sadat2307/MSciNLI huggingface dataset:
Domain: Security and privacy
Sentence 1: Statistical Profiling and Visualization for Detection of Malicious Insider Attacks on Computer Networks  Jeffrey B. Colombe The MITRE Corporation Emerging Technologies Office McLean, VA (703) 883-5307 jcolombe@mitre.org ABSTRACT The massive volume of intrusion detection system (IDS) alarms generated on large networks, and the resulting need for labor­intensive security analysis of the text-based IDS alarm logs, has recently brought into question the cost-effectiveness of IDSs.
Sentence 2: when host-based IDSs are used to monitor an organization s internal networks, the majority of the resulting alarms represent legitimate, automated system administration.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Our core security calculus is simpli.ed, although we take care to include all aspects that require conceptual modeling for reason­ing about DFI.
Sentence 2: we model threads, mutable references, binaries, and data and code pointers; other features of x86 binaries, such as recursion, control .ow, and parameterized procedures, can be encodedinthe core calculus.Wealso modelall detailsofWin­dowsVista that are relevant for mandatory integrity control with dynamic labels.
Relationship: entailment
Domain: Security and privacy
Sentence 1: mines that to reach the goal state (i.e., root level access on C) in c3, an attacker either needs {s7,s8,s9,s2,s3,s5}or {s7,s10,s11,s12,s14,s15,s17} attributes to be satis.ed in the initial (start) state.
Sentence 2: two distinct attack paths are now available for an attacker (in c3), instead of just one attack path (as in c2).
Relationship: entailment
Domain: Security and privacy
Sentence 1: MAST develops a perception of suspicion for applications through the use of Multiple Correspondence Anal­ysis (MCA) [1], a technique used in the social sciences to deter­mine the statistical correlation between multiple categorical (i.e., qualitative) data.
Sentence 2: we develop a questionnaire for Android applications that looks for strong relationships between declared indicators of application functionality (e.g., permissions, intent .lters, the presence of native code, etc.)
Relationship: entailment
Domain: Security and privacy
Sentence 1: However, for L to actually approach the worst-case logn, the block size needs to be close to 2 · logn.
Sentence 2: with 512 byte = 4096 bit blocks, we would need a disk holding 22056 bytes to approach that many levels of recursion (4096 = 2 · log n, so n = 22048 blocks, each of size B =512 byte.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In what we call the full black box model, the PKG is given access to decryption queries and no assumptions are made regarding how the decryption box works.
Sentence 2: just by observing the input/output behavior of the given decryp­tion box, a judge should be able to decide if the box was created by the actual user or by a dishonest PKG.
Relationship: entailment
Domain: Security and privacy
Sentence 1: During regeneration, a majority voting scheme is applied to each of the columns in the three identically regenerated bitstrings as a means of avoiding single bit .ip failures.
Sentence 2: the .nal bitstring is constructed by using the majority of the 3 column bits as the .nal bit for each bit position, i.e., a 1 is assigned in the .nal bitstring when 2 or more of the 3 bits in the column are 1 , and a 0 otherwise.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Resilient Multidimensional Sensor Fusion Using Measurement History Radoslav Ivanov Computer and Information Miroslav PajicDepartment of Electrical and Insup LeeComputer and Information Science Department Systems Engineering Science Department University of Pennsylvania University of Pennsylvania University of Pennsylvania Philadelphia, PA 19104 rivanov@seas.upenn.edu Philadelphia, PA 19104 pajic@seas.upenn.edu Philadelphia, PA 19104 lee@cis.upenn.edu ABSTRACT This work considers the problem of performing resilient sen­sor fusion using past sensor measurements.
Sentence 2: we consider a system with n sensors measuring the same physical variable where some sensors might be attacked or faulty.
Relationship: entailment
Domain: Security and privacy
Sentence 1: For our implementation, we restrict ourselves to a two­valued OCL-like language for specifying SecureUML permis­sion constraint, i.e., we only support calls to public attributes and the result of calling side­e.ect-free public operation of the current resource (ob­ject),  the special SecureUML keyword caller referring to the subject executing an action,  a limited set of functions for comparing values, e.g., _=_, _ <= _, _<_, and  the Boolean operations _ and _, _ or _, and _ not _.
Sentence 2: we do not support recursive OCL expressions and iterator-based expressions like ->forAll( _ | _ ).
Relationship: entailment
Domain: Security and privacy
Sentence 1: The uniqueness results show statistical behavior consistent with law of large numbers and comparisons of bits derived from random processes.
Sentence 2: non-divergence of Gaussian mean values and Gaussian standard deviation values were demonstrated when the number of response comparisons was increased from a relatively small number of about 1 million, giving a high degree of confidence that chip uniqueness from a statistical standpoint is guaranteed as the number of comparisons increase well beyond 1 million (the Gaussian tail is very well behaved and does not flare out or show divergence).
Relationship: entailment
Domain: Security and privacy
Sentence 1: The main feature of such protocols is that they attest speci.c properties of the software platform.
Sentence 2: instead of attesting software using low-level measurements (i.e., software hashes), which could reveal proprietary information to competitors, they attest whether the software satis.es certain properties impliedby the client s security goals.Forexample, on SSC, such protocols could attest that the hypervisor implements the SSC privilege model,but not reveal anyadditional information to clients.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Convertible un- deniable signatures [11, 19, 28] require the signer's assistance for verification, but have a feature that allows the signa- tures to be converted into "un~ersally verifiable" signatures.
Sentence 2: the original signatures can be converted into signa- tures that can be verified by anyone without the assistance of the signer.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Nonetheless, useful fea­tures contribute positively to the .nal score, especially for samples where the anomaly value determined by necessary features is low (this is the case for scores represented in the lower left corner of the graph).
Sentence 2: without the contribution of useful features, the anomaly score of seven samples would be below the threshold (i.e., they would be false negatives).
Relationship: entailment
Domain: Security and privacy
Sentence 1: 4.1 Prevalence of the Botnet Phenomenon In order to provide a high level view of botnet prevalence, we highlight the contribution of their spreading activity to the overall unwanted traf.c received at our darknet space.
Sentence 2: we examine to what extent the traf.c received at this viewpoint can be attributed to botnets spreading activity.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In other words, p is a plan for C if and only if it is a plan for .1 .···..c for some .i .Gi, where .i is a Type 1 constraint and .1 .···..c represents the constraint set {.1,..., .c}.We call .1 .···..c a simple constraint expression.
Sentence 2: we have reduced the satis­.ability of an instance of WSP3(=, =) to determining the satis.ability of one or more instances of WSP1(=, =).
Relationship: entailment
Domain: Security and privacy
Sentence 1: Our attack can be easily adapted to break this variant: In the .rst stage, we recover the noise space but ignore the free term.
Sentence 2: we recover two polynomials v, v ' but instead of having g = a · v + b · v ' for some scalars a, b,wewould have g = a · v + b · v ' + c ·(1, 0,..., 0) for some a, b, c (and similarly for h).
Relationship: entailment
Domain: Security and privacy
Sentence 1: The key thing to note here is that MyPageKeeper identi.es so­ cial malware at the granularity of individual posts, without group­ ing together posts made by any given application.
Sentence 2: for every post that it crawls from the wall or news feed of a sub­ scribed user, MyPageKeeper s determination of whether to .ag that post does not take into account the application responsible for the post.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In the context of TMS, the need to protect the privacy of search operations, minimize data exposure during a possible attack on the TMS infrastructure and, at the same time, a requirement for fast and efficient operations, led to the employment homomorphic encryption algorithms [10] in the solution architecture.
Sentence 2: two partially homomorphic schemes were deployed: Search Scheme [11], a homomorphic encryption scheme designed for text data, allowing equality comparison between encrypted keywords of an email message or query.
Relationship: entailment
Domain: Security and privacy
Sentence 1: EFFICIENT T-PAKE FROM PPSS A password protected secret sharing (PPSS) scheme can be used as a black box to achieve a threshold password authenticated key exchange (T-PAKE) protocol (in the public key model) at very little additional cost.
Sentence 2: the round complexity of the resulting T-PAKE is the same as the PPSS because all the T-PAKE messages can be piggybacked onto the PPSS protocol .ows.
Relationship: entailment
Domain: Security and privacy
Sentence 1: CREATE VIEW Courses_View WITH pAS_key, pRoll_keySELECT * FROM Courses_Table WHERE Authorize(:pAS_key, :pRoll_key) IS NOT NULL Figure 15: course parameterized view definition The idea of this definition is to allow only the application s users to access the table.
Sentence 2: it is not enough to connect to the database with the DB user, in order to access the table, but you also need to be one of the legal application users, so that you can access data.
Relationship: entailment
Domain: Security and privacy
Sentence 1: For the .gures, we aggregated the probability values (which are real numbers in the interval be­tween 0.0 and 1.0) into ten bins, each bin covering an interval of 0.1.
Sentence 2: all probabilities in the interval [0.0, 0.1[ are added to the .rst bin, values in the interval [0.1, 0.2[ are added to the second bin, and so forth.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Remarkably, although locality constitutes a strict con­straint on the structure of LGI laws, it does not reduce their expressive power, as has been proved in [6].
Sentence 2: despite its structural locality,anLGI law can have global ef­fect over the entire L-community mostly because all mem­bers of that community are subject to the same law and can, thus, be used to establish mandatory, community wide, constraints.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 6.3 Advanced Security Features Advanced ob jectives such as secure delegation (O6) and policy-based access control (O7) require a secure user in­terface to handle security-sensitive user input (as discussed in Section 3.2).
Sentence 2: token delegation relies on a password-based authentication of the delegated user U against the car owner O before the delegated token is issued.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Phase I: Trace Collection Trace Collector Web Server User/ (WebScarab Proxy) Simulator Session Inspector Session Exporter Phase II: FSM Inference SessionProfile Web request /response Spec Analzyer Symbolizer RequestProfile Session Variable TemplateProfile Session Log FSM Analyzer DriverSpec StateProfile TestSpec Generator TestSpec Phase III: Testing Testing Engine Web Ser ver State Driver Testing Controller Symbolizer Session Inspector Session Exporter LoginProfile Request Generator Output Evaluator Login Helper InputProfile Figure 4: Prototype System Architecture Testing Engine instantiates test input vectors into con­crete web requests (i.e, Request Generator ), feed them into the application and evaluate the corresponding web responses for vulnerability identi.cation (i.e., Output Evaluator ).
Sentence 2: testing Controller is in charge of the entire test­ing procedure.
Relationship: entailment
Domain: Security and privacy
Sentence 1: G) e The (f,w)subset code is a primitive batch code with n w G) we and Nmj.oj.
Sentence 2: we index each data item by a unique G.el)set TEand each bucket by a unique subset S[f]of wsize at most w. The content of bucket Sis de.ned by: rsdef xT.each bucket receives the sum (or exclusive­ T:sr T w or) of the data items labelled by its supersets.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Our approach provides a di.erent point in the design space.
Sentence 2: our policies are more expressive, in that our framework can handle both integrity and con.dential­ity policies, and more .ne-grained, in that our framework can carry multiple taints from di.erent sources at the same time, rather than just a single bit of taint.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In addition to the above observation, if k2 > 0 then the .rst user and if k2n < d, then the n-th user can be accused of being a traitor.
Sentence 2: a wise traitor strategy (if the .rst or/and the n-th user is in the coalition) would try to have k2 = 0 and k2n = d. In this case, similar to  M [1, d] [d + 1, 2d] [2d + 1, 3d] .
Relationship: entailment
Domain: Security and privacy
Sentence 1: Extensible Markup Language (XML) Unlike HTML, which has a predefined vocabulary of tags for displaying data, XML is designed to describe data by enabling XML authors to invent self-descriptive tags to annotate web pages or sections of text on a web page and apply arbitrary structure to their web documents.
Sentence 2: xML is all about the description of data while HTML describes data and its presentation.
Relationship: entailment
Domain: Security and privacy
Sentence 1: If we are able to precisely identify these in-VM syscall execution contexts when the corresponding trusted utilities are executed at the VMM layer, and if we are able to maintain a secure duplicate of the running VM, through redirecting both their memory read and write operation from the secure VM to the running VM, then we can transparently update the in-VM kernel state of the running VM from the outside via the secure VM.
Sentence 2: the semantic-gap (e.g., the memory location of in-VM kernel state) is automatically bridged by the intrinsic instruction constraints encoded in the binary code in the duplicated VM.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 2, Where, Trequestis trustworthiness of the peer requests to consume service, Tprovide is trustworthiness of the peer provides service, and [T ,T ],(T <T ) is the range of trustworthiness.
Sentence 2: 12 1 2 when a peer look for peers who can provide some service it need, it should look for those peers whose trustworthiness is close to it.
Relationship: entailment
Domain: Security and privacy
Sentence 1: One of the key reasons that trusted systems are good at enabling price discrimi­nation is that they sharply decrease sharing; they are designed to eliminate any redistribution of the information good outside of the control of the content provider.
Sentence 2: price discrimination allows the market to extend to consumers with lower willingness to pay, but at the expense of cutting off existing means, through sharing and secondary markets, of getting the information or entertainment at low or no cost to some of those same consumers.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We propose to simply drop the requirement that v ' exer­ cises the same control path as v. Instead, we de.ne VR(v) to be the valuation vc that maps every integer variable to v(s) i 1 and every string variable s to a , where a denotes a string of a s of length i.
Sentence 2: vc maps s to a string of a s precisely as long as the string mapped by v. We note thatvc is manifestlybenign andnon-attackingfor any program.
Relationship: entailment
Domain: Security and privacy
Sentence 1: CONCLUSION Through empirical demonstrations, we have shown that the characteristics of storage are fundamentally different from those of network, and cryptographic assumptions on the uniqueness of keys and IVs relative to content can be violated through unanticipated side effects from various storage mechanisms.
Sentence 2: we have demonstrated storage mechanisms that can convert intended one-time pads into two-time pads: in-place updates, backups, inconsistencies between memory and disk content, and wear-leveling for flash storage.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 1.2 Our Contributions In this paper, we describe a methodology which allows the service provider to obtain a quality measure so as to guarantee that the input to later computations and the cor­responding output (the actual analysis performed on the data) comply with certain con.dence criteria.
Sentence 2: we propose a protocol that allows evaluating the quality of ECG signals and communicates to the user whether the in­put signal is good enough to continue with the measurement and analysis or not.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We can clearly see that the targets and the activities of this army have evolved between certain attack events (e.g., when the value of zi is low).
Sentence 2: this army has been scanning (at least) four con­secutive class A-subnets during its lifetime (still 183 days), while probing at the same time three di.erent ports on these subnetworks.
Relationship: entailment
Domain: Security and privacy
Sentence 1: In this paper, we present and analyze ProActive Caching according to per­formance improvements and provide a detailed comparison with other approaches for caching role-based and dynamic access control decisions in business process-driven systems.
Sentence 2: we compare generic caching strategies against caching strategies developed to speci.cally cache access con­trol decisions, i. e., SAAMRBAC and ProActive Caching.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Ourexpectationsabout the advantagesof the Grid s features were borne out by the statistical results of our user study.
Sentence 2: our study demonstrates higher accuracyfor the Grid compared toWindows for the view-complex, change­complex, and con.ict-complex tasks that tested the Grid s whole policy, effective policy, group membership informa­tion,simplechanges,andnewpolicy semantics features.
Relationship: entailment
Domain: Security and privacy
Sentence 1: 4.4 BehaviorAccumulation Sincethe suspicionlevelisgeneratedeverytimewindow, a worm canhideitselfby appearing benignfor each windowbut malicious overall.
Sentence 2: it may spread suspicious behavior over different time windows or reduce the intensity of malicious activ­ities within a single window, thus decreasing its suspicion level.
Relationship: entailment
Domain: Security and privacy
Sentence 1: A setting where this is particularly important is that of wire- less ad-hoc networking: secure communication between ar-bitrary co-located devices that share no a priori trust infor- mation other than that their owners wish them to commu- nicate with each other.
Sentence 2: we want to capture the user intuition that they want their device to commu-nicate (only) with that particular communication partner -to have the act of pointing out who you want to talk to also implicitly indicate to the software who was authenti-cated to communicate with you.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Virtualizationhasbeenusedas enablingtechnology to en­sure strong isolation indi.erent researchprojects likeTerra [6], EMSCB [11] and OpenTC [8].
Sentence 2: openTC is a research project funded by the European Commission and aiming at the development of an open-source security framework built upon TCG s security concepts.
Relationship: entailment
Domain: Security and privacy
Sentence 1: We can achieve such scenarios when the input/output size combined with the internal gate size of the circuit makes such logic analysis and reduction intractable in the average case.
Sentence 2: the only guarantee that an adversary recovers components when they are originally configured in such "no-win" scenarios (D in Figure 3) is when the circuit is conducive to perfect minimization to begin with (i.e., the semantics of the truth table would clearly reveal component independence or boundaries).
Relationship: entailment
Domain: Security and privacy
Sentence 1: If it is preferred, Naor and Reingold s [8] technique could be used to replace the functions f3,K and f0,K in EK (·) with two pair-wise independent permutations.
Sentence 2: let h1(·) and h2(·) be two pair-wise independent permutations on the set of 2n-bit bi­nary strings.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The detection function of an SVM for an unknown sequence x of events is given by f(x)= (f(x), w) + b =Ie(x) we + b. e.E where f(x) simply returns the orientation of f(x) with re­spect to the hyperplane.
Sentence 2: f(x) > 0 indicates mali­cious behavior in the sequence x and f(x) = 0 corresponds to benign activity.
Relationship: entailment
Domain: Security and privacy
Sentence 1: The attacker in PETRA s threat model can jeopardize the integrity and authenticity of the software update components by executing weak man-in-the-middle attacks on network traffic.
Sentence 2: the attacker is assumed to have the necessary expertise and tools to conduct any type of modification and fabrication attack on the network data without being capable of physically compromising the communicating end systems, namely the content distribution server and the update client.
Relationship: entailment
Domain: Security and privacy
Sentence 1: This likelihood information can be used to improve the mapping process of the signature string to the WM objective function, thus increasing the WM strength.
Sentence 2: the heuristics can be used to decide if each bit bi.S should be mapped to the variable xi or its complement xi (+ or in the objective function) in order to increase the likelihood that as­signing variables according to S would result in a valid solution.
Relationship: entailment
Domain: Security and privacy
Sentence 1: These new instructions are not intended when the original kernel code is generated.
Sentence 2: if these instructions happen to end with a ret instruction, they can be potentially combined to form a gadget as an organi­zational unit in return-oriented programming  and perform some (malicious) primitive operations, such as a comparison of two register operands or a memory load from a speci.ed location.
Relationship: entailment
Domain: Security and privacy
Sentence 1: As we have already noted, this does not mean that tokens should be identical across servers only that there should be considerable overlap in the brute-force computation needed to solve the puzzle for a given channel-number across servers.
Sentence 2: it is not desirable for one server to be able to use its shortcut to compute the tokens associated with another server, as this would result in a di.usion of trust across all participating servers rather than in the bastion alone.
Relationship: entailment
Domain: Security and privacy
Sentence 1: Care is thus required when applying our results to general real-world settings, e.g., large-scale smart grids, where locational prices are im­portant.
Sentence 2: propositions 1 to 5 are based on the linearized abstract supply and demand models at a .xed operating point.3 The security analysis in Propositions 2 to 5 is based on the approximate aggregate demand model in Eq.
Relationship: entailment