sadat2307/MSciNLI huggingface dataset:
Domain: Security and privacy
Sentence 1: More details on Mebrootcanbefoundin[9,12,25].Inthispaper, wewillfocus onTorpig, introducing Mebrootonlywhen necessaryto understand Torpig s behavior.
Sentence 2: hereinafter, we present the life cy­cle ofTorpig and the organization of theTorpig botnet, as we ob­servedit during the courseof our analysis.We will use Figure1as a reference.
Relationship: entailment
Domain: Security and privacy
Sentence 1: However this is mitigated as the considered encryption scheme are generally imple­mented in a few instructions (around 10)  we implement a simpli.cation procedure on the com­puted set of invariants: if fi entails fj (for i .= j), then we can safely delete fi from the set of asser­tions {f1,...,fn}.
Sentence 2: we keep only the minimal preconditions with respect to strength in our computed set of invariants (the usual Hoare logic cor­responds to the degenerated case where this set has a minimum element, called the weakest precondition).
Relationship: entailment
Domain: Security and privacy
Sentence 1: For this reason, we decided to focus our effort on those MSI .elds that were either more interesting from an attacker s point of view, or had clear constraints set by the vendors.
Sentence 2: here we present the results we obtained by fuzzing the vector .eld of a compatibility format MSI data component and the don t care .eld of a remappable format MSI address compo­nent.
Relationship: entailment
Domain: Security and privacy
Sentence 1: It is apparent that it may be impossible to .nd an assign­ment of authorized users to work.ow steps such that all con­straints are satis.ed.
Sentence 2: in this case, we say that the work.ow speci.cation is unsatis.able.The Workflow Satisfiabil­ity Problem (WSP) is known to be NP-hard, even when the set of constraints only includes constraints that have a relatively simple structure (and that would arise regularly in practice).1 1the Graph k-Colorability problem can be reduced to a special case of WSP in which the work.ow spec­i.cation only includes separation-of-duty constraints [21].
Relationship: entailment
Domain: Security and privacy
Sentence 1: These sequences can be easily found on the x86 architecture due to variable-length instructions and unaligned memory access.
Sentence 2: consider for instance the following x86 code with the given intended instruction sequence: If the interpretation of the byte stream starts two bytes later, the following unintended instruction sequence would be executed by an Intel x86 processor: 00 00 add %al ,(% eax ) 00 e9 add %ch ,% c l c3 r e t solutions only securing returns in function epi­logues are not able to detect ROP attacks that are based on these sequences.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Suppose a set of (at most) t users T .
Sentence 2: {1,...,n}colludes to build a pirate decoder D. Then the tracing algo­rithm Trace takes a tracing key tk as input, interacts with D and outputs the identity of at least one colluder (also traitor) in T .Ø TraceD(tk) .
Relationship: entailment
Domain: Security and privacy
Sentence 1: The channel attenuation and 100 50 0 Transmitter-Receiver Separation (m) Interference Range vs. Transmitter-Receiver separation creases, approximating the interference range as a multiple of the transmitter-receiver separation, is no longer accurate.
Sentence 2: for very weak links (i.e., when the receiver can just barely decode the signal from the transmitter), the interference range can be arbitrarily high.
Relationship: entailment
Domain: Security and privacy
Sentence 1: EXI S T I N G MO D E LS F O R SEC U RE CH ANN EL ESTA B L ISH M EN T. When studying the combined properties of a key establishment and a secure channel protocol, one usually considers a security game where the execution model and the attack interface for key ex­change and secure channels are glued together; and the adver­sary tries to break the secure channel using additional information that possibly leaked during the key-exchange phase.
Sentence 2: such a model also allows to analyze protocols that do not clearly separate the two components.
Relationship: entailment
Domain: Security and privacy
Sentence 1: However, what we argue in this paper is that un­less exponentially large l is used, the IND-sID-CCA of Chen et al s scheme can be broken by the polynomial-time adver­sary, which invalidates the security result (proof) presented in [9].
Sentence 2: (the recommended size 512 for l is not su.cient to prevent the attack but if l is exponentially large, the scheme can not be used in practice.)
Relationship: entailment
Domain: Security and privacy
Sentence 1: An atomic location is de.ned to be a rectangle, which is de.ned by the co-ordinates of its lower-left and upper­right corners.
Sentence 2: 6 a rectangle is a pair [l, r], where l, r .S.A location is the union of one or more disjoint atomic locations: clearly, the set of locations is a subset of 2S and .
Relationship: entailment
Domain: Security and privacy
Sentence 1: To have a thorough understanding of the philosophies behind malware design, we have spent considerable amount of time analyzing the behaviors of malware programs.
Sentence 2: since 2008, we have read, recorded and analyzed the technical details of 2,600 malware samples of a wide range of formats and varieties, such as viruses, worms, backdoors, rootkits, and Trojan horses.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 7.2 Comparisons of Different Classi.cationMethods In this set of experiments, we compare our IFSS with di.erent ensemble methods obtained using di.erent com­binations of feature representations, training sets and base classi.ers.
Sentence 2: we use: (1) API calls and inter­pretable strings as diverse features, (2) DB T 1 consisting of 491,733 PE .le samples obtained from the history data set of Kingsoft Anti-virus lab, and DB T 2 containing 530,448 PE .les which is the data collection of the week from Jan. 1st, 2009 to Jan. 7th, 2009, (3) associative classi.er de­scribed in Section 5.1 and linear SVM [14] implemented in LibLinear package as heterogenous base classi.ers, to con­struct di.erent classi.ers.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Beyond graph, G (similar to Figure 2d), which may be constructed remembering the previous n - 1 characters of context, a as the cross product of G1 =(V1,E1) and G2 =(V2,E2).
Sentence 2: state can remember whether the previous context includes G =(V1 × V2,E), where E contains the labeled a  tag that has not yet been closed with .
Relationship: entailment
Domain: Security and privacy
Sentence 1: For example, the application that authorizes a $10 purchase from Amazon is probably not the same one we want to do our banking with, and neither would be suited to sending encrypted messages to our mistresses.
Sentence 2: it is important that the user be able to clearly under­stand what he is doing when using the Nebuchadnezzar, and this is likely to require a diversity of user interfaces.
Relationship: entailment
Domain: Security and privacy
Sentence 1: CA1 RegReq 5.80 1.72 0.18 1.61 UpdateReq 5.90 1.69 0.19 1.71 CCReq 2.39 - 0.22 2.51 CA2 RegReq 5.97 1.70 0.24 1.37 UpdateReq 5.72 1.93 0.24 1.44 CCReq 2.40 - 0.25 1.28 ILS1 RegReq 10.09 1.38 0.37 1.72 UpdateReq 9.32 1.40 0.36 1.90 CCReq 5.68 - 0.15 1.24 request to the time that CA1 sends the response (which went through all required entities), without considering network latency.
Sentence 2: we measure the total processing time spent by the entities involved.
Relationship: entailment
Domain: Security and privacy
Sentence 1: More generally, tm/n is i th input nondeducible despite J, for a set J of size k, if it can be guaranteed that 1 + k < m < n - k. If k = 1, then the smallest m and n to satisfy the above requirement are respectively 3 and 5.
Sentence 2: for t3/5, if the adversary knows the output as well as one of the inputs, it still cannot infer any of the other 4 inputs.
Relationship: entailment
Domain: Security and privacy
Sentence 1: There are many possible video-password schemes that belong to this class, which use di.erent types of information (e.g., timestamps, text, click-points, and drawings) as part of a user s authen­tication secret.
Sentence 2: a particular video-password scheme (e.g., Click-Based Video-Passwords) requires a user u to remember some speci.c types of information (e.g., times­tamps and click-points) cued by video vu.In this Section, we .rst review these types of information, and then use these types to de.ne video-passwords.
Relationship: entailment
Domain: Security and privacy
Sentence 1: For the role mapping problem, we propose three different algorithms to deal with different scenarios.
Sentence 2: if an exactly matched role set exists for a user s request PRQ, we extend a simple algorithm proposed in the literature [3] to find it; and if such an exactly matching role set doesn t exist, we propose two new algorithms to find a sub-optimal role set based on the availability or least privilege needs.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In Figure 3 we show the false positive and false negative rates for each user on the testing data without a smoothing window.
Sentence 2: for a particular user x, the false positive rate is the percentage of user x s data points in the testing data that are labeled as intruder and the false negative rate is the percentage of the intruder data points labeled as x (the valid user).
Relationship: entailment
Domain: Security and privacy
Sentence 1: 6.4 Non-Slanderability In this paper, we assume the existence of a distributed trust mechanism, e.g., one of the protocols proposed in [22, 20, 35].
Sentence 2: together with the traceability property provided by SDT, the maximum number of claims that the adversary group can send against the same good user for the same reason is the number of adversaries in that group, which is assumed to be less than t. adversaries cannot collect enough number of claims, i.e.
Relationship: entailment
Domain: Security and privacy
Sentence 1: By tagged TLS, we mean the generic TLS ciphersuite from Figure 2, without any of the SCSV/RIE extensions shown in green, but where an arbitrary string can be placed in the extC and extS .elds.
Sentence 2: it is a normal TLS ciphersuite, but with an arbitrary extension .eld that just carries strings that are not being interpreted as having any particular meaning.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Finally, auth isaMAC of(ids, count, type) using a key known to the trusted third party only.
Sentence 2: we may use a marking technique that sets one HTML or HTML/SSL cookie, one cache cookie and one browser history cookie for each IP address8 used by the trusted third party, in addition to one HTML cookie, one cache cookie and one browser his­tory cookie associated with the domain of the trusted third party.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Despite its recent introduction, the relative leakage model has already attracted considerable attention [1, 30, 3, 25, 2, 11], due to its elegance and generality.
Sentence 2: one nat­ural application where protection against memory attacks is very relevant [1, 2] is the setting of identity-based encryp­tion (IBE) [34].
Relationship: entailment
Domain: Security and privacy
Sentence 1: As such, rather than randomly selecting the nodes whose probability have to be increased, we de­cided to select them in a particular area, to check if UAR shows this anomaly.
Sentence 2: we select 20 nodes among those with distance 5 to node v.As expected, the UAR mea­sure detects these nodes, as con.rmed by the jump between trends in Figure 6.
Relationship: entailment
Domain: Security and privacy
Sentence 1: This latter option allows for a great deal of .exibility and can be implemented by leverag­ing the new instrumentation package introduced in Java 5 (http: //java.sun.com/j2se/1.5.0/).
Sentence 2: unfortunately, the current implementation of the Java 5 instru­mentation package is still incomplete and does not yet provide some key features needed by WA S P. it does not allow for clearing the final .ag in the string library classes, which pre­vents the MetaStrings library from extending them.
Relationship: entailment
Domain: Security and privacy
Sentence 1: An outgoing .ow oj is a matched schedule for fi if and only if Nfi (t') -Nfi (t) =N(t' +.)
Sentence 2: -N(t) (1) oj oj ' for any time interval of [t,t],t' =t,where Nfi (t) is the total number of packets arrived at .ow fi until time t. ' the number of packets arrived during [t,t] at .ow fi must be smaller than or equal to the number of ' ' packets sent by .ow oj during [t,t+ .
Relationship: entailment
Domain: Security and privacy
Sentence 1: [1,l], T rapdoorKai = Qi.That is, Mallory wishes to uniquely identify the under­lying keywords of each of the queries Qi.In this paper, we show that Mallory has a very high probability of succeeding if she has access to the following background knowledge on the document set.
Sentence 2: mallory knows the underlying keywords for k of the queries in the sequence Q.Mallory has ac­cess to the set KQ .KB ×Q,where KQ is de.ned as {(x, y)|(x .KB ) .
Relationship: entailment
Domain: Security and privacy
Sentence 1: Unfortunately, the decidability of most DLs stems, in part, from a severe restriction on the types of relations that can be expressed between individuals in the domain of discourse.
Sentence 2: 2http://ksl.stanford.edu/projects/NIMD/Kani-dl-v1.owl 3http://www.w3.org/TR/owl-time/ most DLs, including SHIN, exhibit some form of tree model property, i.e., every satis.able KB has a tree shape model.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Unmapped page (64k) code region (cs) Unmapped (64k) data region (ds) Trampolines (64k) | Unmapped (64k) | ld.so code (LC) | Unmapped (LC) | Unmapped (LD) | ld.so data (LD) | Application code (C0) | Unmapped (C0) | Unmapped (D0) | Application data (D0) | Library 1 code (C1) | Unmapped (C1) | Unmapped (D1) | Library 1 data (D1) | Library 2 code (C2) | Unmapped (C2) | Unmapped (D2) | Library 2 data (D2) | Unmapped gap for expansion | Unmapped gap for expansion | | Heap | | Unmapped gap | end of cs Stack end of ds Figure 4: A layout of the NaCl address space for dynamic linking/loading.
Sentence 2: pIC code is oblivious to how hardware segments are con.gured.
Relationship: entailment
Domain: Security and privacy
Sentence 1: [25] focus on HTTP-based malware with the objective to automatically generate network signatures for malware.
Sentence 2: they use single-linkage clustering over three stages, mainly to reduce computational complexity: .rst, a coarse-grained clustering is performed; each of the corresponding clusters is then subdivided into a more .ne-grained set of clusters; and, eventually, similar clusters are merged together to avoid redundant signature generation.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In this paper, we evaluate the potential for and impact of the large scale infection and coordination of mobile phones in attacks against the core of a cellular network.
Sentence 2: we character­ ize Denial of Service (DoS) attacks using selected service request types on the Home Location Register (HLR), the central reposi­ tory of user location and pro.le information in the network, by a botnet composed entirely of mobile phones.
Relationship: entailment
Domain: Security and privacy
Sentence 1: A variable x .ows from a label s P or lower in a substitution environment s, written x T P,if x/µ@P ' .
Sentence 2: s for some µ and P ' such that either P ' P,or µ s isa variable and (inductively) µ T P. x .ows from a label P or lower if x is an instance of a value substituted at P or lower.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Speci.cally, we get the following result, answering Question 1 above in the af.rmative: For any constant rate p1there is a constant >1 such that for every kand suf.ciently large nthere is an (n,N,k,m)multiset batch code with n/N?pand mO(k).
Sentence 2: one can insist on adding only an arbitrarily small percentage to the original storage, yet reduce the load by any de­sired amount kusing only poly(k)devices.
Relationship: entailment
Domain: Security and privacy
Sentence 1: If passwords were randomly chosen, the weakest Amazon passwords would come into range after about of 220 106 attempts, and UsaJobs passwords after 252 4.5×1015 attempts.
Sentence 2: there are nine orders of magnitude di.erence between how hard it is to brute­force an Amazon password and a UsaJobs.gov one.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Service matching In the cases where the ssh-based iden­ti.cation failed, we attempted to compare the banners cap­tured by NMap with the information extracted from the ser­vices installed on the AMIs.
Sentence 2: we compared the service name, the service version, and (optionally) the ad­ditional information .elds returned by the thirty common ports we scanned in our experiment.
Relationship: entailment
Domain: Security and privacy
Sentence 1: and, -given d and the neighborhood connectivity constraints imposed by wireless communication (e.g., the number ' of nodes nin a neighborhood), what values should the key ring size, k,and pool, P ,have for anetwork of size n?
Sentence 2: if memory capacity of each sensor limits the key ring size to a given value of k, what should the size of the key pool, P ,be?
Relationship: entailment
Domain: Security and privacy
Sentence 1: In this paper, we address this gap by analyzing these regular expres­sions and developing memory-efficient DFA-based solutions for high speed processing.
Sentence 2: we make the following con­tributions:  We analyze the computational and storage cost of building individual DFAs for matching regular expressions, and iden­tify the structural characteristics of the regular expressions in networking applications that lead to exponential growth of DFAs, as presented in Section 3.2.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In fact, our main construction in Section 4 also will not satisfy this property the very moment corrupt = false, but will only make S pseudorandom when the .rst call to next is made (which is the only thing that matters at the end).
Sentence 2: looking at the analysis of [1], the (truncated) PRG G ' inside the refresh procedure is only needed to en­sure the state pseudorandomness of their construction.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Table 1: An overview of existing platform-level ex­ploits in Android     Figure 1: The RiskRanker architecture potential security risks into corresponding detection mod­ules of two orders of complexity.
Sentence 2: we analyze each app from an Android market with a set of analysis modules designed to detect behaviors that we classify as high-or medium-risk: The .rst-order modules handle non­obfuscated apps by evaluating the risks in a straightforward manner; The second-order modules capture certain behav­iors (e.g., encryption and dynamic code loading) that are in themselves not of concern, but that in conjunction with others may form malicious patterns and be instrumental to detect stealthy malware.
Relationship: entailment
Domain: Security and privacy
Sentence 1: However, devel­oping mechanisms intelligent enough to respond to a more dynamic attack remains challenging, especially if adversaries deliberately attempt to target nodes other than the HLR.
Sentence 2: because a signi.cant amount of context is lost as messages move between mobile devices and the HLR (e.g., granularity of location), it will likely become dif.cult for a provider to separate attacks from other traf.c.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In the current state of the art, however, perfect soundness results in poor scalabil­ity.
Sentence 2: since MOPS is designed to be a practical tool for check­ing safety properties on large programs, it tries to strike a 3the tool is sound for the theorem the pro­gram satis.es the safety property  irq_DISABLE() Yield()  irq_ENABLE() (a) An FSA describing this property.
Relationship: entailment
Domain: Security and privacy
Sentence 1: However, in the .nal matching function f (found by apply­ing the Hungarian algorithm on the cost matrix), these two neighbor nodes can be unrelated, in which case, the true edit cost between i and j is higher than the estimate.
Sentence 2: because the cost matrix is predetermined, the algorithm will only be able to consider the local structure of the nodes without any information about the matching.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 10 shows the set sizes of the mincuts and the selected bottlenecks for chosen ratios bC of 0.7 and 0.5 for varying sizes of D. The plots for the three countries show that |M(S, D)| is one to two orders of magnitude larger than |B(S,D)(bC )| in the entire range of measured |D| and bC .
Sentence 2: the attack against the selected bottlenecks requires a much lower adversary s .ooding capability than for a mincut while achieving substantial connectivity degra­dation; e.g., 70%.
Relationship: entailment
Domain: Security and privacy
Sentence 1: PPGCT.Finally, securityofthePPGCTprotocol(Sec.4.3) against semi-honest adversaries relies on that of the underlying PSI pro­tocol, to which it is immediately reducible.
Sentence 2: (a semi-honest participant with a non-negligible advantage in distin­guishing between real and simulated executions of PPGCT would have the same advantage in distinguishing between real and sim­ulated executions of PSI.)
Relationship: entailment
Domain: Security and privacy
Sentence 1: Thus, R = u Ru,where R is the outcome of the transformation function (i.e., the observable trace).
Sentence 2: if two actual events ei and ej are associated with the same user (i.e., ei ~ ej)and ei = trns(ei)and ej = trns(ej), then we de.ne a relation ~o between ei and ej.we de.ne ei ~o e j if ei,e j .RIu for some u .U.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Hence B solves A the BDHP with probability: -1 -1 (qC (qC + 1))qC (1 - qC (qC + 1))ß qC ß =( )qC ( ) qC + 1 qC + 1 1 -qC ( ß = (1 + )) qC qC + 1 ß = e(qC + 1) where e is the base for natural logarithm.
Sentence 2: b solved the BDHP with non-negligible probability, which contradicts with the BDHP assumption.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Because of the notion of "rendering", correctness in our protocol does not guarantee robustness in the traditional sense.
Sentence 2: dishonest users may try to alter their inputs based on those of honest users, or dishonest users and servers may collude to change input or output values in the middle of a mix network execution based on input val- ues and intermediate transcripts.
Relationship: entailment
Domain: Security and privacy
Sentence 1: CONFIDENTIAL INDEXING Our solution for producing privacy-aware indexes builds on the observations made in the previous section character­izing vulnerabilities from inferences.
Sentence 2: to block such inferences, we require that di.erent occurrences of a same plaintext value be mapped to di.erent index values whenever such occurrences are subject to di.erent autho­rizations.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Our strategic objective is to create a new intrinsic passive metering technique and the .rst IC auditing technique us­ing a combination of statistical, majorization, and IC char­acterization techniques.
Sentence 2: we propose a new intrinsic passive metering scheme based on physical level IC characteristics, such as threshold voltage (Vth) and e.ective Copyright 2011 ACM 978-1-4503-1005-5/11/10 ...$10.00.
Relationship: entailment
Domain: Security and privacy
Sentence 1: An OSBE scheme enables the sender to send a message with the as- surance that it can be seen only by the receiver if it has appropriate certificates while at the same time protecting the receiver's privacy such that the sender does not know whether the receiver has the re- quired certificates or not.
Sentence 2: oSBE performs access control on a message in an oblivious (or privacy preserving) fash- ion.
Relationship: entailment