Bibliographic record
Abstract
Phishing has become the most convenient technique that hackers use nowadays to gain access to protected systems. This is because cybersecurity has evolved and low-cost systems with the least security investments will need quite advanced and sophisticated mechanisms to be able to penetrate technically. Systems currently are equipped with at least some level of security, imposed by security firms with a very high level of expertise in managing the common and well-known attacks. This decreases the possible technical attack surface. Nation-states or advanced persistent threats (APTs), organized crime, and black hats possess the finance and skills to penetrate many different systems. However, they are always in need of the most available computing resources, such as central processing unit (CPU) and random-access memory (RAM), so they normally hack and hook computers into a botnet. This may allow them to perform dangerous distributed denial of service (DDoS) attacks and perform brute force cracking algorithms, which are highly CPU intensive. They may also use the zombie or drone systems they have hacked to hide their location on the net and gain anonymity by bouncing off around them many times a minute. Phishing allows them to gain their stretch of compromised systems to increase their power. For a normal hacker without the money to invest in sophisticated techniques, exploiting the human factor, which is the weakest link to security, comes in handy. The possibility of successfully manipulating the human into releasing the security that they set up makes the life of the hacker very easy, because they do not have to try to break into the system with force, rather the owner will just open the door for them. The objective of the research is to review factors that enhance phishing and improve the probability of its success. We have discovered that hackers rely on triggering the emotional effects of their victims through their phishing attacks. We have applied the use of artificial intelligence to be able to detect the emotion associated with a phrase or sentence. Our model had a good accuracy which could be improved with the use of a larger dataset with more emotional sentiments for various phrases and sentences. Our technique may be used to check for emotional manipulation in suspicious emails to improve the confidence interval of suspected phishing emails.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".