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  1. Article ; Online: Min-entropy estimation for semiconductor superlattice true random number generators

    Jing Liu / Jianguo Xie / Lu Chao / Han Wu / Peng Ding / Xiaoming Chen / Huamin Feng

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    2022  Volume 9

    Abstract: Abstract Semiconductor superlattice true random number generator (SSL-TRNG) has an outstanding practical property on high-throughput and high-security cryptographic applications. Security in random number generators is closely related to the min-entropy ... ...

    Abstract Abstract Semiconductor superlattice true random number generator (SSL-TRNG) has an outstanding practical property on high-throughput and high-security cryptographic applications. Security in random number generators is closely related to the min-entropy of the raw output because feeding cryptographic applications with insufficient entropy leads to poor security and vulnerability to malicious attacks. However, no research has focused on the min-entropy estimation based on the stochastic model for SSL-TRNG, which is a highly recommended method for evaluating the security of a specific TRNG structure. A min-entropy estimation method is proposed in this paper for the SSL-TRNG by extending the Markov stochastic model derived from the memory effects. By calculating the boundary of the transition matrix, the min-entropy result is the average value of each sample (1 bit) is 0.2487. Moreover, the experimental results show that the estimator is accurate enough to adjust compression rate dynamically in post-processing to reach the required security level, estimating entropy on the fly rather than off-line.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: TIM

    Yizhe You / Jun Jiang / Zhengwei Jiang / Peian Yang / Baoxu Liu / Huamin Feng / Xuren Wang / Ning Li

    Cybersecurity, Vol 5, Iss 1, Pp 1-

    threat context-enhanced TTP intelligence mining on unstructured threat data

    2022  Volume 17

    Abstract: Abstract TTPs (Tactics, Techniques, and Procedures), which represent an attacker’s goals and methods, are the long period and essential feature of the attacker. Defenders can use TTP intelligence to perform the penetration test and compensate for defense ...

    Abstract Abstract TTPs (Tactics, Techniques, and Procedures), which represent an attacker’s goals and methods, are the long period and essential feature of the attacker. Defenders can use TTP intelligence to perform the penetration test and compensate for defense deficiency. However, most TTP intelligence is described in unstructured threat data, such as APT analysis reports. Manually converting natural language TTPs descriptions to standard TTP names, such as ATT&CK TTP names and IDs, is time-consuming and requires deep expertise. In this paper, we define the TTP classification task as a sentence classification task. We annotate a new sentence-level TTP dataset with 6 categories and 6061 TTP descriptions from 10761 security analysis reports. We construct a threat context-enhanced TTP intelligence mining (TIM) framework to mine TTP intelligence from unstructured threat data. The TIM framework uses TCENet (Threat Context Enhanced Network) to find and classify TTP descriptions, which we define as three continuous sentences, from textual data. Meanwhile, we use the element features of TTP in the descriptions to enhance the TTPs classification accuracy of TCENet. The evaluation result shows that the average classification accuracy of our proposed method on the 6 TTP categories reaches 0.941. The evaluation results also show that adding TTP element features can improve our classification accuracy compared to using only text features. TCENet also achieved the best results compared to the previous document-level TTP classification works and other popular text classification methods, even in the case of few-shot training samples. Finally, the TIM framework organizes TTP descriptions and TTP elements into STIX 2.1 format as final TTP intelligence for sharing the long-period and essential attack behavior characteristics of attackers. In addition, we transform TTP intelligence into sigma detection rules for attack behavior detection. Such TTP intelligence and rules can help defenders deploy long-term effective threat detection ...
    Keywords TTPs ; Threat intelligence ; Natural language processing (NLP) ; Advanced persistent threat (APT) ; Computer engineering. Computer hardware ; TK7885-7895 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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