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7COM1084 Research Portfolio

Assignment Brief:

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.

Solution:

ADVANCED RESEARCH TOPICS IN COMPUTER SCIENCE

PART 1- COMPUTER NETWORKS AND SYSTEM SECURITY
PART 2- CYBER SECURITY

Part 1:

1. Introduction

Networking in computer science is an important part that helps in maintaining a technical connection between computers. Spectrum sensing (SS) and cognitive radio networks (CRN) are two important components that are associated with wireless technology to maintain effective networking between systems. Specialism of this research is exact detection of functionalities of CRN using SS strategy for maintaining network security and connectivity. SS is helpful to provide proper safety for primary users (PUs) and secondary users (SUs) during their usage of CRN. SS helps in exact detection of holes in the spectrum using different advanced techniques such as probability in detecting holes (Pd) and false alarm (Pfa). Another important strategy is probability of missed detection (Pmd) that can be helpful to detect techniques of SS and CRN. This study will guide to answer a research question after analysis of existing works and will provide possible approach to express strength for research conduction.

2. Open research question

  • What are the intelligent and different energy-efficient mechanisms of spectrum sensing in the improvement of efficiency in cognitive radio networks?

SS is a function of CRN that helps in prevention of harmful security risks that are present in wireless technology by maximising the use of spectrum. This strategy can be eventful for both PUs and SUs as it allows the use of spectrum facilities for both of them. The question is interesting as it is susceptible to answer the detailed concept of SS and CRN by mentioning their use in wireless connectivity. CRN is an effective technology of computer science that starts detection of spectrum issues and guides to solve that issue significantly (Kakalou et al. 2017).

CRN cloud technique (Figure 1) contains an advanced transceiver that has enough intelligence to detect different channels of communication for use in wireless techniques. Generally, two important types of CRN are present such as full CRN and SS-CRN among which SS-CRN utilises different radio frequencies to connect with a wireless network. A continuous scan of different spectrums can be effective in capturing opportunities for SUs to use the spectrum accurately along with PUs (Syed & Safdar, 2015). This research question is accurate in answering risk factors of SS and CRN using the concept of Markov chain. Holes in the spectrum cause spectrum misuse in networking that initiates the chance of security issues for users. According to Lee et al. (2019), a cooperative strategy of SS (CSS) (Figure 2) is an advanced method using which presence of PUs can be detected by analysing outcomes from SUs.

SS supports a periodic monitoring strategy of a specific band of frequency using which advancement in wireless processes can be done. Therefore, analysis of this question will suggest detecting spectrum consumption for the elimination of security issues.

Research problem

Improper techniques of SS and CRN can cause security and connectivity issues in networking that can hamper usage of wireless technology for users.

3. Existing work

CRN is a modern platform of wireless connectivity that enhances efficiency in connectivity. CRN allows various unlicensed SUs to use networks in an ad-hoc manner. According to Lee (2018), CRN is a strategy that helps SUs to use the spectrum of PUs providing that interference be less compared to predefined limits for PUs. CRN uses a high spectrum to improve the connectivity in wireless technology based on a deep neural network (DNN). In contrast, Gao et al. (2018) have said that spectrums that are energy-harvesting aids can be beneficial to improve the efficiency of different CRN. Therefore, it is observable from such above discussion that CRN is important along with SS to maintain proper energy consumption in wireless connections. CRN is a strategy that allows unauthorised users to communicate over any licensed region using the spectrum (Zhang et al. 2018). The below image indicates the DNN model for SUs for using spectrum in CRN that is not used by the PUs.

SS is beneficial to maintain a connection between systems in wireless processes and is supportive for improvement of CRN. SS is a vital aspect that helps in detecting procedures for identification of activities of PUs (He & Jiang, 2019).

The above image is accurate in the identification of CRN strategy using cooperative sensing for determination of activities of PUs. In contrast, Rajaguru et al. (2020) have opined that accuracy in sensing is important without which the rate of misdetection can increase significantly. Therefore, it positively can be determined that techniques of SS are crucial in maintaining detection of spectrum use and its loss.

The compatibility of these literatures is not strong enough as they are lacking of information. These literatures have not suggested an analysis of SS security issues for which they are not fully compatible in guiding the answer of the research question. Further, availability of details in the use of SS in CRN is not completely mentioned to achieve ideas of CRN utilisation by PUs and SUs. Therefore, the first open problem that can be considered is unavailability of information related to SS and CRN for the advancement of wireless connection with security. Another open problem is lack of presence of a solution for overcoming spectrum holes to increase availability of spectrum for CRN.

4. Research approach

Availability of a large historical dataset and analysis of those secondary data are the approaches mentioned in this provided paper. Approaches that are associated with SS are mainly two types such as nyquist-rate approach and sub-nyquist approach (Hamdaoui et al. 2018). Applying these approaches, determination of functionalities for CRN can be achieved easily in any network. This provided secondary approach has helped to determine historic data through which the actual trend of use of SS in CRN has been achieved. Data analysis is done by maintaining a threshold value that has been detected within a range starting from 1 to a maximum of 5 (Syed & Safdar, 2015). Analysis has helped in the determination of energy consumption strategy by CRN and effective sensing techniques for the reduction of energy consumption. The below mentioned image indicates the analysis strategy of SS for SUs by using a three-state model of the Markov chain.

CSS is an important strategy using which determination of sensing information by SUs over any licensed channel can easily be measured (Jin et al. 2019). This secondary model system has helped to analyse those user data effectively to guide for applying concepts in future improvement of network security.

The main advantage of using this approach is an analysis of vast data that has supported strongly to determine key issues of CRN and SS. Another advantage associated with this approach is availability of options to utilise the concept of energy consumption, especially in SS technique. A technical concept about false alarm and spectrum detection are the important outcomes from this approach. However, disadvantages include a lack of support from primary data that creates issues in managing authenticity of results. Lack of proper availability of strategies to detect risk factors in spectrum usage is another disadvantage in this approach. Another disadvantage indicates the consistency of data that is not maintained properly during an analysis of historic data.

An alternative strategy that can be accurate is the use of primary data that can provide accuracy in achieving results. Dataset consisting of historic data can contain erroneous information through which reduces the efficacy of results. Primary data can easily support gathering practical results and can enhance analysis by improving accuracy. Further, primary data can help to gather specific information during research through which uniqueness of results can be maintained properly.

5. Personal investment

The advantage of using SS in CRN can be answered positively with the help of this specific question. SS is a technology using which advanced wireless networking and management of security can be possible. Requirement of an energy-efficient approach in CRN can easily be answered with the help of the question. The concept of different models through which analysis of SS and CRN is possible is mentioned in this paper. Algorithms of SS can be determined as parametric and different non-parametric schemes (Soni et al. 2020). Therefore, questions regarding the use of algorithms in CRN and SS can be answered with the help of this specific unique question.

The strength that has been provided by this paper is knowledge about CRN and SS that can be applied for future research. Further, this research will fit perfectly for the conduction of future research as it contains information about models using which analysis of SS can be possible.

6. Reference List

Journals

Gao, Y., He, H., Deng, Z., & Zhang, X. (2018) ‘Cognitive radio network with energy-harvesting based on primary and secondary user signals’. IEEE Access. 6 pp.9081-9090. https://ieeexplore.ieee.org/iel7/6287639/6514899/08268049.pdf

Hamdaoui, B., Khalfi, B., & Guizani, M. (2018) ‘Compressed wideband spectrum sensing: Concept, challenges, and enablers’. IEEE Communications Magazine. 56 (4) pp.136-141. https://arxiv.org/pdf/1805.03822

He, H., & Jiang, H. (2019) ‘Deep learning based energy efficiency optimization for distributed cooperative spectrum sensing’. IEEE Wireless Communications. 26 (3) pp.32-39. https://par.nsf.gov/servlets/purl/10160605

Jin, Z., Yao, K., Lee, B., Cho, J., & Zhang, L. (2019) ‘Channel status learning for cooperative spectrum sensing in energy-restricted cognitive radio networks’. IEEE Access. 7 pp.64946-64954. https://ieeexplore.ieee.org/iel7/6287639/6514899/08712490.pdf

Kakalou, I., Psannis, K. E., Krawiec, P., & Badea, R. (2017) ‘Cognitive radio network and network service chaining toward 5G: Challenges and requirements’. IEEE communications Magazine. 55 (11) pp.145-151. https://ruomo.lib.uom.gr/bitstream/7000/490/1/IoannaKakalouIEEECommunicatiosnMagazineRUOMO.pdf

Lee, W. (2018) ‘Resource allocation for multi-channel underlay cognitive radio network based on deep neural network’. IEEE Communications Letters. 22 (9) pp.1942-1945. https://ieeexplore.ieee.org/abstract/document/8419776/

Lee, W., Kim, M., & Cho, D. H. (2019) ‘Deep cooperative sensing: Cooperative spectrum sensing based on convolutional neural networks’. IEEE Transactions on Vehicular Technology. 68 (3) pp.3005-3009. https://ieeexplore.ieee.org/abstract/document/8604101/

Rajaguru, R., Devi, K. V., & Marichamy, P. (2020) ‘A hybrid spectrum sensing approach to select suitable spectrum band for cognitive users’. Computer Networks. 180 pp.107387. https://www.sciencedirect.com/science/article/pii/S1389128620304850

Soni, B., Patel, D. K., & Lopez-Benitez, M. (2020) ‘Long short-term memory based spectrum sensing scheme for cognitive radio using primary activity statistics’. IEEE Access. 8 pp.97437-97451. https://ieeexplore.ieee.org/iel7/6287639/8948470/09096319.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/

Zhang, X., Zhang, X., Han, L., & Xing, R. (2018) ‘Utilization-oriented spectrum allocation in an underlay cognitive radio network. IEEE Access. 6 pp.12905-12912. https://ieeexplore.ieee.org/iel7/6287639/6514899/08300515.pdf

Part 2:

1. Introduction

Cyber security (CS) is a serious challenge in modern day as lack of security in systems can hamper networking system. CS can be considered as implementation of safety technologies to make systems and networks safe from unwanted hazards. A probe request (PR) is the combination of 802.11 frames of management that is sent by any client to avail the information of SSIDs. WLAN is advanced wireless connectivity between systems that are highly susceptible to attack by probe request. Visibility of user information and connectivity details are the main factors of cyber-attacks in WLAN. MAC spoofing and issue of PR are two common cyber securities that can spoil the reputation of any secured network. This study will consist of a detailed analysis of cyber risks that are associated with PR along with support from other exacting works. Further, details of approaches that can be used alternatively for better analysis of data will be mentioned here.

2. Open research question

  • What are the intelligent as well as realistic methods for recognition of rough probe request frames in WLAN?

Maintaining CS and blocking any PR are two essential parts for maintaining unhampered system functionalities especially for wireless networking. Specialism associated with this question is its capability in detecting cyber risk and providing suggestions for stopping those issues. Maintaining 802.11-access point (AP) in PR attack is crucial as it causes injection of malware to violate other authentic AP for connectivity (Agarwal et al. 2018) (Figure 6).

Attackers try to access all information that is associated with a network before attack. The next step that an attacker tries to do is spoofing the existing MAC address using which information can easily be gettable (Ratnayake et al. 2011). Therefore, protecting that MAC address is essential for securing the network and system from cyber-attacks. This authentic question creates interest due to its ability in answering techniques of PR attacks in any networking system. Detecting the piracy of software and threats of malware are two important issues associated with cyber-attacks (Ullah et al. 2019). The image below indicates a complete architecture based threat of cyber security for IoT systems that are vulnerable to Cyber risk.

The speciality of this question is that, it can provide information regarding adaptation of the security process for identification of cyber-attacks using PR. STA is another important part of determining cyber issues. However, it can miss frames that are effective for maintaining service quality in any artificial neural network (ANN). Therefore, this question can answer this frame analysis technique effectively. Using such ANN technique, selection of proper security system to detect and reduce PR and CS issues can be possible.

Research problem

Problem associated with this research is the lack of security management to eliminate PR and CS issues causing system network damage.

3. Existing work

Cyber securities can be differentiated into three types such as cloud security, security of network and application security. Cloud storage is an important option to make information available anywhere. Therefore, maintaining such cloud security and security in the network are vital for the safety of information. Wifiphising is a type of cyber risk that uses PR as a step by an attacker mentioned ion figure 8. Wifiphising consists of two critical steps that are the spoof attack in MAC address to create fake AP and force from attacker to use that fake AP (Sethuraman et al. 2019).

Attackers use the DoS system to reverse the coding of a network before attacking the wireless network. In contrast, Stute et al. (2019) have argued that randomisation of MAC addresses of a network can help to prevent PR attacks in Wi-Fi. Therefore, it is considered from this fact that randomisation-securing strategy of MAC addresses is eventful to reduce the influence of PR and CS problems in a network and system. The presence of intrusion detection technologies in wireless systems can be helpful to detect PR attacks at initial stage.

PR attacks are PRFA generated attacks that mainly try to identify a lack of security in WLAN using which they attack a wireless network. The presence of vulnerabilities in different digital systems such as in IoT and Wi-Fi are the root cause of CS issues and PR attacks (Tweneboah-Koduah et al. 2017). CS consists of four domains such as physical domain, a domain that contains information, cognitive domain and social domain. All of these domains need to be secured with safety software to eliminate PR attacks. In contrast, Reyes-Moncayo et al. (2020) have said that frames in PR contain different information about safety in various wireless stations. Therefore, it is required to maintain safety in WLAN to reject any unauthentic request such as PR to reduce security issues. The below image indicates the detailed taxonomy of Wi-Fi functioning for which security magnet is important.

All of these literatures are completely effective in answering issues of CS and PR attacks due to lack of information on techniques of those systems. Further, solutions that can permanently solve security issues in networks are absent. Therefore, the first open problem is due to a lack of information regarding cyber-attacks and PR attacks. Another open problem is the improper presence of solutions in these literatures to super reduction strategy of PR issue. 

4. Research approach

Analysis of sample frames of primary data is the chosen approach for analysing data regaled with PR and CS. WIDS variable selection strategy is used here for an analysis of such frames to gather authentic data. Based on this particular paper, it has been observed that target output for the user is considered as 1 that is termed as genuine whereas attacker is considered as 0 that is non-genuine (Ratnayake et al. 2011). Different scenarios for the test are taken here for gathering results from users and detecting issues of attack. The outcome strongly indicates that a trained NN has capabilities in attack detection through which malicious attacks can be determined. MATLAB is used here for designing the attack technique of PR and possible outcomes from that. PR attacks can easily be determined using different approaches to machine learning using advanced software (Vijayakumar & Ganapathy, 2018). The use of the 802.11 system of WLAN to trace the cyber issues and PR strategies are important here for maintaining authenticity of results.

The use of a frame analysis tactics has provided an advantage in identifying attackers in a system positively. Another advantage is the use of MATLAB and its functionality to analyse primary data in research. The importance of security up gradation in MAC id and WLAN system to eliminate PR and CS issues are mentioned here. The use of neural networks for the detection of PR attacks has added advantages for using such frame analysis techniques. PR injection includes the prevention of a client obtaining a genuine IP address by spoofing a malicious probe reply (Tripathi & Hubballi, 2021). This approach has helped in determining such spoofed replies efficiently using which the intensity of cyber-attacks are measured. Disadvantages include a lack of management of frames to maintain a continuous analysis of attacks. Further, the unavailability of secondary sources creates limitations in achieving historic data regarding PR attacks and CS.

An alternative approach that can be utilised in the research is gathering historic secondary data along with frame analysis primary technique to compare results. Further, secondary sources will enable gathering data of a wide range to properly analyse the issue of CS and PR. Authenticity can be maintained throughout with the use of both of the data. Secondary research will further support achieving the target by maintaining accuracy in the analysis as it will support the availability of large amounts of authentic data.

5. Personal investment

Research question is selected for answering the actual cause of CS and PR along with solutions to reduce the influence. Flooding attack in PR indicates flooding of requests in AP that forces a user to respond to them (Satam & Hariri, 2020). That question is supportive to answer the reason for PR flood and issue with them. Drawbacks in the safety of the WLAN system can be answered effectively using that question. Different challenges of CS and PR attacks can easily be identified after a successful answer to the research question. Solutions such as the use of encryption to protect networks from PR can be answered easily.

Availability of exact information regarding CS and PR will enhance the strength of knowledge to implement during research. Further, that knowledge will be fit exactly to do future research on specific topics of CS and PR. Availability of practical analysis techniques will support to use that frame techniques in future research.

6. Reference List

Journals

Agarwal, M., Biswas, S., & Nandi, S. (2018) ‘An efficient scheme to detect evil twin rogue access point attack in 802.11 Wi-Fi networks’. International Journal of Wireless Information Networks. 25 (2) pp.130-145. https://www.researchgate.net/profile/Mayank-Agarwal-27/publication/324215846_An_Efficient_Scheme_to_Detect_Evil_Twin_Rogue_Access_Point_Attack_in_80211_Wi-Fi_Networks/links/5ad347f9aca272fdaf7a458c/An-Efficient-Scheme-to-Detect-Evil-Twin-Rogue-Access-Point-Attack-in-80211-Wi-Fi-Networks.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

Reyes-Moncayo, H. I., Malaver-Mendoza, L. D., & Ochoa-Murillo, A. L. (2020) ‘Survey of the security risks of Wi-Fi networks based on the information elements of beacon and probe response frames’. Scientia et Technica. 25 (3) pp.351-357. https://ojs2.utp.edu.co/index.php/revistaciencia/article/download/23781/16414

Satam, P., & Hariri, S. (2020) ‘WIDS: An anomaly based intrusion detection system for Wi-Fi (IEEE 802.11) protocol’. IEEE Transactions on Network and Service Management. 18 (1) pp.1077-1091. https://ieeexplore.ieee.org/abstract/document/9249426/

Sethuraman, S. C., Dhamodaran, S., & Vijayakumar, V. (2019) ‘Intrusion detection system for detecting wireless attacks in IEEE 802.11 networks’. IET networks. 8 (4) pp.219-232. https://scholar.google.com/scholar?output=instlink&q=info:PBarS7KHem0J:scholar.google.com/&hl=en&as_sdt=0,5&as_ylo=2017&scillfp=9388642765588242302&oi=lle

Stute, M., Narain, S., Mariotto, A., Heinrich, A., Kreitschmann, D., Noubir, G., & Hollick, M. (2019) ‘A billion open interfaces for eve and mallory: Mitm, dos, and tracking attacks on ios and macos through apple wireless direct link’. In 28th {USENIX} Security Symposium ({USENIX} Security 19). (pp. 37-54). https://www.usenix.org/system/files/sec19fall_stute_prepub.pdf

Tripathi, N., & Hubballi, N. (2021) ‘Application Layer Denial-of-Service Attacks and Defense Mechanisms: A Survey’. ACM Computing Surveys (CSUR). 54 (4) pp.1-33. https://www.researchgate.net/profile/Nikhil-Tripathi-2/publication/351302051_Application_Layer_Denial-of-Service_Attacks_and_Defense_Mechanisms_A_Survey/links/60a6006c92851c43da0321a8/Application-Layer-Denial-of-Service-Attacks-and-Defense-Mechanisms-A-Survey.pdf

Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017) ‘Cyber security threats to IoT applications and service domains’. Wireless Personal Communications. 95 (1) pp.169-185. https://www.researchgate.net/profile/Samuel-Tweneboah-Koduah/publication/317283254_Cyber_Security_Threats_to_IoT_Applications_and_Service_Domains/links/5ab50b510f7e9b68ef4be69c/Cyber-Security-Threats-to-IoT-Applications-and-Service-Domains.pdf

Ullah, F., Naeem, H., Jabbar, S., Khalid, S., Latif, M. A., Al-Turjman, F., & Mostarda, L. (2019) ‘Cyber security threats detection in internet of things using deep learning approach’. IEEE Access. 7 pp.124379-124389. https://ieeexplore.ieee.org/iel7/6287639/8600701/08812669.pdf

Vijayakumar, D. S., & Ganapathy, S. (2018) ‘Machine Learning Approach to Combat False Alarms in Wireless Intrusion Detection System’. Comput. Inf. Sci.. 11 (3) pp.67-81. https://pdfs.semanticscholar.org/869d/e29d1b0180bb71ad3c0ac2fd9acc47c00583.pdf

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