sadat2307/MSciNLI huggingface dataset:
Domain: Security and privacy
Sentence 1: 5.2 Experimental Identi.cation of relevant State Information We introduced the notion of a pro.le in De.nition 5 ab­stractly as an aggregation of views.
Sentence 2: a pro.le may consist only of the last view as-is, but may also contain accumulated information from a series of consecutive views of a system run.
Relationship: entailment
Domain: Security and privacy
Sentence 1: On the other hand, the security requirements by eCK actually cause the eCK-model less ap­plicable than the traditional CK-framework.
Sentence 2: any DHKE, where the session-key can be derived from the DH-exponents that are speci.ed as the session-states (e.g., IKE [49] and SIGMA [50], STS [32], SSL [35] and TLS [30], and JFK [3] and much more), cannot be proven secure in the eCK-model.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In order to get from the isolated model, in which each consumer of identity in­formation manages this information himself to an identity management which takes the decentralized nature of the In­ternet into account, we argue that consumers of identity information need to be able to assess and distinguish the quality of the information they receive.
Sentence 2: with regard to the launch of electronic ID cards as fostered by several european governments, di.erent sources of identity information will have a di.erent quality in terms of correct­ness and integrity.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Thus, we formulate the problem of emulating the malware sam­ple as constructing an emulator that behaves as the sample ex­pects with respect to the anti-emulation check, but is otherwise unchanged.
Sentence 2: this modi.cation Eel of Ee satis.es f(Eel ,I)= f(Er,I) (the anti-emulator check is fooled), even though g(Eel ,I)= g(Ee,I) (the environment is otherwise the same).
Relationship: entailment
Domain: Security and privacy
Sentence 1: The easiest way to ex­ploit this structure is for the recursive construction of the G structure to stop as soon as its corresponding sub-grid con­sists of cells that all have the same key.
Sentence 2: step 1 of Data Struct Build needs to contain a termination test for when all of the mn cells share the same key (in which case it stops even if n > 2).
Relationship: entailment
Domain: Security and privacy
Sentence 1: In this attack, the attacker intends to construct an attack vector a such that the result­ing estimate x bad = x + c,where c =(c1,c2, ..., cn)T and ci for i .Iv is the speci.c error that the attacker has chosen to inject to xi.
Sentence 2: the attacker wants to replace xi1 , ..., and xir with xi1 + ci1 , ...,and xir + cir , respectively.
Relationship: entailment
Domain: Security and privacy
Sentence 1: However, in the Bell-LaPadula model the criterion is universal the security label of a subject must dominate every part of the security label of an object.
Sentence 2: if we de.ned the secu­rity level of a permission to be R(p), we would require that R(p) R(u), where this order inequality is interpreted as .R(p) ..R(u).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Such collusion increases the average detection error, since honest nodes use all the values provided by bad-mouthing nodes when they compute the indirect trust in sel.sh nodes.
Sentence 2: when the number of bad-mouthing nodes is higher than the number of sel.sh nodes, as shown in Fig­ 0.9 ure 7, the average error decreases when the number of dis­honest nodes increases.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Nevertheless, our .ndings about challenges in IdM process before deployment of the IdM solution corroborate their key .ndings.
Sentence 2: they identi.ed a set of chal­lenges that we also observed in our case study: management of exceptions, getting noti.cation about policy change (em­ployees leave the organization or change their department), getting updated information about who is responsible for a resource, verifying requests, keeping records, and choosing a usable access control management interface.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In response to a command, the counter must produce a veri­.able output message that certi.es the output and the execu­tion of that command.
Sentence 2: if a user invokes a command cmd(t) on the counter at some real time t and then subse­quently receives a corresponding output response message Out(t) from the counter, there must be a veri.cation algo­rithm that the user can follow to check Out(t) and convince himself that the counter has in fact executed cmd(t),and that Out(t) is indeed cmd(t) s correct output.
Relationship: entailment
Domain: Security and privacy
Sentence 1: (int .int) Accordingtothistype, C should provide a proof of (B says Do(o)) .Do(o) The reference monitor can then try to establish Do(o)for itself, in this case by proving B says Do(o).
Sentence 2: the reference monitor may simply assume B says Do(o)if it has authenticated the caller as B.
Relationship: entailment
Domain: Security and privacy
Sentence 1: For example, to measure H(a) would require an aliasing analysis to know what addresses a could have pointed to, and a calculation of the joint entropies between all values that could have been loaded and the taint source.
Sentence 2: equivocations for address dependen­cies occur when there is not perfect entropy in the range of values that could have been loaded or stored, due to limitations on the ad­dress or redundancy in the values in memory.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The mapping between the input CSI samples Cu,s and user pro.le Cu,s is constructed in the form of the kernel func­tion Kernel(·, ·), such as Kernel(Cu,s,Cu,s )=FT (Cu,s)F(Cu,s ).
Sentence 2: we choose a polynomial kernel as the mapping function and the problem in Equation 6 can be expressed as: S Sl 1  max{ as- au(yu,syu,s Kernel(Cu,s,Cu,s ))as } as s=1 2 s=1 s =1 subject to the constraints: (8) S as = 0, asyu,s =0, (9) s=1 where as is the Lagrange multipliers associated with equa­tion 7.
Relationship: entailment
Domain: Security and privacy
Sentence 1: r2 | r1; r2 | r * | f?, where P is an atomic proposition in P , r is a regular expres­sion over the set of actions in A ,and a is an atomic action in A .
Sentence 2: pDL formulas are composed from atomic propo­sitions by applying arbitrary propositional connectives, and modal operators ( r) f and [r]f.Formula ( r) f means that there exists an execution of r (i.e., a sequence of actions con­forming to the regular expression r)reaching a state where f holds; and formula [r]f is intended to mean that all ter­minating executions of r reach a state where f holds.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Not only does this delay detection to Epoch 225 in our case, but also note that whether or not a new worm s .ngerprints ever correspond to the most frequently occuring set depends upon the relative rate with which the worm packets appear during the time window captured by the distribu­tion.
Sentence 2: if the rate of Witty worm packets remains below that of the Slammer worm, detection mechanisms that analyze the most prevalent .ngerprints will not yield good results.
Relationship: entailment
Domain: Security and privacy
Sentence 1: These include parameter and results caching meth­ Communication Path  ods to avoid frequent accesses to backend servers, request redirection and scheduling for load balancing or to di.eren­tiate service levels for di.erent end users [1], and request duplication or partitioning to service complex requests or to attain high levels of reliability [13].
Sentence 2: the actions and communications explicitly carried out by appli­cations are subject to a myriad of implicit controls to gain performance or reliability advantages.
Relationship: entailment
Domain: Security and privacy
Sentence 1: CONCLUSION In this paper we propose a new biometric measure for users of smartphones.
Sentence 2: we focus on the movement that a user per­formswhen answering(or placing)aphonecall,andweinvestigate whether this movement can be used as a biometric authentication measure.We propose four basic methods(leveraging different sen­sors available on the phone and several similarity algorithms).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Thus, we have a set of candidate attribute-value pairs, cau r for every user {ui,uj,uk ... } who is assigned to role i r. The union of these sets is the candidate role attribute set cau r for role r. Some of the individual attribute-value pairs sets will be exactly the same.
Sentence 2: some users as­signed to role r are likely to have exactly the same attribute­value pairs, cau i r .
Relationship: entailment
Domain: Security and privacy
Sentence 1: If we have two sets, S1 and S2, and |S1| = |S2|, then we say that S1 is denser at n-bits if |Cn(S1)| < |Cn(S2)|.
Sentence 2: if the total number of n-bit CIDR blocks containing S1 is smaller than the set containing S2.
Relationship: entailment
Domain: Security and privacy
Sentence 1: On the other hand, we cannot simply provide an equa- N N tion such as ReplicatedSystemi because this implies in­i=0 dependence in the failure of each system including the in­teraction of subsystems in their environment [20].
Sentence 2: one can imagine that for some 0 = j,k < N and j = k; ReplicatedSubSystemj causes ReplicatedSubSystemk to be less safe, or indicate a less safe system.
Relationship: entailment
Domain: Security and privacy
Sentence 1: On the other hand, the result of Lemma 1 is ap­plied to solve the problem in a convex fashion.
Sentence 2: problem (14) is recast with p1,...,pK as the variables, i.e., minimize -t (15) p1,...,pK ,t N ainpi subject to t -=0,i=1,...,Kbin + pi n=1 K pi =Pmax R , i=1  which is a strictly convex problem, and can be solved by any standard convex optimization algorithm.
Relationship: entailment
Domain: Security and privacy
Sentence 1: However, session key distribution in [27] requires that any coalition of at most t valid group members cannot get any information about another mem­ber s personal secret, while De.nition 2 in our paper requires that the uncertainty of such a coalition to determine another member s personal secret is at least b bits.
Sentence 2: session key dis­tribution in [27] doesn t allow any information leakage, while our De.nition 2 allows certain information leakage as long as the un­certainty of the secret is at least b bits.
Relationship: entailment
Domain: Security and privacy
Sentence 1: (The reader can refer to a recent overview of SMC [10], or a textbook-level treatment [6].)
Sentence 2: by running a dis­tributed protocol amongst themselves the parties can learn the desired result f(s1,...,sN )(or, in the general case, each party Pi learns the result outi) while ensuring that no in­formation about other party s input is revealed beyond what is implied by the result(s).
Relationship: entailment
Domain: Security and privacy
Sentence 1: We note that a user can potentially draw inferences on tuples that she cannot access only when such tuples have index values in common with other tuples that the user can access.
Sentence 2: there can be inference whenever there are plaintext tuples that have the same value for at least one attribute and can be accessed by di.erent, but overlapping, sets of users (i.e., the tuples have di.erent but overlapping acls).
Relationship: entailment
Domain: Security and privacy
Sentence 1: The polynomial function f in the veri.cation query Qi will take values on a subset of Zp of size = (p - (i - 1)f)/f as deg(f) = f. Thus, A can correctly guess the output of f with probability = f/(p - (i - 1)f).
Sentence 2: qi will be a valid type 2 forgery with probability at most e f/(p - (i - 1)f), i.e., Pr[Yi 2|Bi-1] = .
Relationship: entailment
Domain: Security and privacy
Sentence 1: - log2 max(|aut(C)|, |aut(S)|), where |aut(G)| denotes the number of automorphisms of graph G. We derive an upper bound on accuracy that may be achieved by any de-anonymization algorithm using the number of graph au­tomorphism.
Sentence 2: if the social network S has |aut(S)|automorphisms and contact graph C has |aut(C)| automorphisms then the accuracy of any de-anonymization algorithm cannot ex­ .C .S ceed min(1 - , 1 - ), where .S is the smallest natural |VC ||VS| number such that .S!
Relationship: entailment
Domain: Security and privacy
Sentence 1: Further, in an ideal taxonomy the axes of the lattice would be orthogonal from each other, but we can see that our current axes are not.
Sentence 2: we have seen an interplay between the Decision Mode and the Enforce­ment axis: in the systems with Centralized decision mode, there exists the potential for a network failure where the central server is inaccessible, and so a service will default to fail-closed rather than possibly improperly service a re­quest that limits the implementation to total prevention on the Enforcement axis.
Relationship: entailment
Domain: Security and privacy
Sentence 1: To mitigate these issues, in this paper we want to study to which extent it is possible to enrich the information collected during malware execution to make the experiments repeat­able and achieve full network containment.
Sentence 2: we experiment with a protocol-agnostic technique [20] previ­ously adopted to model the attack tra.c in high-interaction honeypots [18, 16].
Relationship: entailment
Domain: Security and privacy
Sentence 1: Sim­ilarly, when single-path symbolic execution is applied to test case generation toincrease coverage, itwillbe unable(in one iteration) to generate an input that forces execution down a different branch than in the original execution, if taking that branch is only feasi­ble with a different number of loop iterations.
Sentence 2: in single-path symbolic execution, the values of a symbolic variable re.ect only thedatadependencies on the symbolicinputs control dependencies, includingloopdependencies, areignored.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 2 The key observation of the proof is that U1 n D2 = Ø from the typing rule for the sequential composition implies: ''' '' ''' '' m1 I(Ei ) m2 .
Sentence 2: m1 I(Ei ) m2 , i = 1, 2 variables that appear in expressions that may be de­classi.ed in c2 (namely, expressions from Ei ''' ), are never updated in c1.
Relationship: entailment
Domain: Security and privacy
Sentence 1: To relate the behavior of our implementation to the theory of the framework, the semantics of calling a method not mentioned in the policy is considered to be the same as if the body of that method was recursively unfolded into the client code.
Sentence 2: the labels of values returned by internal function calls are determined solely by the rules for arithmetic operations (as described in Section 7.1.1).
Relationship: entailment
Domain: Security and privacy
Sentence 1: This way, only the credit issuer and the trusted mer­chant devices can write valid balances on the user s device.1 The harder problem is that of preventing the user from rewinding or re­playing his account balance.
Sentence 2: even if the balances are signed with an unforgeable signature, the user can still easily copy old signed balances and reuse these copies with different merchants.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Characterizing the Behavior of a Program Using Multiple-Length N-grams Carla Marceau Odyssey Research Associates 1 Ithaca, NY 14850 carla @ oracorp.com ABSTRACT Some recent advances in intrusion detection are based on detecting anomalies in program behavior, as characterized by the sequence of kernel calls the program makes.
Sentence 2: traces of kernel calls are collected during a training period.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We start with the assumption that the attacker was able to penetrate the .rewall to place an executable on the end host (e.g., overwriting an existing application), evade the anti-virus software (e.g., by performing an xor with a bit­mask), and execute the software on the end host (e.g., by the user running the executable).
Sentence 2: we achieved this through a client-side exploit, altered the calc.exe executable, Table 1: Time of events of the example to obtain memory contents and recon.gure the network.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The third case is for a later execution of the PUF to pro­duce x0 as a result.
Sentence 2: there must exist some j such that the contents of Reg would be the sequence x0,R1,R2,R3,...,Rj = x0 Recall that we assumed (m = 1) |Cj| = |Rj | = |Rj ' | = |x0| = m = .- log2 .. Then the probability that any Rj = x0 would be 1/2m , which gives us 11 11 P (Rj = x0)= == == .
Relationship: entailment
Domain: Security and privacy
Sentence 1: Keeping in mind the diverse requirements on OPE, this ar­ticle investigates randomized construction schemes of order­preserving functions that improve on one-wayness-related properties of the ideal object .
Sentence 2: when consid­ering the functions produced by a speci.c scheme and an entry c .R of the ciphertext range, there is a distribution specifying the probability that entries from the plaintext do­main D are mapped to c. For the ideal ob ject , this relates to the hypergeometric distribution [3], featuring a dominant peak at the most likely plaintext (m.l.p.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In addition, since the means to go from the input to the output is data-oblivious with respect to the I/Os (simulated using the address keys), the server who is watching the inputs and outputs cannot correlate any set of values.
Sentence 2: independent of the set of I/Os, any input item, x, at the beginning of the sort can be mapped to any output item, y, at the end.
Relationship: entailment
Domain: Security and privacy
Sentence 1: V endork: T 2Vi,k + T 2Vj,k = 1 (10) Equation (10) constrains that for any pair of dependent tasks i and j, T 2Vi,k and T 2Vj,k can never be 1 at the same time.
Sentence 2: the constraint prohibits dependent tasks from being sched­uled on cores from the same vendor and hence on the same core.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Although motivated by ICS, our proposed framework is generic, and can be used in other areas, such as health in­formation networks, military and government information management.
Sentence 2: access control for cloud-based information and computing services across security domains is a great challenge; our proposed framework can be used in a uni.ed form of attributed-based policies to combine RBAC with traditionally used mandatory access control, includ­ing non-hierarchical caveats, and other security polices; the framework also helps to deal with a great number of security objects, including both information and cloud services.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We argue that protection should be achieved through careful monitoring of activity at the DBMS layer.
Sentence 2: we identify four dimensions of tasks and actions executed by cyber-insiders during data exfiltration missions; these are: identification of data sources, data retrieval from the DBMS, lateral movement and exfiltration proper, i.e., transferring data outside the organization.
Relationship: entailment
Domain: Security and privacy
Sentence 1: So, when Pth =0.1, P =0.9875, when Pth =0.21,P = b 0.9999.
Sentence 2: in the case of a = 100 , when ev­ery node sends a message with probability 0.21 after every random period, the probability of the event messages being discovered by an attacker is close to 1/n.
Relationship: entailment
Domain: Security and privacy
Sentence 1: A new group of MA attacks, called Branch Prediction Analysis (BPA), on the other hand, exploit instruction path of a cipher [2, 1, 4, 3].
Sentence 2: an adversary can reveal the execution .ow of a cipher using BPA, and if this execution .ow is key dependent as in the case of RSA and ECC, then he can compromise the system.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In order to solve this problem, we leverage the asymmetry between the simulator and the cheating committer.
Sentence 2: note that the simulator knows how to equivocate even at the start of the decommitment phase (conditioned on the event that it previously extracted a valid trapdoor from the receiver), while the cheating sender can only equivocate after the re­ceiver reveals the trapdoor.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The .rst condition says that there is an algorithm that checks whether a given bit-string t * , that has been submitted to H1 by B, corresponds to the challenge y.
Sentence 2: if the tester answers yes (1), then t * matches with the value of t as computed by the decryption or­acle and additionally satis.es the sanity check; and if it answers no (0), then t * does not satisfy the sanity check.
Relationship: entailment
Domain: Security and privacy
Sentence 1: This strategy will improve the performance experienced by mobile users of Tor when accessing the Internet not only for standard web-browsing but also for a wider range of services.
Sentence 2: the successful use of streaming ser­vices, such as Voice-over-IP (VoIP), by a rapidly growing community of mobile Tor users, may depend on some such strategy, since the current design of Tor is likely to lead to a level of interruption to this type of service that will be considered unacceptable by users.
Relationship: entailment
Domain: Security and privacy
Sentence 1: But a compromised machine might still try to read con.dential data from a remote host and then leak the data.
Sentence 2: we assume that a run-time access control mechanism is de­ployed so that read requests from untrusted machines for con.den­tialdatawouldbe rejected.akeystorewould not send keys to untrusted machines, so a compromised machine is not able to obtain con.dential databycorrupting codeexecution.
Relationship: entailment
Domain: Security and privacy
Sentence 1: To achieve its goal (e.g., introducing errors in the aggre­gate), the .rst thing that the aggregator would attempt to try is to modify individual data coming from participants whose data it is responsible to aggregate.
Sentence 2: since a partici­pant generates a tag for each of its data item using the Tag algorithm, we require unforgeability of Tag .it should be computationally impossible for the aggregator to forge a non-trivial tag of a victim participant.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The density of generated alerts by the IDS is used as an indicator of aggres­sion, which in turn determines whether a signature should be pushed to the prevention system or still be monitored6 .
Sentence 2: a signature that is reported frequently by many sites will have a higher virulence estimation than one that is reported less frequently and by a smaller number of honeypots, and is therefore more likely to be pushed to the IDS/IPS sensors.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Intuitively, srelease semantics stipulate that a peer Alice can send peer Carla a fact or rule ¢ directly signed by Bob if (1) ¢ is true at Alice, and (2) Bob lsigns srelease(¢, Alice, Carla) , Carla = Alice , or Carla = Bob is true at Alice.
Sentence 2: alice can only send out a formula signed by Bob if she is sending it to herself or to Bob, or she can prove that Bob thinks that it is okay for her to send the message out.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The reason for this is the high system complexity and plenty of subtleties surrounding the design, de.nition and analysis of key-exchange protocols.
Sentence 2: though seemingly conceptually simple, the (H)MQV protocols were actually very delicately designed, in the sense that slight changes in the design structure or parameters can lose provable se­curity or even be totally insecure.
Relationship: entailment