@inproceedings{xu_hybrid_2014,
	title = {Hybrid side-channel/machine-learning attacks on {PUFs}: {A} new threat?},
	shorttitle = {Hybrid side-channel/machine-learning attacks on {PUFs}},
	doi = {10.7873/DATE.2014.362},
	abstract = {Machine Learning (ML) is a well-studied strategy in modeling Physical Unclonable Functions (PUFs) but reaches its limits while applied on instances of high complexity. To address this issue, side-channel attacks have recently been combined with modeling techniques to make attacks more efficient [25][26]. In this work, we present an overview and survey of these so-called “hybrid modeling and side-channel attacks” on PUFs, as well as of classical side channel techniques for PUFs. A taxonomy is proposed based on the characteristics of different side-channel attacks. The practical reach of some published side-channel attacks is discussed. Both challenges and opportunities for PUF attackers are introduced. Countermeasures against some certain side-channel attacks are also analyzed. To better understand the side-channel attacks on PUFs, three different methodologies of implementing side-channel attacks are compared. At the end of this paper, we bring forward some open problems for this research area.},
	booktitle = {Design, {Automation} and {Test} in {Europe} {Conference} and {Exhibition} ({DATE}), 2014},
	author = {Xu, Xiaolin and Burleson, W.},
	month = mar,
	year = {2014},
	pages = {1--6}
}

{"_id":"oWAxXa76n7DFeBCt5","bibbaseid":"xu-burleson-hybridsidechannelmachinelearningattacksonpufsanewthreat-2014","authorIDs":[],"author_short":["Xu, X.","Burleson, W."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Hybrid side-channel/machine-learning attacks on PUFs: A new threat?","shorttitle":"Hybrid side-channel/machine-learning attacks on PUFs","doi":"10.7873/DATE.2014.362","abstract":"Machine Learning (ML) is a well-studied strategy in modeling Physical Unclonable Functions (PUFs) but reaches its limits while applied on instances of high complexity. To address this issue, side-channel attacks have recently been combined with modeling techniques to make attacks more efficient [25][26]. In this work, we present an overview and survey of these so-called “hybrid modeling and side-channel attacks” on PUFs, as well as of classical side channel techniques for PUFs. A taxonomy is proposed based on the characteristics of different side-channel attacks. The practical reach of some published side-channel attacks is discussed. Both challenges and opportunities for PUF attackers are introduced. Countermeasures against some certain side-channel attacks are also analyzed. To better understand the side-channel attacks on PUFs, three different methodologies of implementing side-channel attacks are compared. At the end of this paper, we bring forward some open problems for this research area.","booktitle":"Design, Automation and Test in Europe Conference and Exhibition (DATE), 2014","author":[{"propositions":[],"lastnames":["Xu"],"firstnames":["Xiaolin"],"suffixes":[]},{"propositions":[],"lastnames":["Burleson"],"firstnames":["W."],"suffixes":[]}],"month":"March","year":"2014","pages":"1–6","bibtex":"@inproceedings{xu_hybrid_2014,\n\ttitle = {Hybrid side-channel/machine-learning attacks on {PUFs}: {A} new threat?},\n\tshorttitle = {Hybrid side-channel/machine-learning attacks on {PUFs}},\n\tdoi = {10.7873/DATE.2014.362},\n\tabstract = {Machine Learning (ML) is a well-studied strategy in modeling Physical Unclonable Functions (PUFs) but reaches its limits while applied on instances of high complexity. To address this issue, side-channel attacks have recently been combined with modeling techniques to make attacks more efficient [25][26]. In this work, we present an overview and survey of these so-called “hybrid modeling and side-channel attacks” on PUFs, as well as of classical side channel techniques for PUFs. A taxonomy is proposed based on the characteristics of different side-channel attacks. The practical reach of some published side-channel attacks is discussed. Both challenges and opportunities for PUF attackers are introduced. Countermeasures against some certain side-channel attacks are also analyzed. To better understand the side-channel attacks on PUFs, three different methodologies of implementing side-channel attacks are compared. At the end of this paper, we bring forward some open problems for this research area.},\n\tbooktitle = {Design, {Automation} and {Test} in {Europe} {Conference} and {Exhibition} ({DATE}), 2014},\n\tauthor = {Xu, Xiaolin and Burleson, W.},\n\tmonth = mar,\n\tyear = {2014},\n\tpages = {1--6}\n}\n\n","author_short":["Xu, X.","Burleson, W."],"key":"xu_hybrid_2014","id":"xu_hybrid_2014","bibbaseid":"xu-burleson-hybridsidechannelmachinelearningattacksonpufsanewthreat-2014","role":"author","urls":{},"downloads":0},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/ky25","creationDate":"2019-05-11T17:47:04.703Z","downloads":0,"keywords":[],"search_terms":["hybrid","side","channel","machine","learning","attacks","pufs","new","threat","xu","burleson"],"title":"Hybrid side-channel/machine-learning attacks on PUFs: A new threat?","year":2014,"dataSources":["XxiQtwZYfozhQmvGR"]}