are installed by internet service providers (ISP) in the premises of their customers. Wi-Fi equipment, owned either by end users or ISP companies, should be configured as securely as possible to avoid potential attacks. The security capabilities and features of Wi-Fi routers and access points are inserted into beacon and probe response frames. Potential attackers can use sniffing tools like Wireshark to capture these frames and extract information about security features to discover vulnerabilities. In order to assess the security risks of Wi-Fi networks we conducted a survey in which we used Wireshark to capture the traffic from several Wi-Fi networks, and then through a filter we selected the beacon and probe response frames to analyze the security information elements carried by those frames. We came to the conclusion that despite technical recommendations, some security parameters and options are still set in a way that makes networks more prone to attacks. With this paper we want the readers to be aware of the security risks of their Wi-Fi networks, even the ones set up by their internet service providers.
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Kohlios, Christopher, and Thaier Hayajneh. "A Comprehensive Attack Flow Model and Security Analysis for Wi-Fi and WPA3." Electronics 7, no. 11 (October 30, 2018): 284. http://dx.doi.org/10.3390/electronics7110284.

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The presence of wireless communication grows undeniably more prevalent each year. Since the introduction of the IEEE 802.11 standard for Wireless Local Area Networks (WLAN) in 1997, technologies have progressed to provide wireless accessibility to industries and consumers with growing ease and convenience. As the usage of personal devices, such as phones and watches, that connect to the Internet through Wi-Fi increases, wireless attacks on users are becoming more critical. This paper provides a novel attack model to offer an organized and comprehensive view of the possible attacks on Wi-Fi latest security standards. All existing attacks will be investigated, with emphasis on more recent attacks, such as the KRACK and PMKID Dictionary attacks. The main contribution of this paper is to analyze the technology offered in the new Wi-Fi Protected Access III (WPA3) security scheme and provide the first comprehensive security analysis and discussion to determine whether it has addressed the vulnerabilities of its predecessor. An interesting finding of this paper is that WPA3 still lacks in addressing all the issues existing in WPA2 and exploring other mitigations for future research.
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., Rabia, Naveed Ali, Shahzaib Ali, Ahthasham Sajid, and Afia Zafar. "A SECURITY REVIEW OVER WI-FI AND LI-FI." Information Management and Computer Science 3, no. 1 (April 6, 2020): 01–09. http://dx.doi.org/10.26480/imcs.01.2020.01.09.

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Wi-Fi and Li-Fi are two modern era technologies that are used for data communication. Wi-Fi stands for Wireless Fidelity and Li-Fi stands for Light Fidelity. Wi-Fi uses radio frequency (RF) waves to for transmission of data, on the other hand Li-Fi uses light signals or visible light for data communication. Li-Fi provides transmission of data through illumination by sending data through an LED light bulb. Wi-Fi and Li-Fi technology is used in the various applications of daily life. Both the technologies differ in their properties, architecture, and working. Li-Fi is based on the IEEE 802.11ad standard and uses VLC for data communication while Wi-Fi uses the IEEE standard 802.11 on different devices like routers to transmit data. The objective of this research work is to highlight security prospective of these both technologies; various attacks which can occur and their counter measure as well as misconceptions related to both Wi-Fi vs. Li-Fi is presented in this research paper. Wi-Fi is widely implemented in many major and minor fields of life and the capacity it is providing is accessed. Li-fi, on the end hand, is a means of an opportunity to further develop the efficiency and use of data and to provide resources that are required for the future needs.
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Chen, Yongle, Xiaojian Wang, Yuli Yang, and Hong Li. "Location-Aware Wi-Fi Authentication Scheme Using Smart Contract." Sensors 20, no. 4 (February 15, 2020): 1062. http://dx.doi.org/10.3390/s20041062.

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Advanced wireless technology in Internet of Things (IoT) devices is increasing and facing various security threats. The authentication of IoT devices is the first line of defense for the wireless network. Especially in a Wi-Fi network, the existing authentication methods mainly use a password or digital certificate, these methods are inconvenient to manage due to certificate issuance or prone to be attacked because passwords are easily cracked. In this paper, we propose a location-aware authentication scheme using smart contracts to ensure that IoT devices can securely perform Wi-Fi network authentication. The scheme adopts the concept of secondary authentication and consists of two phases: the registration phase, which is mainly designed to complete the generation of the public and private keys, and to link the device information with its related device information; the authentication phase, which is mainly designed to determine whether the requesting device is within a legal location range. We use the smart contract to ensure the credibility and irreparability of the authentication process. Analysis of the attack model and the attacks at different stages proves that this certification scheme is assured, and the simulation results show that the overhead introduced by this scheme is acceptable, this scheme can provide greater security for the Wi-Fi authentication of IoT devices.
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Xin, Liangxiao, David Starobinski, and Guevara Noubir. "Cascading Attacks on Wi-Fi Networks: Theory and Experiments." IEEE Transactions on Control of Network Systems 7, no. 4 (December 2020): 1757–68. http://dx.doi.org/10.1109/tcns.2020.2999452.

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A. Reyes, Abel, Francisco D. Vaca, Gabriel A. Castro Aguayo, Quamar Niyaz, and Vijay Devabhaktuni. "A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System." Electronics 9, no. 10 (October 15, 2020): 1689. http://dx.doi.org/10.3390/electronics9101689.

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The growth of wireless networks has been remarkable in the last few years. One of the main reasons for this growth is the massive use of portable and stand-alone devices with wireless network connectivity. These devices have become essential on the daily basis in consumer electronics. As the dependency on wireless networks has increased, the attacks against them over time have increased as well. To detect these attacks, a network intrusion detection system (NIDS) with high accuracy and low detection time is needed. In this work, we propose a machine learning (ML) based wireless network intrusion detection system (WNIDS) for Wi-Fi networks to efficiently detect attacks against them. The proposed WNIDS consists of two stages that work together in a sequence. An ML model is developed for each stage to classify the network records into normal or one of the specific attack classes. We train and validate the ML model for WNIDS using the publicly available Aegean Wi-Fi Intrusion Dataset (AWID). Several feature selection techniques have been considered to identify the best features set for the WNIDS. Our two-stage WNIDS achieves an accuracy of 99.42% for multi-class classification with a reduced set of features. A module for eXplainable Artificial Intelligence (XAI) is implemented as well to understand the influence of features on each type of network traffic records.
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Park, Min-Woo, Young-Hyun Choi, Jung-Ho Eom, and Tai-Myoung Chung. "Dangerous Wi-Fi access point: attacks to benign smartphone applications." Personal and Ubiquitous Computing 18, no. 6 (October 29, 2013): 1373–86. http://dx.doi.org/10.1007/s00779-013-0739-y.

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Xin, Liangxiao, and David Starobinski. "Countering Cascading Denial of Service Attacks on Wi-Fi Networks." IEEE/ACM Transactions on Networking 29, no. 3 (June 2021): 1335–48. http://dx.doi.org/10.1109/tnet.2021.3062363.

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Sharma, Kavita, and B. B. Gupta. "Taxonomy of Distributed Denial of Service (DDoS) Attacks and Defense Mechanisms in Present Era of Smartphone Devices." International Journal of E-Services and Mobile Applications 10, no. 2 (April 2018): 58–74. http://dx.doi.org/10.4018/ijesma.2018040104.

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This article describes how in the summer of 1999, the Computer Incident Advisory Capability first reported about Distributed Denial of Service (DDoS) attack incidents and the nature of Denial of Service (DoS) attacks in a distributed environment that eliminates the availability of resources or data on a computer network. DDoS attack exhausts the network resources and disturbs the legitimate user. This article provides an explanation on DDoS attacks and nature of these attacks against Smartphones and Wi-Fi Technology and presents a taxonomy of various defense mechanisms. The smartphone is chosen for this study, as they have now become a necessity rather than a luxury item for the common people.
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Fong, Ken Kin-Kiu, and Stanley Kam Sing Wong. "Wi-Fi Adoption and Security in Hong Kong." Asian Social Science 12, no. 6 (May 20, 2016): 1. http://dx.doi.org/10.5539/ass.v12n6p1.

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<p>WiFi is the fastest and most cost-effective way of wireless Internet connectivity. Nowadays, almost all of the mobile phones and an increasing number of home entertainment systems are WiFi-enabled. Being the key enabler of the “Internet of Everything”, WiFi brings including people, processes, data and devices, together and turns data into valuable information that makes life better and business thrive. With all mobile devices, wearable gadgets, home entertainment systems and home automation systems connected together and linked to the Internet, devices can now interact with one another and data be shared among the devices. However, transmitting information across the WiFi network means leaving your computer or devices vulnerable to attack, giving unscrupulous people the opportunity to intercept traffic, selectively eavesdrop on critical communications or even the administrative access and thus the ability to harvest all the information they want. All these threats highlight the growing importance of keeping your WiFi secure from unauthorized access and malicious attacks.</p><p>Basing on empirically collected quantitative data, this paper presents a comprehensive study on Hong Kong people’s knowledge about WiFi security and their use of WiFi in connecting the Internet. Findings of the study shed light on the knowledge gaps of Hong Kong WiFi users in using and setting up WiFi connections so that service providers, policy makers and stakeholders can devise appropriate security measures to improve the security of WiFi connection. The study also canvasses and analyses the views of the users on the connectivity and quality of free and commercial WiFi service in Hong Kong. The findings can help government and private WiFi operators to further improve the service provided. </p>
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12

Ilham Firdaus, Januar Al Amien, and Soni Soni. "String Matching untuk Mendeteksi Serangan Sniffing (ARP Spoofing) pada IDS Snort." Jurnal CoSciTech (Computer Science and Information Technology) 1, no. 2 (October 31, 2020): 44–49. http://dx.doi.org/10.37859/coscitech.v1i2.2180.

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Sniffing technique (ARP Spoofing) is an attack that sends fake ARP packets or ARP packets that have been modified according to the network address attacker's to poison the victim's ARP cache table. ARP spoofing attack is a dangerous attack because it can monitor the activities of victims in searching the browser and can steal social logins, office and other accounts. This attack supports the occurrence of other computer network attacks such as Denial of service, Man in the middle attack, host impersonating and others. Sniffing attacks are generally found in places that provide public Wi-Fi such as campus, libraries, cafes, and others. IDS Snort can detect sniffing attacks (Arp Spoofing). String Matching Method KMP algorithm is applied to detect attacks on snort logging files to provide alerts (messages) to users. Tests carried out are black box testing to test application functionality, and accuracy testing. All application functionality was successful, and testing the accuracy of the match between manual calculations for string matching and accurate application.
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13

Sharma, Kavita, and B. B. Gupta. "Multi-layer Defense Against Malware Attacks on Smartphone Wi-Fi Access Channel." Procedia Computer Science 78 (2016): 19–25. http://dx.doi.org/10.1016/j.procs.2016.02.005.

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14

Elhamahmy, M., and T. Sobh. "Preventing Information Leakage Caused by War Driving Attacks in Wi-Fi Networks." International Conference on Aerospace Sciences and Aviation Technology 14, AEROSPACE SCIENCES (May 1, 2011): 1–9. http://dx.doi.org/10.21608/asat.2011.23405.

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15

Kalniņš, Rūdolfs, Jānis Puriņš, and Gundars Alksnis. "Security Evaluation of Wireless Network Access Points." Applied Computer Systems 21, no. 1 (May 24, 2017): 38–45. http://dx.doi.org/10.1515/acss-2017-0005.

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Abstract The paper focuses on the real-world usage of IEEE 802.11 wireless network encryption and Wi-Fi Protected Setup (WPS) function. A brief history on the development of encryption methods and WPS is given. Wireless scanning of 802.11 networks in a capital city has been performed, and the results of it have been analysed. To ascertain the knowledge about the security of wireless networks of the average user, an online survey has been conducted. To test the security of encryption methods and WPS function, practical attacks against private test wireless networks have been made. The authors conclude that the safest way to set up 802.11 network with a pre-shared key is to use Wi-Fi Protected Access 2 (WPA2) encryption without support for WPS function. Statistics in Riga shows that networks are often configured otherwise and thus vulnerable to attacks. Survey results prove that respondents are not well informed regarding the security of wireless networks.
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Mirzoev, Timur, and Joel Stacey White. "The Role of Client Isolation in Protecting Wi-Fi Users from ARP Spoofing Attacks." i-manager's Journal on Information Technology 1, no. 2 (May 15, 2012): 11–17. http://dx.doi.org/10.26634/jit.1.2.1779.

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17

Nivaashini, M., and P. Thangaraj. "Computational intelligence techniques for automatic detection of Wi-Fi attacks in wireless IoT networks." Wireless Networks 27, no. 4 (April 15, 2021): 2761–84. http://dx.doi.org/10.1007/s11276-021-02594-2.

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18

Tang, Zhanyong, Yujie Zhao, Lei Yang, Shengde Qi, Dingyi Fang, Xiaojiang Chen, Xiaoqing Gong, and Zheng Wang. "Exploiting Wireless Received Signal Strength Indicators to Detect Evil-Twin Attacks in Smart Homes." Mobile Information Systems 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/1248578.

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Evil-Twin is becoming a common attack in smart home environments where an attacker can set up a fake AP to compromise the security of the connected devices. To identify the fake APs, The current approaches of detecting Evil-Twin attacks all rely on information such as SSIDs, the MAC address of the genuine AP, or network traffic patterns. However, such information can be faked by the attacker, often leading to low detection rates and weak protection. This paper presents a novel Evil-Twin attack detection method based on the received signal strength indicator (RSSI). Our approach considers the RSSI as a fingerprint of APs and uses the fingerprint of the genuine AP to identify fake ones. We provide two schemes to detect a fake AP in two different scenarios where the genuine AP can be located at either a single or multiple locations in the property, by exploiting the multipath effect of the Wi-Fi signal. As a departure from prior work, our approach does not rely on any professional measurement devices. Experimental results show that our approach can successfully detect 90% of the fake APs, at the cost of a one-off, modest connection delay.
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Kovtsur, M., A. Kistruga, G. Voroshnin, and A. Fedorova. "Research of authentication failure and ARP inject attacks and methods of their detection in IEEE 802.11 networks." Telecom IT 9, no. 1 (March 31, 2021): 87–98. http://dx.doi.org/10.31854/2307-1303-2021-9-1-87-98.

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Nowadays, Wi-Fi technology is used everywhere in the world: in the offices of large companies, public places and in simple home premises. However, the use of a public transmission medium leads to the possibility of various attacks. This article discusses attacks such as authentication failure and Address Resolution Protocol inject. The considered attacks have shown high efficiency and danger for wireless networks. In this connection, methods of detection and protection against them were proposed. Methods. At the first stage, the concept of how attacks work, their main idea and purpose, were analyzed. At the subsequent stages, for testing, a laboratory stand was created, on which they were simulated. In the course of the experiments, the effectiveness of attacks was found out, and the main phases of attacks and the elements that contribute to their detection were graphically reflected in the article. Main results. As a result of the work done, it was possible to isolate attack vectors and identify their accompanying anomalies. Based on the research materials, mechanisms for detecting and preventing the attacks under consideration were developed. The practical part. All theoretical and experimental materials collected in the article can be used in detecting and preventing attacks on wireless networks by network administra-tors and information security specialists.
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Abdul Malik, Mohd Mirza, Mohd Nizam Osman, and Mushahadah Maghribi. "Monitoring and Evaluating Open Wireless LAN using Hybrid IDS." Journal of Computing Research and Innovation 4, no. 2 (November 14, 2019): 60–67. http://dx.doi.org/10.24191/jcrinn.v4i2.118.

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In this ever-growing wireless technology era, the number of Open Wireless Local Area Network (WLAN) are on the rise. From cafes to shopping areas, most of them offer users with a free-to-use WLAN which popularly known as Open Wi-Fi or Wi-Fi Hotspots. Although it is a convenient for them to access the Internet at these places, later they know it also makes them a vulnerable target for attackers that might be lurking in the same network they are connected. Therefore, a Hybrid IDS that combines both SNORT, a network-based IDS (NIDS) and OSSEC, a host-based IDS (HIDS) was developed to curb the problem. NIDS was used to monitor network traffics while HIDS monitor user’s system for any suspicious activities. Then, a system that can control and manage both IDS in the much easier and simpler way was developed using Python programming language. The system then can generate alerts with the help of both IDS to notify users for any suspicious activities that might occur in the network or user’s system. Several attacks were launched from the attacker’s laptop to test whether Hybrid IDS can generate alerts to notify the victim. As a result, the system breeze through the testing phase by showing necessary output. All the results were taken and then compared with other scenarios to determine whether they can give the same results as Hybrid IDS. From the comparison results, it can be said that Hybrid IDS can give the extra protection layer towards Open Wi-Fi users. Therefore, the Hybrid IDS was proven to provide vast tracking detection for suspicious activity in the network environments by monitor and alert the users about malicious activities.
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IVANCIU, M., and M. ALEXANDRU. "Antidrone Wireless Personal Shield." Bulletin of the Transilvania University of Brasov. Series I - Engineering Sciences 13(62), no. 2 (January 26, 2021): 9–14. http://dx.doi.org/10.31926/but.ens.2020.13.62.1.8.

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The evolution of small drone technology raises new challenges regarding personal privacy due to the acquisition and flight of unlicensed small drones. Many of these small drones are controlled over Wi-Fi connection making them inexpensive and easy to control. Latest discoveries in vulnerabilities of wireless network protocols give us the advantage to use cyber-attacks as a method of defense. This paper is addressing general audience on how to create a personal shield against small drones with the advantage of being nonintrusive to the other surrounding wireless networks and, at the same time, protecting your personal privacy of a rogue drone.
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Vitomsky, E. V., and D. N. Somov. "Methods for protecting local wireless network aimed at providing temporary silency of signals and information interaction structure." Issues of radio electronics, no. 3 (April 26, 2020): 35–40. http://dx.doi.org/10.21778/2218-5453-2020-3-35-40.

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This work is devoted to the security problem of wireless local area networks. It is known that against a number of typical attacks, the most common IEEE802.11 networks are poorly protected. Existing ways of solving this problem with the help of regular wireless device software are not effective enough, and against some attacks, it is even not possible. One of the reasons, which complicates the solution of this problem, is low level of temporal and spatial security of modern wireless network. Based on the analysis of common attacks on Wi-Fi networks, original protection methods have been developed to consider this aspect. These methods were embodied in a real device. Protection methods are implemented by slightly modifying the standard software of the device having an open hardware platform. An experiment was carried out using this device, which confirmed the possibility of deploying a local wireless network which security was enhanced using the methods proposed in this paper.
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23

Mugavero, Roberto, Stanislav Abaimov, Federico Benolli, and Valentina Sabato. "Cyber Security Vulnerability Management in CBRN Industrial Control Systems (ICS)." International Journal of Information Systems for Crisis Response and Management 10, no. 2 (April 2018): 49–78. http://dx.doi.org/10.4018/ijiscram.2018040103.

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As cyberattacks are becoming the prevalent types of attacks on critical infrastructures, due protection and effective response are crucial in CBRN facilities. This article explores comprehensive cyber security vulnerability management related to CBRN Control Systems and Industrial Control Systems (ICS) and provides recommendations that will increase CBRN operational cyber security and ensure further platform for the research in the field of operational vulnerability detection and remediation. The article reviews several key issues related to ICS vulnerability management cycle, vulnerability sharing with security developers, patch and network management, cyber offensive threats and threat actors and related cyber security challenges. It covers such specific issues as ICS connectivity to private/public networks, critical ICS accessibility via Web Access, Wi-Fi and/or unauthorised software inside corporate networks. The proposed solutions refer to some areas of vulnerability management for the awareness and development of countermeasures.
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Thompson, Aderonke F., Oghenerukevwe E. Oyinloye, Matthew T. David, and Boniface K. Alese. "A Secured System for Internet Enabled Host Devices." Network and Communication Technologies 5, no. 1 (February 6, 2020): 26. http://dx.doi.org/10.5539/nct.v5n1p26.

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In the world of wireless communication, heterogeneous network topologies such as Wi-Fi and Long-Term Evolution (LTE) the topologies authentication service delivery forms a major challenge with access control; which is sought to be addressed. In this paper, we propose a security model by adapting Capability-based Context Aware Access Control (CCAAC) model for internet-enabled devices for defense against hacking or unauthorized access. The steps applied during the programming of this web application was followed through using the Elliptic-Curve Diffie&ndash;Hellman (ECCDH) algorithm so that the initiation of a random prime number between a range, the encryption and exchange of the devices public keys to the decryption are interpreted the right way to the machine making use of it. The results established a security model that has a good chance of being effective against present cyber-attacks other security loopholes.
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Lu, Qian, Haipeng Qu, Yuzhan Ouyang, and Jiahui Zhang. "SLFAT: Client-Side Evil Twin Detection Approach Based on Arrival Time of Special Length Frames." Security and Communication Networks 2019 (June 2, 2019): 1–10. http://dx.doi.org/10.1155/2019/2718741.

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In general, the IEEE 802.11 network identifiers used by wireless access points (APs) can be easily spoofed. Accordingly, a malicious adversary is able to clone the identity information of a legitimate AP (LAP) to launch evil twin attacks (ETAs). The evil twin is a class of rogue access point (RAP) that masquerades as a LAP and allures Wi-Fi victims’ traffic. It enables an attacker with little effort and expenditure to eavesdrop or manipulate wireless communications. Due to the characteristics of strong concealment, high confusion, great harmfulness, and easy implementation, the ETA has become one of the most severe security threats in Wireless Local Area Networks (WLANs). Here, we propose a novel client-side approach, Speical Length Frames Arrival Time (SLFAT), to detect the ETA, which utilizes the same gateway as the LAP. By monitoring the traffic emitted by target APs at a detection node, SLFAT extracts the arrival time of the special frames with the same length to determine the evil twin’s forwarding behavior. SLFAT is passive, lightweight, efficient, hard to be escaped. It allows users to independently detect ETA on ordinary wireless devices. Through implementation and evaluation in our study, SLFAT achieves a very high detection rate in distinguishing evil twins from LAPs.
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Gaber, Tarek, Yassine El Jazouli, Esraa Eldesouky, and Ahmed Ali. "Autonomous Haulage Systems in the Mining Industry: Cybersecurity, Communication and Safety Issues and Challenges." Electronics 10, no. 11 (June 7, 2021): 1357. http://dx.doi.org/10.3390/electronics10111357.

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The current advancement of robotics, especially in Cyber-Physical Systems (CPS), leads to a prominent combination between the mining industry and connected-embedded technologies. This progress has arisen in the form of state-of-the-art automated giant vehicles with Autonomous Haulage Systems (AHS) that can transport ore without human intervention. Like CPS, AHS enable autonomous and/or remote control of physical systems (e.g., mining trucks). Thus, similar to CPS, AHS are also susceptible to cyber attacks such as Wi-Fi De-Auth and GPS attacks. With the use of the AHS, several mining activities have been strengthened due to increasing the efficiency of operations. Such activities require ensuring accurate data collection from which precise information about the state of the mine should be generated in a timely and consistent manner. Consequently, the presence of secure and reliable communications is crucial in making AHS mines safer, productive, and sustainable. This paper aims to identify and discuss the relation between safety of AHS in the mining environment and both cybersecurity and communication as well as highlighting their challenges and open issues. We survey the literature that addressed this aim and discuss its pros and cons and then highlight some open issues. We conclude that addressing cybersecurity issues of AHS can ensure the safety of operations in the mining environment as well as providing reliable communication, which will lead to better safety. Additionally, it was found that new communication technologies, such 5G and LTE, could be adopted in AHS-based systems for mining, but further research is needed to considered related cybersecurity issues and attacks.
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Devi, P. Prasanya, R. S. Kannan, and M. Ravindran. "A Novel Analysis of Wireless Network Monitoring using Wireshark." Restaurant Business 118, no. 7 (July 28, 2019): 27–33. http://dx.doi.org/10.26643/rb.v118i7.7243.

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In this paper, we describe Wireless networks have seen unprecedented rise in their size and number of users in recent years. This unprecedented rise is attributed to the rise in the number of mobile computing devices. Moreover the amount of data that is handled by these wireless networks has increased in recent years. One such wireless network that is widely used but is still prone to attacks is WiFi. Wi-Fi protocol (IEEE 802.11), over the years has been upgraded many times, but these upgrades have mainly resulted in increase in the overall data rate of the communication. Little has been done to improve the security of the protocol. This research focuses on inherent flaws in Wired Equivalent Privacy protocol (WEP) used by the 802.11 standard, Temporal Key Integrity Protocol (TKIP) which is considered an interim solution to legacy 802.11 equipment. The demand for ubiquitous personal communications is driving the development of wireless networks that can accommodate mobile voice and data users who move throughout buildings, cities, or countries. The objective of this chapter is to provide the fundamentals of wireless networks so that the general readers can be able to easily grasp some of the ideas in this area.
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Chuchaev, Alexandr, Yulia Gracheva, and Sergey Malikov. "Attacks on the Information System of Unmanned Vehicles in the Etiology of Road Accidents." Russian Journal of Criminology 15, no. 1 (March 9, 2021): 55–67. http://dx.doi.org/10.17150/2500-4255.2021.15(1).55-67.

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Highly automated vehicles (HAVs) integrate numerous technologies that provide safe and efficient transportation without a driver. Connecting mechanisms ensure communication between vehicles and their infrastructure, and the exchange of data, such as the positioning of the vehicle, its speed, etc. The purpose of each of these functions is to support further automation, which turns a driver from a road user into an observer-controller by transferring to technologies those functions that were earlier performed by people. The automation of control is achieved through a constant analysis of the environment by sensors and the use of previously obtained information, such as maps or data on the road covering, for planning the performance of the vehicle. HAVs use multi-functional hardware and software units that include sensors: lidars, radars, cameras, GPS, odometers, hyrosystems, etc.; systems of data exchange with the road infrastructure using GPRS, 5G, Wi-Fi and other standards; software that controls transport systems, including the systems of machine vision, and neural networks for controlling the road situation. The authors present a classification of threats to HAVs’ security and analyze risks connected with the threats of compromising sensors, control and connection mechanisms, as well as the vulnerabilities of the used technologies. They suggest the wording of a number of new articles in Chapter 27 of the Criminal Code of the Russian Federation devoted to HAVs, which incorporate the following: persons who participated in the development of the corresponding software and guidelines for using the vehicle, operators; actions that could inflict damage, a complex of publically dangerous consequences. A detailed description of four groups of subjects (violators) of infringements on the information system of HAVs is presented: persons using the vulnerability of an information system; malware creators; creators of information systems; operators of information systems. These groups should be taken into account when developing the corresponding norms within Chapter 27 of the Criminal Code of the Russian Federation, the features of aggravating circumstances and the individualization of punishment.
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29

Pecorella, Tommaso, Laura Pierucci, and Francesca Nizzi. "“Network Sentiment” Framework to Improve Security and Privacy for Smart Home." Future Internet 10, no. 12 (December 19, 2018): 125. http://dx.doi.org/10.3390/fi10120125.

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A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalls and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and on going threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g., the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT sensors, Wi-Fi and Ethernet connections.
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30

Perković, T., A. Dagelić, M. Bugarić, and M. Čagalj. "On WPA2-Enterprise Privacy in High Education and Science." Security and Communication Networks 2020 (September 7, 2020): 1–11. http://dx.doi.org/10.1155/2020/3731529.

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A plethora of organizations, companies, and foremost universities and educational institutions are using WPA2-Enterprise protocol to allow their end-users to connect to provided Wi-Fi networks. When both the provider’s and the end-user’s devices are configured properly, it is considered one of the safest Wi-Fi connection protocols with the added benefits of having a unique password for every Wi-Fi user. However, a known evil twin attack can be performed to steal users’ Wi-Fi login credentials, if the devices are not configured correctly. Considering the widespread use of Wi-Fi-enabled smartphones and rising concerns regarding users’ privacy, we focus on the privacy aspects of WPA2-Enterprise vulnerabilities mainly on the widespread Eduroam network. We show that device deanonymization is a concerning liability of many Eduroam networks. More than 87% of 1650 devices collected during a two-month test on our university are vulnerable to MAC address deanonymization attack. Furthermore, by analyzing the Eduroam Configuration Assistant Tool of 1066 different institutions around the world, 67% of exported Eduroam profiles having the Wi-Fi device reveal the user’s identity in the clear, thus linking the users with the device’s MAC address. Indeed, the analysis of the configuration profiles has been confirmed by performing the deanonymization attack on a large-scale international music festival in our country, where 70% of the devices have been vulnerable. Additionally, we showcase the psychological aspects of secure Eduroam users, where some are willing to modify secure configuration profiles to gain aspects to certain blocked features. As a result, the attacker is granted with user credentials and IMSI number and provided with access to all Eduroam-related services.
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31

Gu, Xiaolin, Wenjia Wu, Xiaodan Gu, Zhen Ling, Ming Yang, and Aibo Song. "Probe Request Based Device Identification Attack and Defense." Sensors 20, no. 16 (August 17, 2020): 4620. http://dx.doi.org/10.3390/s20164620.

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Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user’s device, which can cause the leakage of user’s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.
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32

Giles, Jim. "City Wi-Fi networks vulnerable to virus attack." New Scientist 201, no. 2693 (January 2009): 19. http://dx.doi.org/10.1016/s0262-4079(09)60277-3.

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33

Lee, Il-Gu, Kyungmin Go, and Jung Hoon Lee. "Battery Draining Attack and Defense against Power Saving Wireless LAN Devices." Sensors 20, no. 7 (April 5, 2020): 2043. http://dx.doi.org/10.3390/s20072043.

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Wi-Fi technology connects sensor-based things that operate with small batteries, and allows them to access the Internet from anywhere at any time and perform networking. It has become a critical element in many areas of daily life and industry, including smart homes, smart factories, smart grids, and smart cities. The Wi-Fi-based Internet of things is gradually expanding its range of uses from new industries to areas that are intimately connected to people’s lives, safety, and property. Wi-Fi technology has undergone a 20-year standardization process and continues to evolve to improve transmission speeds and service quality. Simultaneously, it has also been strengthening power-saving technology and security technology to improve energy efficiency and security while maintaining backward compatibility with past standards. This study analyzed the security vulnerabilities of the Wi-Fi power-saving mechanism used in smart devices and experimentally proved the feasibility of a battery draining attack (BDA) on commercial smartphones. The results of the experiment showed that when a battery draining attack was performed on power-saving Wi-Fi, 14 times the amount of energy was consumed compared with when a battery draining attack was not performed. This study analyzed the security vulnerabilities of the power-saving mechanism and discusses countermeasures.
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34

Dondyk, Erich, Louis Rivera, and Cliff C. Zou. "Wi-Fi access denial of service attack to smartphones." International Journal of Security and Networks 8, no. 3 (2013): 117. http://dx.doi.org/10.1504/ijsn.2013.057698.

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35

Omollo, Vincent N., and Ruth K. Chweya. "Practical Attack on Wi-Fi Protected Access Version 2 Authentication Protocol." Universal Journal of Communications and Network 3, no. 2 (May 2015): 35–40. http://dx.doi.org/10.13189/ujcn.2015.030201.

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36

Bartoli, Alberto. "Understanding Server Authentication in WPA3 Enterprise." Applied Sciences 10, no. 21 (November 6, 2020): 7879. http://dx.doi.org/10.3390/app10217879.

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In December 2019, the Wi-Fi Alliance published version 2 of WPA3, the new certification program for Wi-Fi devices that updates WPA2. This new version of WPA3 addresses, amongst other things, one of the crucial weaknesses of WPA2: in many practical deployments of enterprise Wi-Fi networks—i.e., networks in which users have personalized credentials—a device may easily be attacked by fraudulent access points claiming to have the name of the targeted network (evil twins). In this work, we present the mechanisms that WPA3 version 2 has introduced for mitigating these risks, which have become more and more relevant in recent years. We discuss the defensive power and potential impact of the various options available. Understanding the resulting scenario is important because WPA3 will determine the behavior of such a fundamental and widespread technology as enterprise Wi-Fi for many years, yet WPA3 enterprise networks may still be configured in a way that could not provide much better defensive power than WPA2.
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37

Bae, Hee-Ra, Min-Young Kim, Su-Kyung Song, Seul-Gi Lee, and Young-Hyun Chang. "Security Attack Analysis for Wireless Router and Free Wi-Fi Hacking Solutions." Journal of the convergence on culture technology 2, no. 4 (December 30, 2016): 65–70. http://dx.doi.org/10.17703/jcct.2016.2.4.65.

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38

Faisol, Ahmad, and Imam Izzat Muttaqin. "IMPLEMENTASI SENSOR MONITORING PADA JARINGAN WI-FI (HOTSPOT) BERBASIS SNORT." Jurnal Teknologi Informasi dan Terapan 5, no. 2 (April 3, 2019): 141–46. http://dx.doi.org/10.25047/jtit.v5i2.92.

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The wireless networking application besides giving the simplicity in communication or data exchange, also has a weakness in security system. Every user's tools that connected to the wireless network must be ready towards the appearance of the destruction or attack, because wireless network doesn't have a clear defense track. On this research, monitoring sensor application based on Snort is being suggested as one of the solutions that can be used to help the network arrangement in monitoring the condition of the network and analyze every dangerous package that is in the network. Snort will detect the intruder and analyze the package that cross the network directly and recording into the data storage media. Monitoring sensor is using rule-base system that will detect every package based on the directions that has defined to the direction data collection. The result of the research shown every new data package that entry the sensor, so the change of the event's amount of the monitoring sensor will change automatically based on the admin's arrangements. More data that cross the sensor, can influence the activity from the server that shown by the slow response from the web server.
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39

Agarwal, Mayank, Santosh Biswas, and Sukumar Nandi. "Advanced Stealth Man-in-The-Middle Attack in WPA2 Encrypted Wi-Fi Networks." IEEE Communications Letters 19, no. 4 (April 2015): 581–84. http://dx.doi.org/10.1109/lcomm.2015.2400443.

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40

Ko, DongHyun, Seok-Hwan Choi, Sungyong Ahn, and Yoon-Ho Choi. "Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack." Sensors 20, no. 23 (November 26, 2020): 6756. http://dx.doi.org/10.3390/s20236756.

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With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.
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41

Nakhila, Omar, Muhammad Faisal Amjad, Erich Dondyk, and Cliff Zou. "Gateway independent user-side wi-fi Evil Twin Attack detection using virtual wireless clients." Computers & Security 74 (May 2018): 41–54. http://dx.doi.org/10.1016/j.cose.2017.12.009.

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42

Du, Yi-Hong, and Shi-Hua Liu. "Epidemic Model of Algorithm-Enhanced Dedicated Virus through Networks." Security and Communication Networks 2018 (June 7, 2018): 1–7. http://dx.doi.org/10.1155/2018/4691203.

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Wi-Fi networks almost cover all active areas around us and, especially in some densely populated regions, Wi-Fi signals are strongly overlapped. The broad and overlapped coverage brings much convenience at the cost of great security risks. Conventionally, a worm virus can infect a router and then attack other routers within its signal coverage. Nowadays, artificial intelligence enables us to solve problems efficiently from available data via computer algorithm. In this paper, we endow the virus with some abilities and present a dedicated worm virus which can pick susceptible routers with kernel density estimation (KDE) algorithm as the attacking tasks automatically. This virus can also attack lower-encryption-level routers first and acquire fast-growing numbers of infected routers on the initial stage. We simulate an epidemic behavior in the collected spatial coordinate of routers in a typical area in Beijing City, where 56.0% routers are infected in 18 hours. This dramatical defeat benefits from the correct infection seed selection and a low-encryption-level priority. This work provides a framework for a computer-algorithm-enhanced virus exploration and gives some insights on offence and defence to both hackers and computer users.
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43

Rizzi, Antonello, Giuseppe Granato, and Andrea Baiocchi. "Frame-by-frame Wi-Fi attack detection algorithm with scalable and modular machine-learning design." Applied Soft Computing 91 (June 2020): 106188. http://dx.doi.org/10.1016/j.asoc.2020.106188.

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44

S, Smys, Haoxiang Wang, and Abul Basar. "5G Network Simulation in Smart Cities using Neural Network Algorithm." March 2021 3, no. 1 (March 29, 2021): 43–52. http://dx.doi.org/10.36548/jaicn.2021.1.004.

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The speed of internet has increased dramatically with the introduction of 4G and 5G promises an even greater transmission rate with coverage outdoors and indoors in smart cities. This indicates that the introduction of 5G might result in replacing the Wi-Fi that is being currently used for applications such as geo-location using continuous radio coverage there by initiating the involvement of IoT in all devices that are used. The introduction of Wi-Fi 6 is already underway for applications that work with IoT, smart city applications will still require 5G to provide internet services using Big Data to reduce the requirement of mobile networks and additional private network infrastructure. However, as the network access begins to expand, it also introduces the risk of cyber security with the enhanced connectivity in the networking. Additional digital targets will be given to the cyber attackers and independent services will also be sharing access channel infrastructure between mobile and wireless network. In order to address these issues, we have introduced a random neural network blockchain technology that can be used to strengthen cybersecurity in many applications. Here the identity of the user is maintained as a secret while the information is codified using neural weights. However, when a cyber security breach occurs, the attacker will be easily tracked by mining the confidential identity. Thus a reliable and decentralized means of authentication method is proposed in this work. The results thus obtained are validated and shows that the introduction of the random neural network using blockchain improves connectivity, decentralized user access and cyber security resilience.
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45

Park, Geun-Duk, Jeong-Soo Park, and Jae-Cheol Ha. "A Secure WPA-PSK Protocol Resistant to Dictionary Attack on Smartphone Communication Using Wi-Fi Channel." Journal of the Korea Academia-Industrial cooperation Society 13, no. 4 (April 30, 2012): 1839–48. http://dx.doi.org/10.5762/kais.2012.13.4.1839.

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46

Agarwal, Mayank, Santosh Biswas, and Sukumar Nandi. "An Efficient Scheme to Detect Evil Twin Rogue Access Point Attack in 802.11 Wi-Fi Networks." International Journal of Wireless Information Networks 25, no. 2 (March 29, 2018): 130–45. http://dx.doi.org/10.1007/s10776-018-0396-1.

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47

Yun, Junhyeok, and Mihui Kim. "SybilEye: Observer-Assisted Privacy-Preserving Sybil Attack Detection on Mobile Crowdsensing." Information 11, no. 4 (April 9, 2020): 198. http://dx.doi.org/10.3390/info11040198.

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Mobile crowdsensing is a data collection system using widespread mobile devices with various sensors. The data processor cannot manage all mobile devices participating in mobile crowdsensing. A malicious user can conduct a Sybil attack (e.g., achieve a significant influence through extortion or the generation of fake IDs) to receive an incentive or destroy a system. A mobile crowdsensing system should, thus, be able to detect and block a Sybil attack. Existing Sybil attack detection mechanisms for wireless sensor networks cannot apply directly to mobile crowdsensing owing to the privacy issues of the participants and detection overhead. In this paper, we propose an effective privacy-preserving Sybil attack detection mechanism that distributes observer role to the users. To demonstrate the performance of our mechanism, we implement a Wi-Fi-connection-based Sybil attack detection model and show its feasibility by evaluating the detection performance.
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48

Singh, Alka, and Chitra Singh. "Quad-band FSS for Electromagnetic Shielding." International Journal of Computer Communication and Informatics 3, no. 1 (May 30, 2021): 1–14. http://dx.doi.org/10.34256/ijcci2111.

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In this paper, a novel method to design Quad-band stop frequency selective surface (FSS) is proposed to shield medical/non-medical devices from electromagnetic radiation of wifi/Bluetooth, GPS,1G, 2G, 3G, 4G and 5G frequencies. The proposed device can also be used in security and surveillance devices to protect them from electromagnetic attack. The FSS unit cell consists of convoluted structures on FR4 substrate. The study was performed by designing individual frequency selective surfaces for 1G, 2G, Wi-Fi/Bluetooth, GPS, 4G and 5G, then combining it to form a single Quad-band FSS unit.
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49

Johnson, Tyler, Daniel Roggow, Phillip H. Jones, and Joseph Zambreno. "An FPGA Architecture for the Recovery of WPA/WPA2 Keys." Journal of Circuits, Systems and Computers 24, no. 07 (June 17, 2015): 1550105. http://dx.doi.org/10.1142/s0218126615501054.

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Wi-Fi protected access (WPA) has provided serious improvements over the now deprecated wired equivalent privacy (WEP) protocol. WPA, however, still has some flaws that allow an attacker to obtain the passphrase. One of these flaws is exposed when the access point (AP) is operating in the WPA personal mode. This is the most common mode, as it is the quickest and easiest to configure. This vulnerability requires the attacker to capture the traffic from the four-way handshake between the AP and client, and then have enough compute time to reverse the passphrase. Increasing the rate at which passphrases can be reversed reduces the amount of time required to construct a repository of service set identifiers (SSIDs) and passphrases, which can increase the chances an attack is successful, or, alternatively, reduce the difficulty of auditing a wireless network for security purposes. This work focuses on creating an field programmable gate array (FPGA)-based architecture to accelerate the generation of a WPA/WPA2 pairwise master key (PMK) lookup table (LUT) for the recovery of the passphrase, with special emphasis on the secure hash algorithm-1 (SHA-1) implementation. PMK generation relies heavily on SHA-1 hashing and, as this work shows, an optimized SHA-1 implementation can achieve up to a 40 × speedup over an unoptimized implementation when generating PMKs.
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Hsneh, Yu Hsiang, Fu Hau Hsu, Shih Jen Chen, Yao Hsin Chen, Yan Ling Hwang, Chuan Sheng Wang, and Yu Liang Hsu. "ET Detector: A Client-Based Solution to Detect an Evil Twin Access Point." Applied Mechanics and Materials 764-765 (May 2015): 900–904. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.900.

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An evil twin is a kind of rogue Wi-Fi access point (AP) which has the same SSID name to the legitimate one and is set up by an adversary. The user who associates to the evil twin, adversary may eaves-drops sensitive data on wireless communications to the Internet. Most of existing detection solutions are administrator-based, which is used by wireless network administrator to verify whether a given AP is in an authorized list or not. Such administrator-based solutions are limited, hardly maintained, and difficult to protect users timely when the attack is launched. Hence, we propose a client-based detection by operating the wireless network interface controller (WNIC) in monitor mode and capturing the Internet packets. Through analyzing the captured packets, client users can easily and precisely detect the evil twin attack even in a complicated AP scenario as well as avoid any threat arisen from connecting to an AP. Our method does not need to know any authorized AP list, and does not rely on data training or machine learning technique of target wireless network. Finally, we implemented a detecting system on Windows 7, which is widely used nowadays.
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