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War of Control Hijacking
Published in Uzzal Sharma, Parmanand Astya, Anupam Baliyan, Salah-ddine Krit, Vishal Jain, Mohammad Zubair Khan, Advancing Computational Intelligence Techniques for Security Systems Design, 2023
Ragini Karwayun, Monika Sainger
The CFI techniques are primarily classified as fine-grained and coarse-grained [27]. Both classifications comprise advantages and drawbacks in terms of control-flow security.Fine-Grained CFI uses a strict approach [28]. Labeling is one of the most common approaches to implementing fine-grained CFI. The edges of a control-flow transfer are labeled, and are checked for integrity on each control transfer. Control-flow transfer is preceded by a validation process to ensure that the transfer is passed to a valid destination with a valid label.Coarse-Grained CFI is referred to as loose CFI [28]. Coarse CFI differentiates between indirect branch instructions and imposes policies on each type. Loose CFI checks when a control-flow transfer initiates with a return instruction so that the destination can be targeted right after the call instruction. This CFI approach is more relaxed and gives a desirable performance, and does not experience high-performance overhead; however.
Flight Planning
Published in Yasmina Bestaoui Sebbane, Multi-UAV Planning and Task Allocation, 2020
Autonomous aircraft trajectory optimization including collision avoidance can be expressed as a list of linear constraints, involving integer and continuous variables known as mixed integer linear program. The mixed integer linear programming approach in [362] uses indirect branch-and-bound optimization, reformulating the problem in a linearized form and using commercial software to solve the MILP problem. A single aircraft collision avoidance application was demonstrated, and then, this approach was generalized to allow for visiting a set of way-points in a given order. Mixed integer linear programming can extend continuous linear programming to include binary or integer decision variables to encode logical constraints and discrete decisions together with the continuous autonomous aircraft dynamics. The approach to optimal path planning based on MILP was introduced in [374]. The autonomous aircraft trajectory generation is formulated as a 3D optimization problem under certain conditions in the Euclidean space, characterized by a set of decision variables, a set of constraints and the objective function. The decision variables are the autonomous aircraft state variables, i.e., position and speed. The constraints are derived from a simplified model of the autonomous aircraft and its environment. These constraints include Dynamics constraints, such as a maximum turning force which causes a minimum turning radius, as well as a maximum climbing rate.Obstacles avoidance constraints like no-flight zonesTarget reaching constraints of a specific way-point or target.
The Stuff Backscatter Random Fields Are Made Of
Published in Gianfranco (Frank) De Grandi, Elsa Carla De Grandi, Spatial Analysis for Radar Remote Sensing of Tropical Forests, 2021
Gianfranco (Frank) De Grandi, Elsa Carla De Grandi
An entirely different situation arises in connection with indirect branch–surface scattering. The vertical stems now supply a contribution similar to the branch direct scattering. Also, category 3 of long branches in the middle layer weighs in. The factor playing a role here is the length of the branches.
Profile-guided optimisation for indirect branches in a binary translator
Published in Connection Science, 2022
Jyun-Siang Huang, Wuu Yang, Yi-Ping You
An indirect branch instruction may have several branch destinations which are only known at runtime. Hyperchaining (including both platform-independent and platform-dependent hyperchaining) establishes a search table for each indirect branch instruction to record the visited branch destinations at runtime. When the indirect branch is executed again, the search table is used to determine the destination of the jump. This speeds up execution. However, there is still room for improvement in the search. In this work, we propose profile-guided optimisation for indirect branch. With the profile option, Rabbit will add code to the target binary which will collect profile information. The profile information includes the destinations of all indirect branch instructions and the execution frequency of each destination for each indirect branch instruction. Rabbit will consult the profile information when translating the same source binary again. An efficient search table based on the profile information will be added to the generated target binary. Profile-guided platform-independent hyperchaining uses an LLVM switch instruction to build the search table, which can be translated into assembly instructions in many different target platforms. Profile-guided platform-dependent hyperchaining uses the assembly instructions of a specific target architecture to create the search table. Our work focuses on the translation from AArch64 binary to RISC-V binary. With the profile information, hyperchaining can provide even more performance improvement.