177 research outputs found
BaseSAFE: Baseband SAnitized Fuzzing through Emulation
Rogue base stations are an effective attack vector. Cellular basebands
represent a critical part of the smartphone's security: they parse large
amounts of data even before authentication. They can, therefore, grant an
attacker a very stealthy way to gather information about calls placed and even
to escalate to the main operating system, over-the-air. In this paper, we
discuss a novel cellular fuzzing framework that aims to help security
researchers find critical bugs in cellular basebands and similar embedded
systems. BaseSAFE allows partial rehosting of cellular basebands for fast
instrumented fuzzing off-device, even for closed-source firmware blobs.
BaseSAFE's sanitizing drop-in allocator, enables spotting heap-based
buffer-overflows quickly. Using our proof-of-concept harness, we fuzzed various
parsers of the Nucleus RTOS-based MediaTek cellular baseband that are
accessible from rogue base stations. The emulator instrumentation is highly
optimized, reaching hundreds of executions per second on each core for our
complex test case, around 15k test-cases per second in total. Furthermore, we
discuss attack vectors for baseband modems. To the best of our knowledge, this
is the first use of emulation-based fuzzing for security testing of commercial
cellular basebands. Most of the tooling and approaches of BaseSAFE are also
applicable for other low-level kernels and firmware. Using BaseSAFE, we were
able to find memory corruptions including heap out-of-bounds writes using our
proof-of-concept fuzzing harness in the MediaTek cellular baseband. BaseSAFE,
the harness, and a large collection of LTE signaling message test cases will be
released open-source upon publication of this paper
An Experimental mmWave Channel Model for UAV-to-UAV Communications
Unmanned Aerial Vehicle (UAV) networks can provide a resilient communication
infrastructure to enhance terrestrial networks in case of traffic spikes or
disaster scenarios. However, to be able to do so, they need to be based on
high-bandwidth wireless technologies for both radio access and backhaul. With
this respect, the millimeter wave (mmWave) spectrum represents an enticing
solution, since it provides large chunks of untapped spectrum that can enable
ultra-high data-rates for aerial platforms. Aerial mmWave channels, however,
experience characteristics that are significantly different from terrestrial
deployments in the same frequency bands. As of today, mmWave aerial channels
have not been extensively studied and modeled. Specifically, the combination of
UAV micro-mobility (because of imprecisions in the control loop, and external
factors including wind) and the highly directional mmWave transmissions require
ad hoc models to accurately capture the performance of UAV deployments. To fill
this gap, we propose an empirical propagation loss model for UAV-to-UAV
communications at 60 GHz, based on an extensive aerial measurement campaign
conducted with the Facebook Terragraph channel sounders. We compare it with
3GPP channel models and make the measurement dataset publicly available.Comment: 7 pages, 7 figures, 3 tables. Please cite it as M. Polese, L.
Bertizzolo, L. Bonati, A. Gosain, T. Melodia, An Experimental mmWave Channel
Model for UAV-to-UAV Communications, in Proc. of ACM Workshop on
Millimeter-Wave Networks and Sensing Systems (mmNets), London, UK, Sept. 202
Double connective tissue graft to treat deep coronal-radicular abrasion: A 19-year follow-up case report
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170298/1/cap10176.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170298/2/cap10176_am.pd
Use of gibberellin in floriculture
This review aimed to show the use of gibberellin in floriculture. In this context, it should be noted that the benefit of the activity of the gibberellins has brought major advances in the field of physiology. Its use is one of the most important tools for the development of agriculture. Thus, the study concluded that the use of gibberellins has been increasingly used by producers and is also a vast important subject that may help in increasing the production of flowers if the farms are dedicated to this purpose.Keywords: Regulators plants, flowers, phenotypic characteristics, postharves
Architecture of Class 1, 2, and 3 Integrons from Gram Negative Bacteria Recovered among Fruits and Vegetables
The spread of antibiotic resistant bacteria throughout the food chain constitutes a public health concern. To understand the contribution of fresh produce in shaping antibiotic resistance bacteria and integron prevalence in the food chain, 333 antibiotic resistance Gram negative isolates were collected from organic and conventionally produced fruits (pears, apples, and strawberries) and vegetables (lettuces, tomatoes, and carrots). Although low levels of resistance have been detected, the bacterial genera identified in the assessed fresh produce are often described not only as environmental, but mostly as commensals and opportunistic pathogens. The genomic characterization of integron-harboring isolates revealed a high number of mobile genetic elements and clinically relevant antibiotic resistance genes, of which we highlight the presence of as mcr-1, qnrA1, blaGES−11, mphA, and oqxAB. The study of class 1 (n = 8), class 2 (n = 3) and class 3 (n = 1) integrons, harbored by species such as Morganella morganii, Escherichia coli, Klebsiella pneumoniae, led to the identification of different integron promoters (PcW, PcH1, PcS, and PcWTNG−10) and cassette arrays (containing drfA, aadA, cmlA, estX, sat, and blaGES). In fact, the diverse integron backbones were associated with transposable elements (e.g., Tn402, Tn7, ISCR1, Tn2 ∗ , IS26, IS1326, and IS3) that conferred greater mobility. This is also the first appearance of In1258, In1259, and In3-13, which should be monitored to prevent their establishment as successfully dispersed mobile resistance integrons. These results underscore the growing concern about the dissemination of acquired resistance genes by mobile elements in the food chain
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