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  • Published: 13 September 2024

From the lab to a career in defence research

  • Teresa Schubert 1  

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Nature Reviews Psychology is interviewing individuals with doctoral degrees in psychology who pursued non-academic careers. We spoke with Mike Tombu about his journey from a postdoctoral fellow to a defence scientist.

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Schubert, T. From the lab to a career in defence research. Nat Rev Psychol (2024). https://doi.org/10.1038/s44159-024-00366-1

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How to prepare an excellent thesis defense

Thesis defence

What is a thesis defense?

How long is a thesis defense, what happens at a thesis defense, your presentation, questions from the committee, 6 tips to help you prepare for your thesis defense, 1. anticipate questions and prepare for them, 2. dress for success, 3. ask for help, as needed, 4. have a backup plan, 5. prepare for the possibility that you might not know an answer, 6. de-stress before, during, and after, frequently asked questions about preparing an excellent thesis defense, related articles.

If you're about to complete, or have ever completed a graduate degree, you have most likely come across the term "thesis defense." In many countries, to finish a graduate degree, you have to write a thesis .

A thesis is a large paper, or multi-chapter work, based on a topic relating to your field of study.

Once you hand in your thesis, you will be assigned a date to defend your work. Your thesis defense meeting usually consists of you and a committee of two or more professors working in your program. It may also include other people, like professionals from other colleges or those who are working in your field.

During your thesis defense, you will be asked questions about your work. The main purpose of your thesis defense is for the committee to make sure that you actually understand your field and focus area.

The questions are usually open-ended and require the student to think critically about their work. By the time of your thesis defense, your paper has already been evaluated. The questions asked are not designed so that you actually have to aggressively "defend" your work; often, your thesis defense is more of a formality required so that you can get your degree.

  • Check with your department about requirements and timing.
  • Re-read your thesis.
  • Anticipate questions and prepare for them.
  • Create a back-up plan to deal with technology hiccups.
  • Plan de-stressing activities both before, and after, your defense.

How long your oral thesis defense is depends largely on the institution and requirements of your degree. It is best to consult your department or institution about this. In general, a thesis defense may take only 20 minutes, but it may also take two hours or more. The length also depends on how much time is allocated to the presentation and questioning part.

Tip: Check with your department or institution as soon as possible to determine the approved length for a thesis defense.

First of all, be aware that a thesis defense varies from country to country. This is just a general overview, but a thesis defense can take many different formats. Some are closed, others are public defenses. Some take place with two committee members, some with more examiners.

The same goes for the length of your thesis defense, as mentioned above. The most important first step for you is to clarify with your department what the structure of your thesis defense will look like. In general, your thesis defense will include:

  • your presentation of around 20-30 minutes
  • questions from the committee
  • questions from the audience (if the defense is public and the department allows it)

You might have to give a presentation, often with Powerpoint, Google slides, or Keynote slides. Make sure to prepare an appropriate amount of slides. A general rule is to use about 10 slides for a 20-minute presentation.

But that also depends on your specific topic and the way you present. The good news is that there will be plenty of time ahead of your thesis defense to prepare your slides and practice your presentation alone and in front of friends or family.

Tip: Practice delivering your thesis presentation in front of family, friends, or colleagues.

You can prepare your slides by using information from your thesis' first chapter (the overview of your thesis) as a framework or outline. Substantive information in your thesis should correspond with your slides.

Make sure your slides are of good quality— both in terms of the integrity of the information and the appearance. If you need more help with how to prepare your presentation slides, both the ASQ Higher Education Brief and James Hayton have good guidelines on the topic.

The committee will ask questions about your work after you finish your presentation. The questions will most likely be about the core content of your thesis, such as what you learned from the study you conducted. They may also ask you to summarize certain findings and to discuss how your work will contribute to the existing body of knowledge.

Tip: Read your entire thesis in preparation of the questions, so you have a refreshed perspective on your work.

While you are preparing, you can create a list of possible questions and try to answer them. You can foresee many of the questions you will get by simply spending some time rereading your thesis.

Here are a few tips on how to prepare for your thesis defense:

You can absolutely prepare for most of the questions you will be asked. Read through your thesis and while you're reading it, create a list of possible questions. In addition, since you will know who will be on the committee, look at the academic expertise of the committee members. In what areas would they most likely be focused?

If possible, sit at other thesis defenses with these committee members to get a feel for how they ask and what they ask. As a graduate student, you should generally be adept at anticipating test questions, so use this advantage to gather as much information as possible before your thesis defense meeting.

Your thesis defense is a formal event, often the entire department or university is invited to participate. It signals a critical rite of passage for graduate students and faculty who have supported them throughout a long and challenging process.

While most universities don't have specific rules on how to dress for that event, do regard it with dignity and respect. This one might be a no-brainer, but know that you should dress as if you were on a job interview or delivering a paper at a conference.

It might help you deal with your stress before your thesis defense to entrust someone with the smaller but important responsibilities of your defense well ahead of schedule. This trusted person could be responsible for:

  • preparing the room of the day of defense
  • setting up equipment for the presentation
  • preparing and distributing handouts

Technology is unpredictable. Life is too. There are no guarantees that your Powerpoint presentation will work at all or look the way it is supposed to on the big screen. We've all been there. Make sure to have a plan B for these situations. Handouts can help when technology fails, and an additional clean shirt can save the day if you have a spill.

One of the scariest aspects of the defense is the possibility of being asked a question you can't answer. While you can prepare for some questions, you can never know exactly what the committee will ask.

There will always be gaps in your knowledge. But your thesis defense is not about being perfect and knowing everything, it's about how you deal with challenging situations. You are not expected to know everything.

James Hayton writes on his blog that examiners will sometimes even ask questions they don't know the answer to, out of curiosity, or because they want to see how you think. While it is ok sometimes to just say "I don't know", he advises to try something like "I don't know, but I would think [...] because of x and y, but you would need to do [...] in order to find out.” This shows that you have the ability to think as an academic.

You will be nervous. But your examiners will expect you to be nervous. Being well prepared can help minimize your stress, but do know that your examiners have seen this many times before and are willing to help, by repeating questions, for example. Dora Farkas at finishyourthesis.com notes that it’s a myth that thesis committees are out to get you.

Two common symptoms of being nervous are talking really fast and nervous laughs. Try to slow yourself down and take a deep breath. Remember what feels like hours to you are just a few seconds in real life.

  • Try meditational breathing right before your defense.
  • Get plenty of exercise and sleep in the weeks prior to your defense.
  • Have your clothes or other items you need ready to go the night before.
  • During your defense, allow yourself to process each question before answering.
  • Go to dinner with friends and family, or to a fun activity like mini-golf, after your defense.

Allow yourself to process each question, respond to it, and stop talking once you have responded. While a smile can often help dissolve a difficult situation, remember that nervous laughs can be irritating for your audience.

We all make mistakes and your thesis defense will not be perfect. However, careful preparation, mindfulness, and confidence can help you feel less stressful both before, and during, your defense.

Finally, consider planning something fun that you can look forward to after your defense.

It is completely normal to be nervous. Being well prepared can help minimize your stress, but do know that your examiners have seen this many times before and are willing to help, by repeating questions for example if needed. Slow yourself down, and take a deep breath.

Your thesis defense is not about being perfect and knowing everything, it's about how you deal with challenging situations. James Hayton writes on his blog that it is ok sometimes to just say "I don't know", but he advises to try something like "I don't know, but I would think [...] because of x and y, you would need to do [...] in order to find out".

Your Powerpoint presentation can get stuck or not look the way it is supposed to do on the big screen. It can happen and your supervisors know it. In general, handouts can always save the day when technology fails.

  • Dress for success.
  • Ask for help setting up.
  • Have a backup plan (in case technology fails you).
  • Deal with your nerves.

defence research papers

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Research on multi-layer defense against ddos attacks in intelligent distribution networks.

defence research papers

1. Introduction

  • Firstly, this paper conducts a DDoS security analysis on the new power distribution communication network based on SDN architecture.
  • Secondly, with the progressive nature of computation in mind, a three-layer defense architecture is designed from shallow to deep, efficiently preventing resource wastage in detecting low-intensity DDoS attacks.
  • Thirdly, this paper employs Rényi entropy for DDoS detection. This step is improved for complex DDoS attacks, and when combined with the random forest classifier, it effectively detects potential DDoS behaviors in the traffic passing through switches.
  • Finally, this paper uses a dual defense method combining BiCNN and OneDCNN to inspect and filter port traffic, ensuring normal communication of valid traffic while eliminating malicious traffic.

2. System Models

2.1. network composition of user-side devices in distribution networks, 3. methodology, 3.1. ddos detection based on rényi entropy, 3.2. random forest classifier, 3.3. convolutional neural network, 4. system design.

  • Without the first layer, BiCNN would need to run continuously, constantly monitoring each switch. Since BiCNN is based on CNN, its computational load and model size are significantly higher compared to the Rényi entropy and random forest combination, which retrieves fewer features. Under low-saturation DDoS attacks, most of the time, there are no abnormal data in the switch, leading to resource wastage.
  • Without the second layer, OneDCNN would need to filter all packets across the entire network upon receiving the first layer’s alert, resulting in an excessive computational load that could crash the system.
  • Without the third layer, the mechanism cannot participate in the controller’s decision-making process, rendering it unable to perform packet filtering.

5. Results and Validation

5.1. experimental topology, 5.2. baselines.

  • φ-entropy [ 10 ]: This method analyzes traffic characteristics using φ-entropy to achieve early attack detection. φ-entropy amplifies the characteristic differences between different types of data by adjusting parameters. As a non-machine learning method, it performs well in early attack detection, particularly suited for detecting anomalous traffic by emphasizing data differences through parameter adjustments.
  • Spark Streaming and Flink [ 27 ]: This method proposes an online internet traffic monitoring and DDoS attack detection approach based on big data frameworks (Spark Streaming and Flink). It is suitable for large-scale internet traffic monitoring and achieves high detection rates through machine learning techniques.
  • 3Dual-CNN [ 26 ]: In the first two layers of the three-layer detection mechanism proposed in this paper, CNN methods are utilized. By leveraging convolutional layers, pooling layers, and fully connected layers, CNNs effectively extract and integrate image features, excelling in tasks such as target classification and attack detection. However, the effectiveness of CNNs requires a significant number of parameters.
  • Dual Rényi [ 11 ]: In the first two layers of the three-layer detection mechanism proposed in this paper, Rényi entropy methods are used, demonstrating the superiority of introducing the random forest classifier in our work and using the BiCNN method in the second layer.

5.3. Evaluation Criteria

5.4. attack modes.

  • Each batch contains 100 entries to facilitate model training and testing.
  • Two types of batches are extracted from the dataset: one type contains entirely benign data, labeled as benign; the other type contains attack data, labeled as attack, with the proportion of attack data allocated according to the aforementioned distribution.

5.5. Results Analysis

6. conclusions, author contributions, data availability statement, conflicts of interest.

  • Fotopoulou, M.; Petridis, S.; Karachalios, I.; Rakopoulos, D. A Review on Distribution System State Estimation Algorithms. Appl. Sci. 2022 , 12 , 11073. [ Google Scholar ] [ CrossRef ]
  • Yang, H.; He, X.; Wang, Z.; Qiu, R.C.; Ai, Q. Blind False Data Injection Attacks Against State Estimation Based on Matrix Reconstruction. IEEE Trans. Smart Grid 2022 , 7 , 3174–3187. [ Google Scholar ] [ CrossRef ]
  • Vinicius, D.; Pedro, R.; Damien, M.; Mario, M. Detection and Mitigation of Low-Rate Denial-of-Service Attacks: A Survey. IEEE Access 2022 , 10 , 76648–76668. [ Google Scholar ]
  • Zhao, J.; Jing, X.; Yan, Z.; Pedrycz, W. Network traffic classification for data fusion: A survey. Inf. Fusion 2021 , 72 , 22–47. [ Google Scholar ] [ CrossRef ]
  • Kaur, S.; Kumar, K.; Aggarwal, N.; Singh, G. A comprehensive survey of DDoS defense solutions in SDN: Taxonomy, research challenges, and future directions. Comput. Secur. 2021 , 110 , 102423. [ Google Scholar ] [ CrossRef ]
  • Deb, R.; Roy, S. A comprehensive survey of vulnerability and information security in SDN. Comput. Netw. 2022 , 206 , 108802. [ Google Scholar ] [ CrossRef ]
  • Yuan, J.; Mills, K. Monitoring the macroscopic effect of DDoS flooding attacks. IEEE Trans. Dependable Secur. Comput. 2005 , 2 , 324–335. [ Google Scholar ] [ CrossRef ]
  • Galeano-Brajones, J.; Carmona-Murillo, J.; Valenzuela-Valdés, J.F.; Luna-Valero, F. Detection and Mitigation of DoS and DDoS Attacks in IoT-Based Stateful SDN: An Experimental Approach. Sensors 2020 , 20 , 816. [ Google Scholar ] [ CrossRef ]
  • Biswas, R.; Kim, S.; Wu, J. Sampling Rate Distribution for Flow Monitoring and DDoS Detection in Datacenter. IEEE Trans. Inf. Forensics Secur. 2021 , 16 , 2524–2534. [ Google Scholar ] [ CrossRef ]
  • Tsobdjou, L.D.; Pierre, S.; Quintero, A. An Online Entropy-Based DDoS Flooding Attack Detection System With Dynamic Threshold. IEEE Trans. Netw. Serv. Manag. 2022 , 19 , 1679–1689. [ Google Scholar ] [ CrossRef ]
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Click here to enlarge figure

LayernameLayerdesign
(Kernel Size, (Padding), Stride)
Output
Input 100 × 18, 1 channel
Conv2d_17 × 7, 3, 1100 × 18, 4 channels
Binary_conv2d_15 × 5, 2, 1100 × 18, 8 channels
Conv2d_25 × 5, 2, 1100 × 18, 16 channels
Pooling2 × 2, (1,2)50 × 9, 16 channels
Binary_conv2d_23 × 3, 1, 150 × 9, 16 channels
GAP 1 × 1, 16 channels
Dense 2
FeaturenameRole
Flow DurationHelps to identify the abnormal duration of potential DDoS attacks.
Total Fwd PacketsChecks for the abnormal increase in total packets sent forward.
Total Backward PacketsChecks for the abnormal increase in total packets sent backward.
Fwd Packets Length TotalIdentifies the large data transfer of potential DDoS attacks.
Bwd Packets Length TotalIdentifies the large data transfer of potential DDoS attacks.
Flow Bytes/sMonitors the high data rate of potential DDoS attacks.
Flow Packets/sMonitors the high packet rate of potential DDoS attacks.
Fwd Packets/sChecks for the abnormal increase in packet rate sent forward.
Bwd Packets/sChecks for the abnormal increase in packet rate sent backward.
Flow IAT MeanIdentifies the abnormal inter-arrival time of potential DDoS attacks.
Flow IAT StdMeasures the variation in inter-arrival time, detecting anomalies.
Flow IAT MaxHelps to measure the long delay in data packets during potential DDoS attacks.
Flow IAT MinIdentifies the short delay of data packets in potential DDoS attacks.
SYN Flag CountHelps to identify the abnormal duration of potential DDoS attacks.
FIN Flag CountChecks for the abnormal increase in total packets sent forward.
PSH Flag CountChecks for the abnormal increase in total packets sent backward.
ACK Flag CountIdentifies the large data transfer of potential DDoS attacks.
LayernameLayerdesign
(Kernel Size, (Padding), Stride)
Output
Input 1 × 8, 1 channel
Conv1d_13, 1, 11 × 8, 8 channels
Conv1d_23, 1, 11 × 8, 16 channels
Conv1d_33, 1, 11 × 8, 32 channels
Pooling2, 21 × 4, 32 channels
GAP 1 × 1, 32 channels
Dense 2
FeaturenameRole
protoProtocol Type: Used to identify the protocol type used by the data packet; different protocol types may behave differently in DDoS attacks.
total_lengthTotal Length: The total length of the data packet; unusually large or small packet lengths may be a characteristic of DDoS attacks.
ttlTime to Live: The lifetime of the data packet in the network; abnormal TTL values may indicate abnormal behavior in the network.
src_portSource Port: The port number of the source device; random source port numbers are often used in DDoS attacks.
dst_portDestination Port: The port number of the target device; DDoS attacks typically focus on certain commonly used service ports.
seqSequence Number: The sequence number in a TCP connection; abnormal sequence numbers may indicate forged data packets.
ackAcknowledgment Number: The acknowledgment number in a TCP connection; abnormal acknowledgment numbers may indicate forged acknowledgment packets.
flagsFlags: TCP flag bits; abnormal combinations of flag bits may be a sign of DDoS attacks.
window_sizeWindow Size: The receiving window size; abnormal window sizes may indicate network congestion or attack behavior.
udp_total_lengthUDP Total Length: The total length of the UDP data packet; abnormal lengths may be a characteristic of DDoS attacks.
icmp_typeICMP Type: The type of ICMP message; certain types of ICMP messages sent in large quantities may be part of a DDoS attack.
icmp_codeICMP Code: The code of the ICMP message; specific ICMP codes may be used to identify attack types.
icmp_data_lengthICMP Data Length: The length of the data portion of the ICMP message; abnormal data lengths may indicate the presence of attack behavior.
DevicesParameter
CPUIntel(R) Core(TM) i9-13900HX 2.20 GHz
GPURTX 4070 Laptop
EnvironmentPython 3.7, PyTorch 2.3.1, Cuda 12.5.78
OSUbuntu 18.04.1
TypesIntensive of DDoS Attack
Single Value151617181920
Interval1–3%4–7%8–10%11–14%
Intensive of DDoSAccPreRecF1
1–3%0.5620.7330.1930.301
4–7%0.7050.9010.4520.599
8–10%0.8320.9020.7440.813
11–14%0.9640.9800.9410.959
15%0.9720.94310.971
16%0.9630.9420.9820.961
17%0.9710.94310.971
18%0.9650.93510.966
19%0.9540.90910.952
20%0.9650.93510.966
Intensive of DDoSAccPreRecF1
1–3%0.6700.7930.4620.582
4–7%0.8960.9080.8850.896
8–10%0.9320.9220.9400.931
11–14%0.9350.9220.9520.936
15%0.9430.89310.943
16%0.9350.9000.9820.938
17%0.9550.9250.9930.956
18%0.9300.8900.9800.933
19%0.9350.8910.9940.938
20%0.9600.92510.961
AccuracyPrecisionRecallF1-Score
0.9720.9930.9750.984
MethodsIntensive in ArticleAcc.% shown in ArticleAcc.% by 10%Acc.% by 6%
φ-entropy [ ]361005854.8
Spark Streaming and Flink [ ] 3394.163.458.02
DualCNN [ ]9599.1392.381
DualRenyi [ ]3098.270.250.4
OursNoneNone90.872.3
Parameters TypeDualCNNOurs
Learnable Parameter227,13217,916
Size of Model886 KB213 KB
Time Cost (ms)0.7980.377
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Share and Cite

Xu, K.; Li, Z.; Liang, N.; Kong, F.; Lei, S.; Wang, S.; Paul, A.; Wu, Z. Research on Multi-Layer Defense against DDoS Attacks in Intelligent Distribution Networks. Electronics 2024 , 13 , 3583. https://doi.org/10.3390/electronics13183583

Xu K, Li Z, Liang N, Kong F, Lei S, Wang S, Paul A, Wu Z. Research on Multi-Layer Defense against DDoS Attacks in Intelligent Distribution Networks. Electronics . 2024; 13(18):3583. https://doi.org/10.3390/electronics13183583

Xu, Kai, Zemin Li, Nan Liang, Fanchun Kong, Shaobo Lei, Shengjie Wang, Agyemang Paul, and Zhefu Wu. 2024. "Research on Multi-Layer Defense against DDoS Attacks in Intelligent Distribution Networks" Electronics 13, no. 18: 3583. https://doi.org/10.3390/electronics13183583

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