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Birthday Attack

The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

Marcel Waldvogel – One of the best experts on this subject based on the ideXlab platform.

  • ACSAC – GOSSIB vs. IP traceback rumors
    18th Annual Computer Security Applications Conference 2002. Proceedings., 2002
    Co-Authors: Marcel Waldvogel

    Abstract:

    To identify sources of distributed denial-of-service Attacks, path traceback mechanisms have been proposed. Traceback mechanisms relying on probabilistic packet marking (PPM) have received most attention, as they are easy to implement and deploy incrementally. We introduce a new concept, namely Groups Of Strongly SImilar Birthdays (GOSSIB), that can be used by to obtain effects similar to a successful Birthday Attack on PPM schemes. The original and most widely known IP traceback mechanism, compressed edge fragment sampling (CEFS), was developed by Savage et al. (2000). We analyze the effects of an Attacker using GOSSIB against CEFS and show that the Attacker can seed misinformation much more efficiently than the network is able to contribute real traceback information. Thus, GOSSIB will render PPM effectively useless. It can be expected that GOSSIB has similar effects on other PPM traceback schemes and that standard modifications to the systems will not solve the problem.

  • GOSSIB vs. IP traceback rumors
    Proceedings – Annual Computer Security Applications Conference ACSAC, 2002
    Co-Authors: Marcel Waldvogel

    Abstract:

    To identify sources of distributed denial-of-service Attacks, path\ntraceback mechanisms have been proposed. Traceback mechanisms relying on\nprobabilistic packet marking (PPM) have received most attention, as they\nare easy to Implement and deploy incrementally. In this paper, we\nintroduce a new concept, namely Groups Of Strongly Similar Birthdays\n(GOSSIB(1)), that can be used by to obtain effects similar to a\nsuccessful Birthday Attack on PPM schemes. The original and most widely\nknown IP traceback mechanism, compressed edge fragment sampling (CEFS),\nwas developed by Savage et al. {[}SWKA00]. We analyze the effects of an\nAttacker using GOSSIB against CEFS and show that the Attacker can seed\nmisinformation much more efficiently than the network is able to\ncontribute real traceback information. Thus, GOSSIB will render PPM\neffectively useless. It can be expected that GOSSIB has similar effects\non other PPM traceback schemes and that standard modifications to the\nsystems will not solve the problem.

  • GOSSIB vs. IP traceback rumors
    18th Annual Computer Security Applications Conference 2002. Proceedings., 2002
    Co-Authors: Marcel Waldvogel

    Abstract:

    To identify sources of distributed denial-of-service Attacks, path traceback mechanisms have been proposed. Traceback mechanisms relying on probabilistic packet marking (PPM) have received most attention, as they are easy to implement and deploy incrementally. We introduce a new concept, namely Groups Of Strongly SImilar Birthdays (GOSSIB), that can be used by to obtain effects similar to a successful Birthday Attack on PPM schemes. The original and most widely known IP traceback mechanism, compressed edge fragment sampling (CEFS), was developed by Savage et al. (2000). We analyze the effects of an Attacker using GOSSIB against CEFS and show that the Attacker can seed misinformation much more efficiently than the network is able to contribute real traceback information. Thus, GOSSIB will render PPM effectively useless. It can be expected that GOSSIB has similar effects on other PPM traceback schemes and that standard modifications to the systems will not solve the problem.

Haila Wang – One of the best experts on this subject based on the ideXlab platform.

  • Hash function based on chaotic neural networks
    2006 IEEE International Symposium on Circuits and Systems, 2006
    Co-Authors: Shiguo Lian, Haila Wang

    Abstract:

    Chaos and neural networks have both been used in data encryption because of their cipher-suitable properties, such as parameter-sensitivity, time-varying, random-similarity, etc. Based on chaotic neural networks, a hash function is constructed, which makes use of neural networks’ diffusion property and chaos’ confusion property. This function encodes the plaintext of arbitrary length into the hash value of fixed length (typically, 128-bit, 256-bit or 512-bit). Its security against statistical Attack, Birthday Attack and meet-in-the-middle Attack is analyzed in detail. Its properties make it a suitable choice for data authentication

  • ISCAS – Hash function based on chaotic neural networks
    2006 IEEE International Symposium on Circuits and Systems, 2006
    Co-Authors: Shiguo Lian, Haila Wang

    Abstract:

    Chaos and neural networks have both been used in data encryption because of their cipher-suitable properties, such as parameter-sensitivity, time-varying, random-similarity, etc. Based on chaotic neural networks, a hash function is constructed, which makes use of neural networks’ diffusion property and chaos’ confusion property. This function encodes the plaintext of arbitrary length into the hash value of fixed length (typically, 128-bit, 256-bit or 512-bit). Its security against statistical Attack, Birthday Attack and meet-in-the-middle Attack is analyzed in detail. Its properties make it a suitable choice for data authentication.

Salim Hariri – One of the best experts on this subject based on the ideXlab platform.

  • ICCAC – DNS-IDS: Securing DNS in the Cloud Era
    2015 International Conference on Cloud and Autonomic Computing, 2015
    Co-Authors: Pratik Satam, Hamid Alipour, Youssif Al-nashif, Salim Hariri

    Abstract:

    Recently, there has been a rapid growth in cloud computing due to their ability to offer computing and storage on demand, its elasticity, and significant reduction in operational costs. However, cloud security is a grand obstacle for full deployment and utilization of cloud services. In this paper, we address the security of the DNS protocol that is widely used to translate the cloud domain names to correct IP addresses. The DNS protocol is prone to Attacks like cache poisoning Attacks and DNS hijacking Attacks that can lead to compromising user’s cloud accounts and stored information. We present an anomaly based Intrusion Detection System (IDS) for the DNS protocol (DNS-IDS) that models the normal operations of the DNS protocol and accurately detects any abnormal behavior or exploitation of the protocol. The DNS-IDS system operates in two phases, the training phase and the operational phase. In the training phase, we model the normal behavior of the DNS protocol as a finite state machine and we derive the normal temporal statistics of how normal DNS traffic transition within that state machine and store them in a database. To bound the normal event space, we also apply few known DNS Attacks (e.g. Cache poisoning) and store the temporal statistics of the abnormal DNS traffic transition in a separate database. Then we develop an anomaly metric for the DNS protocol that is a function of the temporal statistics for both the normal and abnormal transitions of the DNS by applying classification algorithms like the Bagging algorithm. During the operational phase, the anomaly metric is used to detect DNS Attacks (both known and novel Attacks). We have evaluated our approach against a wide range of DNS Attacks (DNS hijacking, Kaminsky Attack, amplification Attack, Birthday Attack, DNS Rebinding Attack). Our results show Attack detection rate of 97% with very low false positive alarm rate (0.01397%), and round 3% false negatives.

  • DNS-IDS: Securing DNS in the Cloud Era
    2015 International Conference on Cloud and Autonomic Computing, 2015
    Co-Authors: Pratik Satam, Hamid Alipour, Youssif Al-nashif, Salim Hariri

    Abstract:

    Recently, there has been a rapid growth in cloud computing due to their ability to offer computing and storage on demand, its elasticity, and significant reduction in operational costs. However, cloud security is a grand obstacle for full deployment and utilization of cloud services. In this paper, we address the security of the DNS protocol that is widely used to translate the cloud domain names to correct IP addresses. The DNS protocol is prone to Attacks like cache poisoning Attacks and DNS hijacking Attacks that can lead to compromising user’s cloud accounts and stored information. We present an anomaly based Intrusion Detection System (IDS) for the DNS protocol (DNS-IDS) that models the normal operations of the DNS protocol and accurately detects any abnormal behavior or exploitation of the protocol. The DNS-IDS system operates in two phases, the training phase and the operational phase. In the training phase, we model the normal behavior of the DNS protocol as a finite state machine and we derive the normal temporal statistics of how normal DNS traffic transition within that state machine and store them in a database. To bound the normal event space, we also apply few known DNS Attacks (e.g. Cache poisoning) and store the temporal statistics of the abnormal DNS traffic transition in a separate database. Then we develop an anomaly metric for the DNS protocol that is a function of the temporal statistics for both the normal and abnormal transitions of the DNS by applying classification algorithms like the Bagging algorithm. During the operational phase, the anomaly metric is used to detect DNS Attacks (both known and novel Attacks). We have evaluated our approach against a wide range of DNS Attacks (DNS hijacking, Kaminsky Attack, amplification Attack, Birthday Attack, DNS Rebinding Attack). Our results show Attack detection rate of 97% with very low false positive alarm rate (0.01397%), and round 3% false negatives.