Malicious Code Invariance Based On Deep Learning

The malicious code detection is critical task for in the field of security. The malicious code detection can be possibly by using convolutional neural network (CNN).Themalicious code can be categorized in to different families. The malicious code identification helps to identify the affected malware on the system. Malicious code theft data from our system and it yields high security issues in real time. The neural network architecture classifies the malicious code based on the collected dataset. The dataset contains different families of malicious code. The malicious code detection can be done with the help of model created from CNN architecture

BMOP: Bidirectional Universal Adversarial Learning for Binary OpCode Features

For malware detection, current state-of-the-art research concentrates on machine learning techniques. Binary n -gram OpCode features are commonly used for malicious code identification and classification with high accuracy. Binary OpCode modification is much more difficult than modification of image pixels. Traditional adversarial perturbation methods could not be applied on OpCode directly. In this paper, we propose a bidirectional universal adversarial learning method for effective binary OpCode perturbation from both benign and malicious perspectives. Benign features are those OpCodes that represent benign behaviours, while malicious features are OpCodes for malicious behaviours. From a large dataset of benign and malicious binary applications, we select the most significant benign and malicious OpCode features based on the feature SHAP value in the trained machine learning model. We implement an OpCode modification method that insert benign OpCodes into executables as garbage codes without execution and modify malicious OpCodes by equivalent replacement preserving execution semantics. The experimental results show that the benign and malicious OpCode perturbation (BMOP) method could bypass malicious code detection models based on the SVM, XGBoost, and DNN algorithms.

Comprehensively Meld Code Clone Identifier for Replicated Source Code Identification in Diverse Web Browsers

The emergence and the progress in the process of reusing the software’s, has caused difficulties in the maintaining the software codes and the corresponding depositories. Cloning of software codes is the important reason behind the arising difficulties in the maintenance of the software codes and the depositories. The cloning of codes is a process of replicating the existing codes for utilizing it elsewhere within the software system. The copying and pasting of the fragments of code is also well thought-out as the method of code cloning causing difficulties in the software maintenance. The maintenance of software is described as the alteration performed over the existing software on the completion of the development as well as the implementation process. Utilizing the maintenance process in the software the software companies deliver the improvements and the additional enhancements according to the working circumstance. The maintenance mainly focuses on removing the bugs and to repair the identified faults in the time of execution to enhance the performance. The work focused on the paper is mainly a comprehensive study over the prevailing tools laid out in the process of code-clone identification. The different techniques employed, the challenges incurred, the blunders made in the development, the enhanced refactoring challenges and efforts in the comprehension of codes are explored in the paper. From the study it was understood that the code clone identification using the web based tools are more advantageous than engaging algorithms in identifying the clones. So the a hybridized (meld) web based code clone identifier tool is engaged in the process of identifying the cloning of codes in diverse web browsers in exhibited in the paper. The exhibited tool equips a highly powerful tool for detecting the clones in a precise an efficient manner. The duplication of codes are often vulnerable and could be malicious. So the exhibited work in the future concentrates in developing an extended tool to identify the malicious codes to improvise the process of code clone identification making it concise and effective.

Safe Artificial General Intelligence via Distributed Ledger Technology

Artificial general intelligence (AGI) progression metrics indicate AGI will occur within decades. No proof exists that AGI will benefit humans and not harm or eliminate humans. A set of logically distinct conceptual components is proposed that are necessary and sufficient to (1) ensure various AGI scenarios will not harm humanity, and (2) robustly align AGI and human values and goals. By systematically addressing pathways to malevolent AI we can induce the methods/axioms required to redress them. Distributed ledger technology (DLT, “blockchain”) is integral to this proposal, e.g., “smart contracts” are necessary to address the evolution of AI that will be too fast for human monitoring and intervention. The proposed axioms: (1) Access to technology by market license. (2) Transparent ethics embodied in DLT. (3) Morality encrypted via DLT. (4) Behavior control structure with values at roots. (5) Individual bar-code identification of critical components. (6) Configuration Item (from business continuity/disaster recovery planning). (7) Identity verification secured via DLT. (8) “Smart” automated contracts based on DLT. (9) Decentralized applications—AI software modules encrypted via DLT. (10) Audit trail of component usage stored via DLT. (11) Social ostracism (denial of resources) augmented by DLT petitions. (12) Game theory and mechanism design.