Can the Best Defense be a Good Offense? Evolving (Mimicry) Attacks for Detector Vulnerability Testing under a black-box Assumption

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Kayacik:thesis,
  author =       "Hilmi Gunes Kayacik",
  title =        "Can the Best Defense be a Good Offense? Evolving
                 (Mimicry) Attacks for Detector Vulnerability Testing
                 under a black-box Assumption",
  school =       "Dalhousie University",
  year =         "2009",
  address =      "Halifax, Nova Scotia, Canada",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://web.cs.dal.ca/~kayacik/PhD/GK_Thesis.pdf",
  size =         "374 pages",
  abstract =     "This thesis proposes a black-box approach for
                 automating attack generation by way of Evolutionary
                 Computation. The proposed black-box approach employs
                 just the anomaly rate or detection feedback from the
                 detector. Assuming a black-box access in vulnerability
                 testing presents a scenario different from a white-box
                 access assumption, since the attacker does not posses
                 sufficient knowledge to constrain the scope of the
                 attack. As such, this thesis contributes by providing a
                 black-box vulnerability testing tool for identifying
                 detector weaknesses and aiding detector research in
                 designing detectors which are robust against evasion
                 attacks.

                 The proposed approach focuses on stack buffer overflow
                 attacks on a 32-bit Intel architecture and aims to
                 optimise the various characteristics of the attack.
                 Three components exist in a common stack buffer
                 overflow attack: the shellcode, NoOP and return address
                 components. Therefore, automation of attack generation
                 is realised in three stages: (1) identifying the
                 suitable NoOP and return address components, (2)
                 designing the shellcode at the assembly level, and (3)
                 designing the shellcode at the system call level. The
                 first and second stage address the evasion of misuse
                 detectors by employing obfuscation, whereas the third
                 stage addresses the evasion of anomaly detectors by
                 employing mimicry attacks.

                 In short, the proposed approach takes the form of a
                 black-box search process where the attacks are rewarded
                 according to two main criteria: (a) their ability to
                 carry out the malicious intent, while (b) minimising or
                 eliminating the detectable attack characteristics.
                 Furthermore, it is demonstrated that there are two
                 parts to buffer overflow attacks: (i) the preamble and
                 (ii) the exploit. Therefore, the anomaly rate of the
                 whole attack is calculated on both parts. Additionally,
                 the proposed approach supports multi-objective
                 optimisation, where multiple characteristics of attacks
                 can be improved. The proposed approach is evaluated
                 against six detectors and four vulnerable applications.
                 The results show that attacks which the proposed
                 approach generates under a black-box assumption are as
                 effective as the attacks in generated under a white-box
                 assumption adopted by previous work.",
  notes =        "slides:
                 http://web.cs.dal.ca/~kayacik/PhD/GK_Defense_Slides.pdf",
}

Genetic Programming entries for Hilmi Gunes Kayacik

Citations