Objective: To evaluate the use of monosyllabic word recognition versus sentence recognition to determine candidacy and long-term benefit for cochlear implantation. Study Design: Prospective multi-center single-subject design. Methods: A total of 21 adults aged 18 years and older with bilateral moderate to profound sensorineural hearing loss and low monosyllabic word scores received unilateral cochlear implantation. The consonant-nucleus-consonant (CNC) word test was the central measure of pre- and postoperative performance. Additional speech understanding tests included the Hearing in Noise Test sentences in quiet and AzBio sentences +5 dB signal-to-noise ratio (SNR). Quality of life (QoL) was measured using the Abbreviated Profile of Hearing Aid Benefit and Health Utilities Index. Results: Performance on sentence recognition reached the ceiling of the test after only 3 months of implant use. In contrast, none of the participants in this study reached a score of 80% on CNC word recognition, even at the 12-month postoperative test interval. Measures of QoL related to hearing were also significantly improved following implantation. Conclusion: Results of this study demonstrate that monosyllabic words are appropriate for determining preoperative candidate and measuring long-term postoperative speech recognition performance. Level of Evidence: 2c.

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Analysis of information and risk Actively test or assess networks for evidence of leakage or misconfigurations Asset management inventory and review of known good configuration files Observable identified by organization through user reports or security alert Observe artifacts of data leakage, theft, misconfigured devices Identify suspicious activity or suspect configurations and analyze Security Group/Help Desk initiates Incident Response process Contain the incident Eradication and ongoing mitigations Collect and gather information and evidence to support analysis Develop after-action report and assess loss if any Correct any misconfigurations or findings Validate mitigations and assess policy/strategy Root cause analysis as to why configurations were not as planned or expected Conclusions Network Attacks Preconditions Onset Observable identified by organization through user reports or security alert Security Group/Help Desk initiates Incident Response process Contain the incident Analysis of information and risk Eradication and ongoing mitigations Collect and gather information and evidence to support analysis Notify users whose information has been compromised Develop after-action report and assess loss if any Validate mitigations and assess policy/strategy Conclusions Substation / SCADA Attacks Preconditions Onset Observable identified by organization through user reports or security alert Analysis of information and risk Contain the incident Security Group/Help Desk initiates Incident Response process Collect and gather information and evidence to support analysis Validate mitigations and assess policy/strategy Develop after-action report and assess loss if any Eradication and ongoing mitigations Conclusions AMI Attacks Preconditions Onset Design and implement or update the policies of implemented Security Incident and Event Management (SIEM) to make sure that such breaches are detected, alerted, ticket created and response provided. Create or update the security incident detection and response framework Observable identified by organization through user reports or security alert Contain the incident Job Performance Panel Chair The subject matter expert co-leader of a Job Performance Panel.

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Abstract. This paper presents the application of deception theory to improve the success of client honeypots at detecting malicious web page attacks from infected servers programmed by online criminals to launch drive-by-download attacks. The design of honeypots faces three main challenges: deception, how to design honeypots that seem real systems; counter-deception, techniques used to identify honeypots and hence defeating their deceiving nature; and counter counter-deception, how to design honeypots that deceive attackers. The authors propose the application of a deception model known as the deception planning loop to identify the current status on honeypot research, development and deployment. The analysis leads to a proposal to formulate a landscape of the honeypot research and planning of steps ahead.