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GPU-Based Password Cracking on the Security of Password Hashing Schemes Regarding Advances in Graphics Processing Units
Radboud University Nijmegen Faculty of Science Kerckhoffs Institute Master of Science Thesis GPU-based Password Cracking On the Security of Password Hashing Schemes regarding Advances in Graphics Processing Units by Martijn Sprengers [email protected] Supervisors: Dr. L. Batina (Radboud University Nijmegen) Ir. S. Hegt (KPMG IT Advisory) Ir. P. Ceelen (KPMG IT Advisory) Thesis number: 646 Final Version Abstract Since users rely on passwords to authenticate themselves to computer systems, ad- versaries attempt to recover those passwords. To prevent such a recovery, various password hashing schemes can be used to store passwords securely. However, recent advances in the graphics processing unit (GPU) hardware challenge the way we have to look at secure password storage. GPU's have proven to be suitable for crypto- graphic operations and provide a significant speedup in performance compared to traditional central processing units (CPU's). This research focuses on the security requirements and properties of prevalent pass- word hashing schemes. Moreover, we present a proof of concept that launches an exhaustive search attack on the MD5-crypt password hashing scheme using modern GPU's. We show that it is possible to achieve a performance of 880 000 hashes per second, using different optimization techniques. Therefore our implementation, executed on a typical GPU, is more than 30 times faster than equally priced CPU hardware. With this performance increase, `complex' passwords with a length of 8 characters are now becoming feasible to crack. In addition, we show that between 50% and 80% of the passwords in a leaked database could be recovered within 2 months of computation time on one Nvidia GeForce 295 GTX. -
Argon and Argon2
Argon and Argon2 Designers: Alex Biryukov, Daniel Dinu, and Dmitry Khovratovich University of Luxembourg, Luxembourg [email protected], [email protected], [email protected] https://www.cryptolux.org/index.php/Password https://github.com/khovratovich/Argon https://github.com/khovratovich/Argon2 Version 1.1 of Argon Version 1.0 of Argon2 31th January, 2015 Contents 1 Introduction 3 2 Argon 5 2.1 Specification . 5 2.1.1 Input . 5 2.1.2 SubGroups . 6 2.1.3 ShuffleSlices . 7 2.2 Recommended parameters . 8 2.3 Security claims . 8 2.4 Features . 9 2.4.1 Main features . 9 2.4.2 Server relief . 10 2.4.3 Client-independent update . 10 2.4.4 Possible future extensions . 10 2.5 Security analysis . 10 2.5.1 Avalanche properties . 10 2.5.2 Invariants . 11 2.5.3 Collision and preimage attacks . 11 2.5.4 Tradeoff attacks . 11 2.6 Design rationale . 14 2.6.1 SubGroups . 14 2.6.2 ShuffleSlices . 16 2.6.3 Permutation ...................................... 16 2.6.4 No weakness,F no patents . 16 2.7 Tweaks . 17 2.8 Efficiency analysis . 17 2.8.1 Modern x86/x64 architecture . 17 2.8.2 Older CPU . 17 2.8.3 Other architectures . 17 3 Argon2 19 3.1 Specification . 19 3.1.1 Inputs . 19 3.1.2 Operation . 20 3.1.3 Indexing . 20 3.1.4 Compression function G ................................. 21 3.2 Features . 22 3.2.1 Available features . 22 3.2.2 Server relief . 23 3.2.3 Client-independent update . -
CASH: a Cost Asymmetric Secure Hash Algorithm for Optimal Password Protection
CASH: A Cost Asymmetric Secure Hash Algorithm for Optimal Password Protection Jeremiah Blocki Anupam Datta Microsoft Research Carnegie Mellon University August 23, 2018 Abstract An adversary who has obtained the cryptographic hash of a user's password can mount an offline attack to crack the password by comparing this hash value with the cryptographic hashes of likely password guesses. This offline attacker is limited only by the resources he is willing to invest to crack the password. Key-stretching techniques like hash iteration and memory hard functions have been proposed to mitigate the threat of offline attacks by making each password guess more expensive for the adversary to verify. However, these techniques also increase costs for a legitimate authentication server. We introduce a novel Stackelberg game model which captures the essential elements of this interaction between a defender and an offline attacker. In the game the defender first commits to a key-stretching mechanism, and the offline attacker responds in a manner that optimizes his utility (expected reward minus expected guessing costs). We then introduce Cost Asymmetric Secure Hash (CASH), a randomized key-stretching mechanism that minimizes the fraction of passwords that would be cracked by a rational offline attacker without increasing amortized authentication costs for the legitimate authentication server. CASH is motivated by the observation that the legitimate authentication server will typically run the authentication procedure to verify a correct password, while an offline adversary will typically use incorrect password guesses. By using randomization we can ensure that the amortized cost of running CASH to verify a correct password guess is significantly smaller than the cost of rejecting an incorrect password. -
Implementation and Performance Analysis of PBKDF2, Bcrypt, Scrypt Algorithms
Implementation and Performance Analysis of PBKDF2, Bcrypt, Scrypt Algorithms Levent Ertaul, Manpreet Kaur, Venkata Arun Kumar R Gudise CSU East Bay, Hayward, CA, USA. [email protected], [email protected], [email protected] Abstract- With the increase in mobile wireless or data lookup. Whereas, Cryptographic hash functions are technologies, security breaches are also increasing. It has used for building blocks for HMACs which provides become critical to safeguard our sensitive information message authentication. They ensure integrity of the data from the wrongdoers. So, having strong password is that is transmitted. Collision free hash function is the one pivotal. As almost every website needs you to login and which can never have same hashes of different output. If a create a password, it’s tempting to use same password and b are inputs such that H (a) =H (b), and a ≠ b. for numerous websites like banks, shopping and social User chosen passwords shall not be used directly as networking websites. This way we are making our cryptographic keys as they have low entropy and information easily accessible to hackers. Hence, we need randomness properties [2].Password is the secret value from a strong application for password security and which the cryptographic key can be generated. Figure 1 management. In this paper, we are going to compare the shows the statics of increasing cybercrime every year. Hence performance of 3 key derivation algorithms, namely, there is a need for strong key generation algorithms which PBKDF2 (Password Based Key Derivation Function), can generate the keys which are nearly impossible for the Bcrypt and Scrypt. -
Speeding up Linux Disk Encryption Ignat Korchagin @Ignatkn $ Whoami
Speeding Up Linux Disk Encryption Ignat Korchagin @ignatkn $ whoami ● Performance and security at Cloudflare ● Passionate about security and crypto ● Enjoy low level programming @ignatkn Encrypting data at rest The storage stack applications @ignatkn The storage stack applications filesystems @ignatkn The storage stack applications filesystems block subsystem @ignatkn The storage stack applications filesystems block subsystem storage hardware @ignatkn Encryption at rest layers applications filesystems block subsystem SED, OPAL storage hardware @ignatkn Encryption at rest layers applications filesystems LUKS/dm-crypt, BitLocker, FileVault block subsystem SED, OPAL storage hardware @ignatkn Encryption at rest layers applications ecryptfs, ext4 encryption or fscrypt filesystems LUKS/dm-crypt, BitLocker, FileVault block subsystem SED, OPAL storage hardware @ignatkn Encryption at rest layers DBMS, PGP, OpenSSL, Themis applications ecryptfs, ext4 encryption or fscrypt filesystems LUKS/dm-crypt, BitLocker, FileVault block subsystem SED, OPAL storage hardware @ignatkn Storage hardware encryption Pros: ● it’s there ● little configuration needed ● fully transparent to applications ● usually faster than other layers @ignatkn Storage hardware encryption Pros: ● it’s there ● little configuration needed ● fully transparent to applications ● usually faster than other layers Cons: ● no visibility into the implementation ● no auditability ● sometimes poor security https://support.microsoft.com/en-us/help/4516071/windows-10-update-kb4516071 @ignatkn Block -
Rifflescrambler – a Memory-Hard Password Storing Function ⋆
RiffleScrambler – a memory-hard password storing function ? Karol Gotfryd1, Paweł Lorek2, and Filip Zagórski1;3 1 Wrocław University of Science and Technology Faculty of Fundamental Problems of Technology Department of Computer Science 2 Wrocław University Faculty of Mathematics and Computer Science Mathematical Institute 3 Oktawave Abstract. We introduce RiffleScrambler: a new family of directed acyclic graphs and a corresponding data-independent memory hard function with password independent memory access. We prove its memory hard- ness in the random oracle model. RiffleScrambler is similar to Catena – updates of hashes are determined by a graph (bit-reversal or double-butterfly graph in Catena). The ad- vantage of the RiffleScrambler over Catena is that the underlying graphs are not predefined but are generated per salt, as in Balloon Hashing. Such an approach leads to higher immunity against practical parallel at- tacks. RiffleScrambler offers better efficiency than Balloon Hashing since the in-degree of the underlying graph is equal to 3 (and is much smaller than in Ballon Hashing). At the same time, because the underlying graph is an instance of a Superconcentrator, our construction achieves the same time-memory trade-offs. Keywords: Memory hardness, password storing, Markov chains, mixing time. 1 Introduction In early days of computers’ era passwords were stored in plaintext in the form of pairs (user; password). Back in 1960s it was observed, that it is not secure. It took around a decade to incorporate a more secure way of storing users’ passwords – via a DES-based function crypt, as (user; fk(password)) for a se- cret key k or as (user; f(password)) for a one-way function. -
Hash Functions
11 Hash Functions Suppose you share a huge le with a friend, but you are not sure whether you both have the same version of the le. You could send your version of the le to your friend and they could compare to their version. Is there any way to check that involves less communication than this? Let’s call your version of the le x (a string) and your friend’s version y. The goal is to determine whether x = y. A natural approach is to agree on some deterministic function H, compute H¹xº, and send it to your friend. Your friend can compute H¹yº and, since H is deterministic, compare the result to your H¹xº. In order for this method to be fool-proof, we need H to have the property that dierent inputs always map to dierent outputs — in other words, H must be injective (1-to-1). Unfortunately, if H is injective and H : f0; 1gin ! f0; 1gout is injective, then out > in. This means that sending H¹xº is no better/shorter than sending x itself! Let us call a pair ¹x;yº a collision in H if x , y and H¹xº = H¹yº. An injective function has no collisions. One common theme in cryptography is that you don’t always need something to be impossible; it’s often enough for that thing to be just highly unlikely. Instead of saying that H should have no collisions, what if we just say that collisions should be hard (for polynomial-time algorithms) to nd? An H with this property will probably be good enough for anything we care about. -
Cs 255 (Introduction to Cryptography)
CS 255 (INTRODUCTION TO CRYPTOGRAPHY) DAVID WU Abstract. Notes taken in Professor Boneh’s Introduction to Cryptography course (CS 255) in Winter, 2012. There may be errors! Be warned! Contents 1. 1/11: Introduction and Stream Ciphers 2 1.1. Introduction 2 1.2. History of Cryptography 3 1.3. Stream Ciphers 4 1.4. Pseudorandom Generators (PRGs) 5 1.5. Attacks on Stream Ciphers and OTP 6 1.6. Stream Ciphers in Practice 6 2. 1/18: PRGs and Semantic Security 7 2.1. Secure PRGs 7 2.2. Semantic Security 8 2.3. Generating Random Bits in Practice 9 2.4. Block Ciphers 9 3. 1/23: Block Ciphers 9 3.1. Pseudorandom Functions (PRF) 9 3.2. Data Encryption Standard (DES) 10 3.3. Advanced Encryption Standard (AES) 12 3.4. Exhaustive Search Attacks 12 3.5. More Attacks on Block Ciphers 13 3.6. Block Cipher Modes of Operation 13 4. 1/25: Message Integrity 15 4.1. Message Integrity 15 5. 1/27: Proofs in Cryptography 17 5.1. Time/Space Tradeoff 17 5.2. Proofs in Cryptography 17 6. 1/30: MAC Functions 18 6.1. Message Integrity 18 6.2. MAC Padding 18 6.3. Parallel MAC (PMAC) 19 6.4. One-time MAC 20 6.5. Collision Resistance 21 7. 2/1: Collision Resistance 21 7.1. Collision Resistant Hash Functions 21 7.2. Construction of Collision Resistant Hash Functions 22 7.3. Provably Secure Compression Functions 23 8. 2/6: HMAC And Timing Attacks 23 8.1. HMAC 23 8.2. -
Forgery and Key Recovery Attacks for Calico
Forgery and Key Recovery Attacks for Calico Christoph Dobraunig, Maria Eichlseder, Florian Mendel, Martin Schl¨affer Institute for Applied Information Processing and Communications Graz University of Technology Inffeldgasse 16a, A-8010 Graz, Austria April 1, 2014 1 Calico v8 Calico [3] is an authenticated encryption design submitted to the CAESAR competition by Christopher Taylor. In Calico v8 in reference mode, ChaCha-14 and SipHash-2-4 work together in an Encrypt-then-MAC scheme. For this purpose, the key is split into a Cipher Key KC and a MAC Key KM . The plaintext is encrypted with ChaCha under the Cipher Key to a ciphertext with the same length as the plaintext. Then, the tag is calculated as the SipHash MAC of the concatenated ciphertext and associated data. The key used for SipHash is generated by xoring the nonce to the (lower, least significant part of the) MAC Key: (C; T ) = EncCalico(KC k KM ; N; A; P ); where k is concatenation, and with ⊕ denoting xor, the ciphertext and tag are calculated vi C = EncChaCha-14(KC ; N; P ) T = MACSipHash-2-4(KM ⊕ N; C k A): Here, A; P; C denote associated data, plaintext and ciphertext, respectively, all of arbitrary length. T is the 64-bit tag, N the 64-bit nonce, and the 384-bit key K is split into a 256-bit encryption and 128-bit authentication part, K = KC k KM . 2 Missing Domain Separation As shown above, the tag is calculated over the concatenation C k A of ciphertext and asso- ciated data. Due to the missing domain separation between ciphertext and associated data in the generation of the tag, the following attack is feasible. -
How to Handle Rainbow Tables with External Memory
How to Handle Rainbow Tables with External Memory Gildas Avoine1;2;5, Xavier Carpent3, Barbara Kordy1;5, and Florent Tardif4;5 1 INSA Rennes, France 2 Institut Universitaire de France, France 3 University of California, Irvine, USA 4 University of Rennes 1, France 5 IRISA, UMR 6074, France [email protected] Abstract. A cryptanalytic time-memory trade-off is a technique that aims to reduce the time needed to perform an exhaustive search. Such a technique requires large-scale precomputation that is performed once for all and whose result is stored in a fast-access internal memory. When the considered cryptographic problem is overwhelmingly-sized, using an ex- ternal memory is eventually needed, though. In this paper, we consider the rainbow tables { the most widely spread version of time-memory trade-offs. The objective of our work is to analyze the relevance of storing the precomputed data on an external memory (SSD and HDD) possibly mingled with an internal one (RAM). We provide an analytical evalua- tion of the performance, followed by an experimental validation, and we state that using SSD or HDD is fully suited to practical cases, which are identified. Keywords: time memory trade-off, rainbow tables, external memory 1 Introduction A cryptanalytic time-memory trade-off (TMTO) is a technique introduced by Martin Hellman in 1980 [14] to reduce the time needed to perform an exhaustive search. The key-point of the technique resides in the precomputation of tables that are then used to speed up the attack itself. Given that the precomputation phase is much more expensive than an exhaustive search, a TMTO makes sense in a few scenarios, e.g., when the adversary has plenty of time for preparing the attack while she has a very little time to perform it, the adversary must repeat the attack many times, or the adversary is not powerful enough to carry out an exhaustive search but she can download precomputed tables. -
Sketch of Lecture 34 Mon, 4/16/2018
Sketch of Lecture 34 Mon, 4/16/2018 Passwords Let's say you design a system that users access using personal passwords. Somehow, you need to store the password information. The worst thing you can do is to actually store the passwords m. This is an absolutely atrocious choice, even if you take severe measures to protect (e.g. encrypt) the collection of passwords. Comment. Sadly, there is still systems out there doing that. An indication of this happening is systems that require you to update passwords and then complain that your new password is too close to the original one. Any reasonably designed system should never learn about your actual password in the rst place! Better, but still terrible, is to instead store hashes H(m) of the passwords m. Good. An attacker getting hold of the password le, only learns about the hash of a user's password. Assuming the hash function is one-way, it is infeasible for the attacker to determine the corresponding password (if the password was random!!). Still bad. However, passwords are (usually) not random. Hence, an attacker can go through a list of common passwords (dictionary attack), compute the hashes and compare with the hashes of users (similarly, a brute-force attack can simply go through all possible passwords). Even worse, it is immediately obvious if two users are using the same password (or, if the same user is using the same password for dierent services using the same hash function). Comment. So, storing password hashes is not OK unless all passwords are completely random. -
Securing Audio Using AES-Based Authenticated Encryption with Python
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 9 August 2021 doi:10.20944/preprints202108.0185.v1 Article Securing Audio Using AES-based Authenticated Encryption with Python Jessy Ayala 1 1 New York University, Tandon School of Engineering; [email protected] Featured Application: Securing communication of audio files utilizing symmetric authenticated encryption. Abstract: The focus of this research is to analyze the results of encrypting audio using various au- thenticated encryption algorithms implemented in the Python cryptography library for ensuring authenticity and confidentiality of the original contents. The Advanced Encryption Standard (AES) is used as the underlying cryptographic primitive in conjunction with various modes including Gal- ois Counter Mode (GCM), Counter with Cipher Block Chaining Message Authentication Code (CCM), and Cipher Block Chaining (CBC) with Keyed-Hashing for encrypting a relatively small audio file. The resulting encrypted audio shows similarity in the variance when encrypting using AES-GCM and AES-CCM. There is a noticeable reduction in variance of the performed encodings and an increase in the amount of time it takes to encrypt and decrypt the same audio file using AES- CBC with Keyed-Hashing. In addition, the corresponding encrypted using this mode audio spans a longer duration. As a result, AES should either have GCM or CCM for an efficient and reliable authenticated encryption integration within a workflow. Keywords: AES; Audio analysis; Authenticated encryption; Cryptography; Python 1. Introduction Cryptography is used worldwide for adhering to the security CIA triad: confidenti- ality, integrity, and availability. In an environment where mobile devices have become ubiquitous, voice messages are more common than one may think.