Posts Tagged ‘AES

Direct Cryptanalytic Attacks

During a cryptanalytic attack the adversary observes the output and tries to gain any information about the inner state or future output of the generator. Many RNGs (Random Number Generators) use cryptographic primitives like hash functions (e.g. SHA-1 or MD5) or block ciphers (DES,Triple-DES, AES) to prevent this kind of attacks. The underlying assumption is that the cryptographic security of the primitives transfers to the generators which employ them.Generally, the con dence into the security of this primitives is based only partially on mathematical analysis but mainly on empirical results and statistical tests. Since most of the applications that apply cryptographic RNGs rely on those primitives, we may have con dence in their security as well.

Nevertheless, it is not advisable to blindly trust generators that are built on cryptographic primitives as we will see by the example of the Kerberos 4 session key generator. The speci c method of employing the primitives has a main impact on the security of the generator as well. The Kerberos 4 generator produces a 56-bit key fora DES block cipher by two successive calls of the UNIX random function which uses onlya 32 bit key. The random function is seeded every time a key is requested. Consequently,the strength of the encryption and, thus, the resistance against cryptanalytic attacks is reduced from 56 to 32 bits. It still takes about 6 hours on a DEC Alpha to gain the proper key of a plain text-cipher text pair by brute force, but we see that the 56 bit strength of the encryption is only an illusion. It is the weakest link in the chain that counts.

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Execute-Only Memory (XOM)

The XOM approach, which provides memory protection, is based on a complex key management. The main XOM features are: data ciphering, data hashing, data partitioning, interruption and context switching protection. Figure 1.0 and 1.1 give an overview of the XOM architecture and mechanisms. All the security primitives are included in the trusted zone. The only security information which are not in the trusted zone are the session keys. That is why XOM owns a complex key management to guarantee a secure architecture.

Figure 1.0: XOM architecture for write request

Figure 1.1: XOM architecture for read request

In order to guarantee the data confidentiality and integrity, each memory partitionis associated with a session key which is needed to decrypt its content. Encrypted session keys are stored in main memory and can be decrypted using an asymmetric cipher algorithm (RSA in XOM case). Decrypted session keys are stored in the XOM key table (in the secure zone). The private key required for the asymmetric decryption is stored in the secure zone of the architecture (RSA key in Figures 1.0 & 1.1). The algorithm used for the symmetric deciphering is an AES 256 (256 bits key and 256 bits data input). For write requests, a hash value of the data and its address are concatenated with the data before ciphering with AES. The use of the address in the hash value is there to prevent the relocation attacks. When the core produces a cache miss for a read request, the 256 bits read from the memory need to be decrypted (Figure 1.1). Data integrity is ensured by a hash value relying on a MD5 computation. The hash of the deciphered data and its address are compared with deciphered hash value. If the new computed hash value matches with the deciphered one the data is considered secure and can be used by the processor.

In addition, the data stored in cache memory are associated with an identifier or tag in order to guarantee the data partitioning at a cache level. When a task needs to usea data, the task identifier must be the same as the data, in that case it means the task is allowed to access the data. The tag value are provided by the XOM key table which also manages this part.

All the protections added by this solution have a cost. The first one concerns the XOM implementation in an existing OS. A work is necessary on the OS kernel to add the instructions which help for the hardware security primitives use. All this work is transparent for the kernel user. According to the figures from, a real overhead appears in the cache management (cache miss raises from 10 to 40% depending on the application). This raise is mainly due to the information added into the cache to secure the data. Indeed, by adding some data tagging, some space in the cache memory is lost compared with a non protected solution. Moreover, all the security features are bringing some latency in the system to obtain the data in clear (data de/ciphering, hashing, tag checking). Even if these security primitives are done in hardware, the general architecture performances are slowed down. The decryption needs to be done before the integrity checking. These two operations are not done in parallel, so some more latency is added. Some latency is also added to the software execution because of some software security primitive (secure context switching add some specific instruction for example).

The first proposed version of XOM is known to have security holes like noprotection against replay attacks. In, the authors extended the proposition and replaced the AES-based ciphering scheme with a system based on OTP to guarantee protection against replay attacks and also to increase the performances of the system. Concerning the global security level of the XOM architecture, the attack possibilities are fully dependent on the integrity checking capabilities. To succeed, the attacker mustbe able to pass through the integrity checking in order to execute his own program or use his own data. He may exploit some collisions in the hash algorithm used. For example, with MD5 the signature is 128 bits long. If he wishes to attack the system, he needs to find two inputs which will produce the same result with MD5.

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Desirable Quantum Key Distribution Attributes

Broadly stated, QKD(Quantum Key Distribution) offers a technique for coming to agreement upon a shared random sequence of bits within two distinct devices, with a very low probability that other devices(eavesdroppers) will be able to make successful inferences as to those bits’ values. In specific practice, such sequences are then used as secret keys for encoding and decoding messages between the two devices. Viewed in this light, QKD is quite clearly a key distribution technique, and one can rate QKD’s strengths against a number of important goals for key distribution, as summarized in the following paragraphs.

Confidentiality of Keys : Confidentiality is the main reason for interest in QKD. Public key systems suffer from an ongoing uncertainty that decryption is mathematically intractable. Thus key agreement primitives widely used in today’s Internet security architecture, e.g., Diffie-Hellman, may perhaps be broken at some point in the future. This would not only hinder future ability to communicate but could reveal past traffic.Classic secret key systems have suffered from different problems, namely, insider threats and the logistical burden of distributing keying material. Assuming that QKD techniques are properly embedded into an overall secure system, they can provide automatic distribution of keys that may offer security superior to that of its competitors.

Authentication : QKD does not in itself provide authentication.Current strategies for authentication in QKD systems include prepositioning of secret keys at pairs of devices, to be used in hash-based authentication schemes, or hybrid QKD-public key techniques. Neither approach is entirely appealing. Prepositioned secret keys require some means of distributing these keys before QKD itself begins, e.g., by human courier,which may be costly and logistically challenging. Furthermore, this approach appears open to denial of service attacks in which an adversary forces a QKD system to exhaust its stockpile of key material, at which point it can no longer perform authentication. On the other hand, hybrid QKD-public key schemes inherit the possible vulnerabilities of public key systems to cracking via quantum computers or unexpectedadvances in mathematics.

Sufficiently Rapid Key Delivery : Key distribution systems must deliver keys fast enough so that encryption devices do not exhaust their supply of key bits. This is a race between the rate at which keying material is put into place and the rate at which it is consumed for encryption or decryption activities. Today’s QKD systems achieve on the order of 1,000 bits/second throughput for keying material, in realistic settings, and often run at much lower rates. This is unacceptably low if one uses these keys in certain ways, e.g., as one-time pads for high speed traffic flows. However it may well be acceptable if the keying material is used as input for less secure (but often secure enough) algorithms such as the Advanced Encryption Standard. Nonetheless, it is both desirable and possible togreatly improve upon the rates provided by today’s QKD technology.

Robustness : This has not traditionally been taken into account by the QKD community. However, since keying material is essential for secure communications, it is extremely important that the flow of keying material not be disrupted, whether by accident or by the deliberate acts of an adversary (i.e. by denial of service). Here QKD has provided a highly fragile service to date since QKD techniques have implicitly been employed along a single point-to-point link. If that link were disrupted,whether by active eavesdropping or indeed by fiber cut, all flow of keying material would cease. In our view a meshed QKD network is inherently far more robust than any single point-to-point link since it offers multiple paths for key distribution.

Distance- and Location-Independence : In the ideal world,any entity can agree upon keying material with any other(authorized) entity in the world. Rather remarkably, the Internet’s security architecture does offer this feature – any computer on the Internet can form a security association with any other, agreeing upon keys through the Internet IPsec protocols. This feature is notably lacking in QKD, which requires the two entities to have a direct and unencumbered path for photons between them, and which can only operate fora few tens of kilometers through fiber.

Resistance to Traffic Analysis : Adversaries may be able to perform useful traffic analysis on a key distribution system,e.g., a heavy flow of keying material between two points might reveal that a large volume of confidential information flows, or will flow, between them. It may thus be desirable to impede such analysis. Here QKD in general has had a rather weak approach since most setups have assumed dedicated, point-to-point QKD links between communicating entities which thus clearly lays out the underlying key distribution relationships.

 

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