Targeted Attacks: Redefining Spear Phishing and Business Email Compromise

• URL: http://arxiv.org/abs/2309.14166v1
• Date: Mon, 25 Sep 2023 14:21:59 GMT
• Title: Targeted Attacks: Redefining Spear Phishing and Business Email Compromise
• Authors: Sarah Wassermann, Maxime Meyer, Sébastien Goutal, Damien Riquet,
• Abstract summary: Some rare, severely damaging email threats - known as spear phishing or Business Email Compromise - have emerged. We describe targeted-attack-detection techniques as well as social-engineering methods used by fraudsters. We present text-based attacks - with textual content as malicious payload - and compare non-targeted and targeted variants.
• Score: 0.17175834535889653
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In today's digital world, cybercrime is responsible for significant damage to organizations, including financial losses, operational disruptions, or intellectual property theft. Cyberattacks often start with an email, the major means of corporate communication. Some rare, severely damaging email threats - known as spear phishing or Business Email Compromise - have emerged. However, the literature disagrees on their definition, impeding security vendors and researchers from mitigating targeted attacks. Therefore, we introduce targeted attacks. We describe targeted-attack-detection techniques as well as social-engineering methods used by fraudsters. Additionally, we present text-based attacks - with textual content as malicious payload - and compare non-targeted and targeted variants.

Related papers

• Evaluating the Efficacy of Large Language Models in Identifying Phishing Attempts [2.6012482282204004]
  Phishing, a prevalent cybercrime tactic for decades, remains a significant threat in today's digital world. This paper aims to analyze the effectiveness of 15 Large Language Models (LLMs) in detecting phishing attempts.
  arXiv  Detail & Related papers  (2024-04-23T19:55:18Z)
• A framework for securing email entrances and mitigating phishing impersonation attacks [0.0]
  This work intends to protect users' email composition and settings. A secure code is applied to the composition send button to curtail insider impersonation attack. Also, to secure open applications on public and private devices.
  arXiv  Detail & Related papers  (2023-12-07T07:28:34Z)
• A Serious Game for Simulating Cyberattacks to Teach Cybersecurity [0.3149883354098941]
  Traditional training methods have proven to be less effective over time. In this paper we suggest an alternative approach -- a serious game. Our game explains the intention of each attack and shows the consequences of a successful attack.
  arXiv  Detail & Related papers  (2023-05-04T08:04:24Z)
• A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction [100.9772316028191]
  In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation.
  arXiv  Detail & Related papers  (2022-05-01T05:12:22Z)
• Zero-Query Transfer Attacks on Context-Aware Object Detectors [95.18656036716972]
  Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check. We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check.
  arXiv  Detail & Related papers  (2022-03-29T04:33:06Z)
• A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning [60.826628282900955]
  We show that targeted attacks on black-box NMT systems are feasible, based on poisoning a small fraction of their parallel training data. We show that this attack can be realised practically via targeted corruption of web documents crawled to form the system's training data. Our results are alarming: even on the state-of-the-art systems trained with massive parallel data, the attacks are still successful (over 50% success rate) under surprisingly low poisoning budgets.
  arXiv  Detail & Related papers  (2020-11-02T01:52:46Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)
• Phishing and Spear Phishing: examples in Cyber Espionage and techniques to protect against them [91.3755431537592]
  Phishing attacks have become the most used technique in the online scams, initiating more than 91% of cyberattacks, from 2012 onwards. This study reviews how Phishing and Spear Phishing attacks are carried out by the phishers, through 5 steps which magnify the outcome.
  arXiv  Detail & Related papers  (2020-05-31T18:10:09Z)
• Arms Race in Adversarial Malware Detection: A Survey [33.8941961394801]
  Malicious software (malware) is a major cyber threat that has to be tackled with Machine Learning (ML) techniques. ML is vulnerable to attacks known as adversarial examples. Knowing the defender's feature set is critical to the success of transfer attacks. The effectiveness of adversarial training depends on the defender's capability in identifying the most powerful attack.
  arXiv  Detail & Related papers  (2020-05-24T07:20:42Z)
• Challenges in Forecasting Malicious Events from Incomplete Data [6.656003516101928]
  Researchers have attempted to combine external data with machine learning algorithms to learn indicators of impending cyber-attacks. But successful cyber-attacks represent a tiny fraction of all attempted attacks. As we show in this paper, the process of filtering reduces the predictability of cyber-attacks.
  arXiv  Detail & Related papers  (2020-04-06T22:57:23Z)
• Deflecting Adversarial Attacks [94.85315681223702]
  We present a new approach towards ending this cycle where we "deflect" adversarial attacks by causing the attacker to produce an input that resembles the attack's target class. We first propose a stronger defense based on Capsule Networks that combines three detection mechanisms to achieve state-of-the-art detection performance.
  arXiv  Detail & Related papers  (2020-02-18T06:59:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.