Bibliographic record
Abstract
Wireless LANs are very common in any household or business today. It allows access to their home or business network and the Internet without using wires. Their wireless nature allows mobility and convenience for the user and that opens up a lot of new possibilities in mobile devices such as smartphones and tablets. One application that makes use of wireless LANs is positioning, which can be used in areas where Global Positioning Systems may have trouble functioning or not at all. However, a drawback of using wireless communication is that it is susceptible to eavesdropping and jamming. Once the wireless signal is jammed, an attacker can set up fake access points on different channels or frequencies to impersonate a legitimate access point. In this thesis, this attack is performed specifically to trick WiFi-based location services. The attack is shown to work on Skyhook, Google, Apple and Microsoft location services, four of the major location service providers, and on dual-band hardware. Some countermeasures to such an attack are also presented. \n \nThe web is an important part of many people’s lives nowadays. People expect that their privacy and confidentiality is preserved when they use the web. Previously, web traffic uses HTTP which meant traffic is all unencrypted and can be intercepted and read by attackers. This is clearly a security problem so many websites now default to using a more secure protocol, namely HTTPS which uses HTTP with SSL, and forces the user to HTTPS if they connect to the no SSL protocol. SSL works by exchanging keys between the client and server and the actual data is protected using the key and the cipher suite that is negotiated between the two. However, if a network uses a proxy server, it works slightly different. The SSL connection is broken up into two separate ones and that creates the potential for man-in-the-middle attacks that allow an attacker to intercept the data being transmitted. This thesis analyzes several scenarios in which an adversary can conduct such a man-in-the-middle attack, and potential detection and mitigation methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".