Access Control Policies (ACP) specify which principals such as users have access to which resources. Ensuring the correctness and consistency of ACPs is crucial to prevent security vulnerabilities. However, in practice, ACPs are commonly written in Natural Language (NL) and buried in large documents such as requirements documents, not amenable for automated techniques to check for correctness and consistency. It is tedious to manually extract ACPs from these NL documents and validate NL functional requirements such as use cases against ACPs for detecting inconsistencies. To address these issues, we propose an approach, called Text2Policy, to automatically extract ACPs from NL software documents and resource-access information from NL scenario-based functional requirements. We conducted three evaluations on the collected ACP sentences from publicly available sources along with use cases from both open source and proprietary projects. The results show that Text2Policy effectively identifies ACP sentences with the precision of 88.7% and the recall of 89.4%, extracts ACP rules with the accuracy of 86.3%, and extracts action steps with the accuracy of 81.9%.

/pdf/automated-extraction-of-security-policies-from-natural-11rpk94jit.pdf

Automated extraction of security policies from natural-language software documents

The growth of data and categories of attacks, demand the use of Intrusion Detection System(IDS) effectively using Machine Learning(ML) and Deep Learning(DL) techniques. Apart from the ML and DL techniques, Swarm and Evolutionary (SWEVO) Algorithms have also shown significant performance to improve the efficiency of the IDS models. This survey covers SWEVO-based IDS approaches such as Genetic Algorithm(GA), Ant Colony Optimization(ACO), Particle Swarm Optimization(PSO), Artificial Bee Colony Optimization(ABC), Firefly Algorithm(FA), Bat Algorithm(BA), and Flower Pollination Algorithm(FPA). The paper also discusses applications of the SWEVO in the field of IDS along with challenges and possible future directions.

Role of swarm and evolutionary algorithms for intrusion detection system: A survey

http://www.synchromedia.ca/fr/system/files/SurveyIRS.pdf

Taxonomy of intrusion risk assessment and response system

/pdf/adversarial-attacks-against-windows-pe-malware-detection-a-1i1u5x05.pdf

Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art

Detecting denial of service by modelling web-server behaviour

SAM is an international conference that serves researchers, scholars, professionals, students, and academicians who are looking to both foster working relationships and gain access to the latest research results. The 2014 Congress was composed of research presentations, keynote lectures, invited presentations, tutorials, panel discussions, and poster presentations. Selected Topics: Security Education, Wireless Networks Security + Modeling of Information Security, Information Assurance, Network Security + Security Management, Biometrics and Forensics I + Cryptographic Technologies, Computer and Hardware Security, IOT and SCADA Cybersecurity Education, Cryptographic Technologies I, Cryptographic Technologies II, Security Management I, Security Management II, Biometrics and Forensics II, Security Applications, IOT and SCADA Cybersecurity Education, Security Systems and Applications.

Security and Management

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Security and Management