DOS | EPFL Graph Search

DOS (dɒs, dɔːs) is a family of disk-based operating systems for IBM PC compatible computers. The DOS family primarily consists of Microsoft's MS-DOS and a rebranded version, IBM PC DOS, both of which were introduced in 1981. Later compatible systems from other manufacturers include DR-DOS (1988), ROM-DOS (1989), PTS-DOS (1993), and FreeDOS (1998). MS-DOS dominated the IBM PC compatible market between 1981 and 1995. Although the name has come to be identified specifically with this particular family of operating systems, DOS is a platform-independent acronym for disk operating system, whose use predates the IBM PC. Dozens of other operating systems also use the acronym, beginning with the mainframe DOS/360 from 1966. Others include Apple DOS, Apple ProDOS, Atari DOS, Commodore DOS, TRSDOS, and AmigaDOS. History Origins IBM PC DOS (and the separately sold MS-DOS) and its predecessor, 86-DOS, ran on Intel 8086 16-bit processors. It was developed to be

Denial-of-service resilience in peer-to-peer file sharing systems

Dan Mihai Dumitriu, Willy Zwaenepoel

Peer-to-peer (p2p) file sharing systems are characterized by highly replicated content distributed among nodes with enormous aggregate resources for storage and communication. These properties alone are not sufficient, however, to render p2p networks immune to denial-of-service (DoS) attack. In this paper, we study, by means of analytical modeling and simulation, the resilience of p2p file sharing systems against DoS attacks, in which malicious nodes respond to queries with erroneous responses. We consider the filetargeted attacks in current use in the Internet, and we introduce a new class of p2p-network-targeted attacks. In file-targeted attacks, the attacker puts a large number of corrupted versions of a single file on the network. We demonstrate that the effectiveness of these attacks is highly dependent on the clients’ behavior. For the attacks to succeed over the long term, clients must be unwilling to share files, slow in removing corrupted files from their machines, and quick to give up downloading when the system is under attack. In network-targeted attacks, attackers respond to queries for any file with erroneous information. Our results indicate that these attacks are highly scalable: increasing the number of malicious nodes yields a hyperexponential decrease in system goodput, and a moderate number of attackers suffices to cause a near-collapse of the entire system. The key factors inducing this vulnerability are (i) hierarchical topologies with misbehaving “supernodes,” (ii) high path-length networks in which attackers have increased opportunity to falsify control information, and (iii) power-law networks in which attackers insert themselves into high-degree points in the graph. Finally, we consider the effects of client counter-strategies such as randomized reply selection, redundant and parallel download, and reputation systems. Some counter-strategies (e.g., randomized reply selection) provide considerable immunity to attack (reducing the scaling from hyperexponential to linear), yet significantly hurt performance in the absence of an attack. Other counter-strategies yield little benefit (or penalty). In particular, reputation systems show little impact unless they operate with near perfection.

2005

Learning the electronic density of states in condensed matter

Andrea Anelli, Chiheb Ben Mahmoud, Michele Ceriotti

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation of experimentally observable material properties such as optical absorption and electrical conductivity. We discuss the challenges inherent in the construction of a machine-learning (ML) framework aimed at predicting the DOS as a combination of local contributions that depend in turn on the geometric configuration of neighbors around each atom, using quasiparticle energy levels from density functional theory as training data. We present a challenging case study that includes configurations of silicon spanning a broad set of thermodynamic conditions, ranging from bulk structures to clusters and from semiconducting to metallic behavior. We compare different approaches to represent the DOS, and the accuracy of predicting quantities such as the Fermi level, the electron density at the Fermi level, or the band energy, either directly or as a side product of the evaluation of the DOS. We find that the performance of the model depends crucially on the resolution chosen to smooth the DOS and that there is a tradeoff to be made between the systematic error associated with the smoothing and the error in the ML model for a specific structure. We find however that the errors are not strongly correlated among similar structures, and so the average DOS over an ensemble of configurations is in very good agreement with the reference electronic structure calculations, despite the large nominal error on individual configurations. We demonstrate the usefulness of this approach by computing the density of states of a large amorphous silicon sample, for which it would be prohibitively expensive to compute the DOS by direct electronic structure calculations and show how the atom-centered decomposition of the DOS that is obtained through our model can be used to extract physical insights into the connections between structural and electronic features.

AMER PHYSICAL SOC2020

Denial-of-service resilience in peer-to-peer file sharing systems

Dan Mihai Dumitriu, Willy Zwaenepoel

2005

Learning the electronic density of states in condensed matter

Andrea Anelli, Chiheb Ben Mahmoud, Michele Ceriotti

AMER PHYSICAL SOC2020