Mitigating Risk

2012 ◽  
pp. 261-269
Author(s):  
Ken Lozito
Keyword(s):  
Planning Process ◽  
Deep Level ◽  
Historical Event ◽  
Event Management ◽  
Efficient Manner ◽  
Business Decision ◽  
Security Event ◽  
Security Information ◽  
Security Standards ◽  
Level Analysis

Business Intelligence (BI) has often been described as the tools and systems that play an essential role in the strategic planning process of a corporation. The application of BI is most commonly associated with the analysis of sales and stock trends, pricing and customer behavior to inform business decision-making. There is a growing trend in utilizing the tools and processes used in the analysis of data and applying them to security event management. Security Information and Event Management (SIEM) has emerged within the last 10 years providing a centralized source to enable both real-time and deep level analysis of historical event data to drive security standards and align IT resources in a more efficient manner.

Mitigating Risk

2011 ◽  
Vol 2 (2) ◽  
pp. 67-75 ◽  
Author(s):  
Ken Lozito
Keyword(s):  
Planning Process ◽  
Deep Level ◽  
Historical Event ◽  
Event Management ◽  
Efficient Manner ◽  
Business Decision ◽  
Security Event ◽  
Security Information ◽  
Security Standards ◽  
Level Analysis

Business Intelligence (BI) has often been described as the tools and systems that play an essential role in the strategic planning process of a corporation. The application of BI is most commonly associated with the analysis of sales and stock trends, pricing and customer behavior to inform business decision-making. There is a growing trend in utilizing the tools and processes used in the analysis of data and applying them to security event management. Security Information and Event Management (SIEM) has emerged within the last 10 years providing a centralized source to enable both real-time and deep level analysis of historical event data to drive security standards and align IT resources in a more efficient manner.

scholarly journals A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199858
Author(s):  
Gianpaolo Gulletta ◽  
Eliana Costa e Silva ◽  
Wolfram Erlhagen ◽  
Ruud Meulenbroek ◽  
Maria Fernanda Pires Costa ◽  
...  
Keyword(s):  
Motor Control ◽  
Upper Limb ◽  
Planning Process ◽  
Computational Cost ◽  
Humanoid Robots ◽  
Efficient Manner ◽  
Daily Lives ◽  
Human Motor Control ◽  
Planning Algorithm ◽  
Human Motor

As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions.

scholarly journals ANALYSIS OF ROUTER ATTACK WITH SECURITY INFORMATION AND EVENT MANAGEMENT AND IMPLICATIONS IN INFORMATION SECURITY INDEX

2019 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
CITRA ARFANUDIN ◽  
Bambang Sugiantoro ◽  
Yudi Prayudi
Keyword(s):  
Information Security ◽  
Forensic Analysis ◽  
Event Management ◽  
Information Security Management System ◽  
Security Information ◽  
Information Assets ◽  
Security Index ◽  
Syn Flooding ◽  
The Government ◽  
The Impact

Information security is a need to secure organizational information assets. The government as the regulator issues an Information Security Management System (ISMS) and Information Security Index (US) as a measure of information security in the agency of a region. Security Information and Event Management (SIEM) is a security technology to secure information assets. SIEM is expected to provide information on attacks that occur on the router network and increase the value of the Indeks KAMI of government agencies. However, the use of SIEM is still questionable whether it can recognize a router attack and its impact on the value of our index. This research simulates attacks on routers with 8 attacks namely Mac Flooding, ARP-Poisoning, CDP Flooding, DHCP Starvation, DHCP Rogue, SYN Flooding SSH Bruteforce and FTP Bruteforce. 8 types of attacks followed by digital forensic analysis using the OSCAR method to see the impact on routers and SIEM. Also measured is index KAMI before and after the SIEM to be able to measure the effect of SIEM installation on the value of index KAMI. It was found that the use of SIEM to conduct security monitoring proved successful in identifying attacks, but not all were recognized by SIEM. SIEM only recognizes DHCP Starvation, DHCP Rogue, SSH Bruteforce and FTP Bruteforce. Mac Flooding, ARP-Poisoning, CDP Flooding, SYN Flooding attacks are not recognized by SIEM because routers do not produce logs. Also obtained is the use of SIEM proven to increase our index from the aspect of technology

Security Information and Event Management Implementation Guidance

2013 ◽  
pp. 94-115
Author(s):  
Yushi Shen ◽  
Yale Li ◽  
Ling Wu ◽  
Shaofeng Liu ◽  
Qian Wen
Keyword(s):  
Information Security ◽  
Security Analysis ◽  
The United States ◽  
Professional Judgment ◽  
Event Management ◽  
Security Risks ◽  
Customer Information ◽  
Simple Step ◽  
Security Information ◽  
Security As A Service

This chapter is about guidance and implementation prepared by the Cloud Security Alliance (CSA) Security as a Service (SecaaS) workgroup, which is made up of users and practitioners in the field of information security. In preparing this implementation guide, input has been sought from experts throughout Europe, the Middle East, and the United States. A lot of professional judgment and experience are applied in the architecture, engineering, and implementation of a Security Information and Event Management (SIEM) guide to ensure that it logs the information necessary to successfully increase visibility and remove ambiguity, surrounding the security events and risks that an organization faces. By providing SIEM as a service under SecaaS, the provider has to be able to accept log and event information, customer information and event feeds, and conduct information security analysis, correlation, and support incident response. By providing flexible real-time access to SIEM information, it allows the party consuming the SIEM service to identify threats acting against their environment cloud. This identification then allows for the appropriate action and response to be taken to protect or mitigate the threat. The simple step of increasing visibility and removing ambiguity is a powerful tool to understanding the information security risks that an organization is facing.

Big Data Analytics for Intrusion Detection

2020 ◽  
pp. 292-316 ◽  
Author(s):  
Luis Filipe Dias ◽  
Miguel Correia
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Semantic Gap ◽  
Behavioral Analysis ◽  
Event Management ◽  
Security Monitoring ◽  
Hidden Correlations ◽  
Security Information

Intrusion detection has become a problem of big data, with a semantic gap between vast security data sources and real knowledge about threats. The use of machine learning (ML) algorithms on big data has already been successfully applied in other domains. Hence, this approach is promising for dealing with cyber security's big data problem. Rather than relying on human analysts to create signatures or classify huge volumes of data, ML can be used. ML allows the implementation of advanced algorithms to extract information from data using behavioral analysis or to find hidden correlations. However, the adversarial setting and the dynamism of the cyber threat landscape stand as difficult challenges when applying ML. The next generation security information and event management (SIEM) systems should provide security monitoring with the means for automation, orchestration and real-time contextual threat awareness. However, recent research shows that further work is needed to fulfill these requirements. This chapter presents a survey on recent work on big data analytics for intrusion detection.

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