LEADER 03347nam 22004815 450 001 9910300743803321 005 20240116112059.0 010 $a9781484238707 010 $a1484238702 024 7 $a10.1007/978-1-4842-3870-7 035 $a(CKB)4100000006674712 035 $a(MiAaPQ)EBC5521337 035 $a(DE-He213)978-1-4842-3870-7 035 $a(PPN)230542735 035 $a(CaSebORM)9781484238707 035 $a(OCoLC)1057830162 035 $a(OCoLC)on1057830162 035 $a(EXLCZ)994100000006674712 100 $a20180920d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCybersecurity Incident Response /$eHow to Contain, Eradicate, and Recover from Incidents /$fby Eric C. Thompson 205 $a1st ed. 2018. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2018. 215 $a1 online resource (184 pages) 320 $aIncludes bibliographical references. 327 $aChapter 1: The Significance of Incident Response -- Chapter 2: Necessary Prerequisites -- Chapter 3: Incident Response Frameworks -- Chapter 4: Leadership, Teams, and Culture -- Chapter 5: The Incident Response Strategy -- Chapter 6: Cyber Risks and the Attack Lifecycle -- Chapter 7: Detection and Identification of Events -- Chapter 8: Containment -- Chapter 9: Eradication, Recovery, and Post-Incident Review -- Chapter 10: Continuous Monitoring of Incident Response Program -- Chapter 11: Incident Response Story -- Chapter 12: This Is a Full-Time Job -- Appendix A: NIST CSF. 330 $aCreate, maintain, and manage a continual cybersecurity incident response program using the practical steps presented in this book. Don't allow your cybersecurity incident responses (IR) to fall short of the mark due to lack of planning, preparation, leadership, and management support. Surviving an incident, or a breach, requires the best response possible. This book provides practical guidance for the containment, eradication, and recovery from cybersecurity events and incidents. The book takes the approach that incident response should be a continual program. Leaders must understand the organizational environment, the strengths and weaknesses of the program and team, and how to strategically respond. Successful behaviors and actions required for each phase of incident response are explored in the book. Straight from NIST 800-61, these actions include: Planning and practicing Detection Containment Eradication Post-incident actions What You?ll Learn: Know the sub-categories of the NIST Cybersecurity Framework Understand the components of incident response Go beyond the incident response plan Turn the plan into a program that needs vision, leadership, and culture to make it successful Be effective in your role on the incident response team. 606 $aData protection 606 $aSecurity$3https://scigraph.springernature.com/ontologies/product-market-codes/I28000 615 0$aData protection. 615 14$aSecurity. 676 $a005.8 700 $aThompson$b Eric C$4aut$4http://id.loc.gov/vocabulary/relators/aut$0917128 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910300743803321 996 $aCybersecurity Incident Response$92528341 997 $aUNINA LEADER 02655nam 22005175 450 001 9910896193803321 005 20250808085220.0 010 $a3-031-65820-5 024 7 $a10.1007/978-3-031-65820-4 035 $a(CKB)36315667800041 035 $a(MiAaPQ)EBC31713218 035 $a(Au-PeEL)EBL31713218 035 $a(DE-He213)978-3-031-65820-4 035 $a(EXLCZ)9936315667800041 100 $a20241008d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDiscrete Stochastic Processes $eTools for Machine Learning and Data Science /$fby Nicolas Privault 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (294 pages) 225 1 $aSpringer Undergraduate Mathematics Series,$x2197-4144 311 08$a3-031-65819-1 327 $a- 1. A Summary of Markov Chains -- 2. Phase-Type Distributions -- 3. Synchronizing Automata -- 4. Random Walks and Recurrence -- 5. Cookie-Excited Random Walks -- 6. Convergence to Equilibrium -- 7. The Ising Model -- 8. Search Engines -- 9. Hidden Markov Model -- 10. Markov Decision Processes. 330 $aThis text presents selected applications of discrete-time stochastic processes that involve random interactions and algorithms, and revolve around the Markov property. It covers recurrence properties of (excited) random walks, convergence and mixing of Markov chains, distribution modeling using phase-type distributions, applications to search engines and probabilistic automata, and an introduction to the Ising model used in statistical physics. Applications to data science are also considered via hidden Markov models and Markov decision processes. A total of 32 exercises and 17 longer problems are provided with detailed solutions and cover various topics of interest, including statistical learning. 410 0$aSpringer Undergraduate Mathematics Series,$x2197-4144 606 $aStochastic processes 606 $aComputer science$xMathematics 606 $aStochastic Processes 606 $aMathematical Applications in Computer Science 615 0$aStochastic processes. 615 0$aComputer science$xMathematics. 615 14$aStochastic Processes. 615 24$aMathematical Applications in Computer Science. 676 $a006.310727 700 $aPrivault$b Nicolas$0475313 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910896193803321 996 $aDiscrete Stochastic Processes$94212338 997 $aUNINA