As an MSc research student, you are expected to understand how other areas of computer science relate to your chosen specialism. You must have a grasp of significant current open problems in other specialisms, as well as an understanding of how techniques and methods from other research areas can be applied within your specialism.
In this assignment you must provide an overview of two MSc Research specialism lectures (AI / Networking / Cyber Security / Software Engineering / Data Science) which are NOT the specialism you discussed in Assignment 1.
For each of these two areas, you must discuss the research question presented within the relevant specialist lecture for this specialism, propose some research approaches to investigate this question and identify further work you might undertake which builds on this. You must also explore your personal strengths in this area.
PART 1- COMPUTER NETWORKS AND SYSTEM SECURITY
A computer network (CN) can be termed as a connection between two or more than two computers for sharing different information and resources. The concept of CN is a broad approach of computer science that guides the electronic sharing of data. Spectrum sensing (SS) is a strategy of monitoring any specific band of frequency that supports easy and effective identification of absence of different primary users. The specialism of this research is identification of SS and its use in cognitive radio (CR) networking for effective identification of primary users (PUs) and secondary users (SUs). This networking system is very efficient in determination of different spectrum holes using the concept of probability detection (Pd) and false alarm (Pfa) techniques to protect PUs and provide chances for SUs. This study will guide in answering a completely specified research question along with the works on this topic and different approaches for this research.
- What are the intelligent and different energy-efficient mechanisms of spectrum sensing in the improvement of efficiency in cognitive radio networks?
Spectrum sensing is determined as an accurate and an effective networking technology that helps SUs in accurate detection of different holes in the spectrum that can cause security issue for SUs and other PUs. This specific research question is vital in the sense of answering different intelligent process along with energy efficiency strategy related with spectrum sensing in network security. According to Ozger et al. (2018), CR techniques include sharing a large number of incensed bands of the spectrum to PUs. Relation of this question with real world is important, as it will enable details about the CR technologies and their relation with spectrum for maintaining safety in networking systems. Different types of sensing techniques in the spectrum that are used for network systems can be effective in detection of empty spaces in CR systems (Syed & Safdar, 2015). Those empty spaces are the main reason for misuse of spectrums that can create barriers for development of an advanced wireless networking system using CR networking.
SS and its use on networking safety include techniques using which a historical dataset can be analysed by SUs to determine present space in CR networking. CR networking is beneficial in implementation of a completely next-generation advanced technology in improving networking functioning and safety (Bindhu, 2020). As per the case study, system models such as Markov chain have been used in the specified study guide for answering the states associated with spectrum sensing. This question will be accurate in answering real-world problems regarding energy efficiency in spectrum sensing along with robust nature issues, complexity, and low SNR problem.
Unplanned utilisation of energy-efficient strategy of spectrum sensing in networking and security improvement can be a barrier to advancement of CR networking systems.
CR networking is an important wireless technology that utilises spectrum sensing in maintaining networking facilities and security throughout for both PUs and SUs. CR systems generally use different sensor techniques in the transmission of data over the various networking channels (Ren et al. 2018). CR systems are programmed in a specified format that they can easily utilise different advanced dynamic wireless networking by maintaining accurate use of spectrum. In contrast, Li et al. (2017) have argued that SUs get permission for using spectrum only when the Pus are remaining idle in using such spectrum in CR techniques. Therefore, it is obvious that CR networking provides opportunities for the users with effective management of security in spectrum sensing. CR networks have enough positive aspect such as it can be used as a future generation technology in the improvement of wireless connection along with an increase of networking security for users (Saleem et al. 2017).
Figure 1: Cognitive cycle of CR (Source: Bindhu, 2020)
The above image indicates a cognitive cycle using which CR networks function across systems. Spectrum sensing is essential that helps in prevention of different harmful influences of improper utilisation of spectrum during wireless CR networking. Three critical categories are associated with spectrum sensing such as detection of any known sub-bandwidth, estimation of power density and estimation of boundaries of networking (Arjoune & Kaabouch, 2019). Strategy of spectrum sensing is efficient enough in providing details of the environment to CR networks and possibility of spectrum availability. In contrast, Wu et al. (2018) have suggested that a cooperative strategy in spectrum sensing is highly interactive and can easily improve performance by advancing sensing techniques for wireless networking that is mentioned in the below image. Therefore, this specified technique is effective enough to identify the use of spectrum by using history of usage and improved efficiency of networking by improving energy availability in different licensed channels of networking.
Figure 2: Spectrum sensing in cooperative mode (Source: Wu et al. 2018)
However, these existing literatures are not fully compatible in answering the entire problems regarding spectrum sensing and its use in CR networking for providing safe and effective wireless networking connectivity for users. Detailed techniques of spectrum use and disadvantages are not properly mentioned in this literature. Therefore, one open problem is insufficient availability of details on SS that can be beneficial for improving wireless networking by using CR systems. Another important open problem is a lack of technical details related to Spectrum use in CR systems in this literature creates challenges to identify actual techniques.
According to the provided research paper, the approach that is used is an analysis of a dataset that contains a chunk of historical data of networking that are related with spectrum uses and its importance in CR networking. The technology of dataset processing using databases is the main approach using which modelling is done in the provided research paper. The use of a cooperative strategy of SS is effective enough in gathering the useful information of different SUs using which accuracy of the use of spectrum by PUs can be improved (Wang et al. 2018). The use of two-step Markov chain (in figure 3) as an advanced analysis strategy of such dataset is used in here for achieving an effective outcome. Another important method that is used in this study is energy detectors using which database analysis becomes effective in analysis of research question. Accuracy of spectrum sensing is essential for which proposal for cooperative sensing is required for maintaining spatial diversity (Guo et al. 2017). The analysis of the dataset using MATLAB is another important aspect using which perfection is easily maintained throughout during analysis. The method of energy detection has added simplicity in an analysis of such a large historical dataset.
Figure 3: Markov chain in two-step (Source: Syed & Safdar, 2015)
Advantages that are associated with using such a dataset analysis approach mentioned in the research study are availability of a chunk of historical data and their simplicity. Total scan thresholds that are used here for data analysis are five that further helps in the identification of decision-making strategies for spectrum use by SUs (Syed & Safdar, 2015). Details about false alarms and detection probability of any spectrum hole that can cause security issues for both PUs and SUs are mentioned here as advantages. However, disadvantages are associated with this research study, as it does not include any primary data. This issue causes question marks over the maintenance of an exact authenticity in an analysis of dataset and results. In-depth techniques of analysis of such a large database are absent in this study that has created barriers for improvement of knowledge about accurate analysis methods for such datasets.
Gathering the latest primary data about spectrum sensing and its use in CR networking can be beneficial to maintain authenticity. All the data used here are historic and secondary only that can contain erroneous data. Therefore, use of primary data collection on spectrum sensing using modern techniques will be beneficial to enhance results in future.
Answering the question will guide to achieve concepts about spectrum sensing and its usages in the development of next-generation wireless networking. SS has a crucial part in development of CR networking and the implementation of energy efficiency processes through which loss of spectrum can be reduced effectively. Another aspect that has supported positively for choosing this question is answering various changes that are associated with SS and CR networking. According to, spectrum sensing is a serious challenge to maintain wireless networking and security using CR systems (Fu et al. 2018).
The provided research has helped in gaining basic knowledge by providing accurate details on spectrum sensing and CR networking concepts that is a strength. The presence of a vast historical data concept in spectrum sensing and CR systems will be effective to apply knowledge in future research. This knowledge will make fit for arranging further research especially based on the topic of CR networking and spectrum sensing.
6. Reference List
Arjoune, Y., & Kaabouch, N. (2019) ‘A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions’. Sensors. 19 (1) pp.126. https://www.mdpi.com/1424-8220/19/1/126/pdf
Bindhu, V. (2020) ‘Constraints mitigation in cognitive radio networks using cloud computing’. Journal of trends in Computer Science and Smart technology (TCSST). 2 (01) pp.1-14. https://www.irojournals.com/tcsst/V2/I1/01.pdf
Fu, Y., Yang, F., & He, Z. (2018) ‘A quantization-based multibit data fusion scheme for cooperative spectrum sensing in cognitive radio networks’. Sensors. 18 (2) pp.473. https://www.mdpi.com/1424-8220/18/2/473/pdf
Guo, H., Jiang, W., & Luo, W. (2017) ‘Linear soft combination for cooperative spectrum sensing in cognitive radio networks’. IEEE Communications Letters. 21 (7) pp1573-1576. https://ieeexplore.ieee.org/abstract/document/7885004/
Li, Z., Liu, B., Si, J., & Zhou, F. (2017) ‘Optimal spectrum sensing interval in energy-harvesting cognitive radio networks’. IEEE Transactions on Cognitive Communications and Networking. 3 (2) pp.190-200. https://ieeexplore.ieee.org/abstract/document/7921623/
Ozger, M., Cetinkaya, O., & Akan, O. B. (2018) ‘Energy harvesting cognitive radio networking for IoT-enabled smart grid’. Mobile Networks and Applications. 23 (4) pp.956-966. https://www.repository.cam.ac.uk/bitstream/handle/1810/271764/Energy%20Harvesting%20Cognitive%20Radio%20Networking%20for%20IoT-enabled%20Smart%20Grid.pdf?sequence=1
Ren, J., Hu, J., Zhang, D., Guo, H., Zhang, Y., & Shen, X. (2018) ‘RF energy harvesting and transfer in cognitive radio sensor networks: Opportunities and challenges’. IEEE Communications Magazine. 56 (1) pp.104-110. https://ieeexplore.ieee.org/abstract/document/8255747/
Saleem, Y., Yau, K. L. A., Mohamad, H., Ramli, N., Rehmani, M. H., & Ni, Q. (2017) ‘Clustering and reinforcement-learning-based routing for cognitive radio networks’. IEEE Wireless Communications. 24 (4) pp.146-151. https://www.researchgate.net/profile/Kok-Lim-Yau-2/publication/319240075_Clustering_and_Reinforcement-Learning-Based_Routing_for_Cognitive_Radio_Networks/links/5fa72977a6fdcc06241fcac7/Clustering-and-Reinforcement-Learning-Based-Routing-for-Cognitive-Radio-Networks.pdf
Syed, T. S., & Safdar, G. A. (2015) ‘On the usage of history for energy efficient spectrum sensing’. IEEE Communications Letters. 19 (3) pp.407-410. https://ieeexplore.ieee.org/abstract/document/7004859/
Wang, J., Chen, R., Tsai, J. J., & Wang, D. C. (2018) ‘Trust-based mechanism design for cooperative spectrum sensing in cognitive radio networks’. Computer Communications. 116 pp.90-100. https://www.sciencedirect.com/science/article/am/pii/S0140366417307144
Wu, J., Song, T., Yu, Y., Wang, C., & Hu, J. (2018) ‘Generalized byzantine attack and defense in cooperative spectrum sensing for cognitive radio networks’. IEEE Access. 6 pp.53272-53286. https://ieeexplore.ieee.org/iel7/6287639/6514899/08444625.pdf
Digital and manual both types of storages are available in systems for which management of Cyber Security (CS) technologies are important. Probe request (PR) is a type of PRFA based online attack that works on flaws of technological designing of wireless networking systems. PR targets those flaws and uses a neural network (NN) to create a cyber-attack for gathering details of Wi-Fi. WLAN is a local wireless networking facility that contains MAC addresses using which attackers inject their spoofing tools. The MAC address has very low security and is almost open to all due to improper encryption system availability. IEEE 802.11 is the system used for wireless networking of which safety management is necessary to remove attackers. PR flood (PRF) provides an advantage to attackers to spoof silently and remotely the access point (AP) by creating a fake AP. The importance of open questions in research and possible approaches that will fit for better results will be mentioned here.
- What are the intelligent as well as realistic methods for recognition of rough probe request frames in WLAN?
PR is a sort of cyber-attack that utilises MAC addresses for maintaining association with networks to infringe into system. The use of 802.11 capabilities is essential to incorporate a PR attack in any unsecured Wi-Fi. The presence of a high false positive, as well as high negative rates, creates issues with PR that initiates the chance of cyber-attacks using PR (Acer et al. 2017). An interesting angle for selecting this question is its realism in an analysis of PR issues associated with CS. Master Plan for CS analysis and determination of PR frames can be possible only by answering this typical research question. PRF types of infringement can hamper the smoothness of performance by degrading resources of access network bandwidth (Ratnayake et al. 2011). The presence of different frames such as control frames, frames of data and management frames are associated with MAC-based PR attacks. Attackers select advanced code-based technologies such as DoS, SQL incorporation, injection of remote malware and some other technical engineering systems to damage AP and MAC addresses.
Wi-Fi generally sends PR for accurate scanning of existing networks and AP (Fenske et al. 2021). Use of MAC ID for regulating the interface of networks and maintaining networking communication is essential without which connection cannot be fixed. Implementation of intelligent technologies that can easily interact with Wi-Fi to detect WLAN issues is helpful for the reduction of PR threats. Layers of MAC consist of an 802.11 system of protocol using which clear-cut connection in Wi-Fi can be possible. According to Orlando (2021), the management of cyber issues includes a strategy-based measuring system to reduce its influence. The below image specifies cyber-attacks and their vulnerability in wireless systems.
Figure 4: Cyber risks
(Source: Orlando, 2021)
The problem of the research is that unsecured wireless networks cause serious defilement of information security and bandwidth to spoil network functionalities.
PR works are based on the response of requests after triggering authentication processes in different wireless networks. The presence of AP indirectly helps PR to react in a network in the form of an AP beacon. Intelligent systems usage in WLAN is beneficial to detect AP networks, GUI, use of only a single AP and detect all external attacks using coding systems. PR in any Wi-Fi is used for detecting the information about occupancy in indoor areas (Huang et al. 2019). A hybrid strategy of machine learning that is HVAC is used for detecting the occupancy of any Wi-Fi connection using automation that can help to easily remove external usage as mentioned in the below image.
Figure 5: HVAC Wi-Fi usage detection
(Source: Huang et al. 2019)
In contrast, Belghazi et al. (2019) have said that the presence of 802.11 protocol is a helping hand for detecting u necessary PR and possible responses. Therefore, knowledge can be gathered from this fact that occupancy of indoor Wi-Fi is important along with usage of 802.11 protocol to separate authentic connections that are generally a PR attack from Wi-Fi. The presence of a probe in a threading device can completely disclose the master key of a network using which attackers violate confidentiality (Liu et al. 2018).
The development of effective software is essential to improve the techniques of CS. According to Chung (2020), CS is not a technology that is one time used for protection as attackers are improving their attack types continuously. The use of CS provides sufficient advantage by educating employees about CS, keeping trust, and helping networks to remain ahead from attackers. In contrast, Huang et al. (2018) have strongly supported the building of a power grid for resisting the CS risks in any wireless connectivity. Therefore, security is mandatory to safeguard networks from cyber-attacks and spoofing of AP. The below image precisely indicates building blocks of resist chain from CS.
Figure 6: Grid for the safety of cyber risk
(Source: Huang et al. 2018)
The literature that is picked here contains details about PR and CS however, they are not strong in an analysis of functionalities of PR. Even they lacks in analysis of risk factors correlated with CS in Wi-Fi networks. An open problem that can be generated here is an issue with a discussion about CS and its benefits. Another open problem is the absence of risk reduction processes of PR and the issue of cyber-attacks.
Analysis of the respective sample frame, which is a primary approach, has been adopted for the distinguishing the PR based hazards in Wi-Fi networks. A simulation-based result analysis using the 1000 samples in the frames are selected for maintaining authenticity (Ratnayake et al. 2011). Total two types of frames are considered here for data analysis such as user frame as value 1 (genuine) and attacker frame of value 0 (not genuine). The approach is used for spotting the ill-configured system of WLAN to accurately identify the attacks and strength of wireless signals. A trained NN is utilised to maintain authenticity in the data handling process. WIDS technique is accepted here for analysis of such primary data of 1000 samples. Sequencing of those frames has helped in determining infected stems accurately using STA. Sequences are maintained based on the MAC frame and different attributes of SSI for different independent variables in the analysis. The testing strategy includes requests that are sent to AP by utilising the facility of SSID in PR factors. Clients of WLAN use different PR frames for scanning WLAN networks in any specific area (Li et al. 2020). The below image suggests sensing strategy of Wi-Fi in a networking area.
Figure 7: Wi-Fi sensing
(Source: Li et al. 2020)
Advantages of authenticity and uniqueness in results are the main part for selecting primary frame trajectory in analysis. The facility of the use of MAC address analysis has provided an advantage in determining traffic of a WLAN system. The advantages of gathering concepts of PR issues and strategic technology associated with PR have strongly improved the detection process. The employment of MATLAB and its analysis process has provided advantage in maintaining exactness in data arrangement. Idea of MAC spoofing and AP fraud are other two essential outcomes from this approach. Disadvantages of availability of enormous information due to lack of secondary resources is a serious issue. Detailed analysis and implementation of NN creates a disadvantage in collecting knowledge on the essentiality of NN. The detailed concept of threshold value analysis for network detection is not properly presented using this approach that is another disadvantage.
A secondary approach using secondary data along with primary framing can support a collection of exact results. The availability of safeguard in duplicate information along with cost-efficient planning can support the research. The longitudinal style of analysis is another associated advantage that can improve data generalisation using a secondary framework. Exact data gathering on CS can be beneficial for using a secondary framework.
Gaining knowledge in PR and the issue of cyber-attacks is beneficial for future research processes as it can help to improve security management strategies. Question on this is highly accurate for identification of intelligent strategies for maintaining CS. Analysis of PR frames is an important outcome of this question through which frame analysis techniques can be learnt. PR frames are not a complete part of communication, they generally control frames (Chilipirea et al. 2018). The concept of SSID, WLAN, AP, and the presence of flaws can easily be determined after critical analysis of the question. Distinct inputs of NN in PR risks can be identified easily from the question.
Concepts of PR, CS, WLAN, SSID, AP, and PR frames will provide strength in building the concept of network security. A proper education that can be gained from this paper will be fit accurately to apply basic concepts in future research.
Acer, M. E., Stark, E., Felt, A. P., Fahl, S., Bhargava, R., Dev, B., … & Tabriz, P. (2017, October) ‘Where the wild warnings are: Root causes of Chrome HTTPS certificate errors’. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. (pp. 1407-1420). https://dl.acm.org/doi/pdf/10.1145/3133956.3134007
Belghazi, Z., Benamar, N., Addaim, A., & Kerrache, C. A. (2019) ‘Secure WiFi-direct using key exchange for Iot device-to-device communications in a smart environment’. Future Internet. 11 (12) pp.251. https://www.mdpi.com/1999-5903/11/12/251/pdf
Chilipirea, C., Baratchi, M., Dobre, C., & Steen, M. V. (2018) ‘Identifying stops and moves in WiFi tracking data’. Sensors. 18 (11) pp.4039. https://www.mdpi.com/1424-8220/18/11/4039/pdf
Chung, M. (2020) ‘Signs your cyber security is doomed to fail’. Computer Fraud & Security. 2020 (3) pp.10-13. https://www.magonlinelibrary.com/doi/full/10.1016/S1361-3723%2820%2930029-4
Fenske, E., Brown, D., Martin, J., Mayberry, T., Ryan, P., & Rye, E. (2021) ‘Three years later: A study of mac address randomization in mobile devices and when it succeeds’. Proceedings on Privacy Enhancing Technologies. 2021 (3) pp.164-181. https://sciendo.com/pdf/10.2478/popets-2021-0042
Huang, Q., Rodriguez, K., Whetstone, N., & Habel, S. (2019) ‘Rapid Internet of Things (IoT) prototype for accurate people counting towards energy efficient buildings’. J. Inf. Technol. Constr. 24 pp.1-13. https://itcon.org/papers/2019_01-ITcon-Huang.pdf
Huang, X., Qin, Z., & Liu, H. (2018) ‘A survey on power grid cyber security: From component-wise vulnerability assessment to system-wide impact analysis’. IEEE Access. 6 pp.69023-69035. https://ieeexplore.ieee.org/iel7/6287639/6514899/08528321.pdf
Li, W., Piechocki, R. J., Woodbridge, K., Tang, C., & Chetty, K. (2020) ‘Passive WiFi radar for human sensing using a stand-alone access point’. IEEE Transactions on Geoscience and Remote Sensing. 59 (3) pp.1986-1998. https://discovery.ucl.ac.uk/id/eprint/10103371/1/Passive%20WiFi%20Radar%20for%20Human%20Sensing%20Using%20A%20Stand-Alone%20Access%20Point.pdf
Liu, Y., Pang, Z., Dán, G., Lan, D., & Gong, S. (2018) ‘A taxonomy for the security assessment of IP-based building automation systems: The case of thread’. IEEE Transactions on Industrial Informatics. 14 (9) pp.4113-4123. https://www.diva-portal.org/smash/get/diva2:1217800/FULLTEXT01.pdf
Orlando, A. (2021) ‘Cyber Risk Quantification: Investigating the Role of Cyber Value at Risk’. Risks. 9 (10) pp.184. https://www.mdpi.com/2227-9091/9/10/184/pdf
Ratnayake, D. N., Kazemian, H. B., Yusuf, S. A., & Abdullah, A. B. (2011) ‘An intelligent approach to detect probe request attacks in IEEE 802.11 networks’. In Engineering Applications of Neural Networks. (pp. 372-381). Springer, Berlin, Heidelberg. https://link.springer.com/content/pdf/10.1007/978-3-642-23957-1_42.pdf