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- D class assignments for on-campus graduate students are available by emailing email@example.com D class assignments for DEN@Viterbi are available to students enrolled in the Distance Education Network. For more information go to den.usc.edu
Threats to information systems; technical and procedural approaches to threat mitigation; secure system design and development; mechanisms for building secure security services; risk management. Recommended preparation: Background in computer security preferred. Recommended previous courses of study include computer science, electrical engineering, computer engineering, management information systems, and/or mathematics.
|32400D||048||Lecture||3:30-6:10pm||Thursday||11 of 25||Blaine Burnham||OHE100C|
|32410D||034||Lecture||3:30-6:10pm||Thursday||4 of 20||Blaine Burnham||DEN@Viterbi|
Application of cryptography and cryptanalysis for information assurance in secure information systems. Classical and modern cryptography. Developing management solutions. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; understanding of number theory and programming background are helpful.
|32401D||048||Lecture||9:30-12:20pm||Thursday||6 of 25||Blaine Burnham||RTH217|
|32411D||034||Lecture||9:30-12:20pm||Thursday||4 of 20||Blaine Burnham||DEN@Viterbi|
Policy as the basis for all successful information system protection measures. Historical foundations of policy and transition to the digital age. Detecting policy errors, omissions and flaws. Recommended preparation: Background in computer security, or a strong willingness to learn. Recommended previous courses of studies include degrees in computer science, electrical engineering, computer engineering, management information systems, and/or mathematics.
|32402D||048||Lecture||12:30-1:50pm||Tue, Thu||10 of 25||OHE100B|
|32412D||034||Lecture||12:30-1:50pm||Tue, Thu||2 of 20||DEN@Viterbi|
Assurance as the basis for believing an information system will behave as expected. Approaches to assurance for fielding secure information systems that are fit for purpose. Recommended preparation: Prior degree in computer science, electrical engineering, computer engineering, management information systems, and/or mathematics. Some background in computer security preferred.
|32403D||048||Lecture||6:40-9:20pm||Wednesday||15 of 25||RTH109|
|32413D||034||Lecture||6:40-9:20pm||Wednesday||1 of 25||DEN@Viterbi|
Fundamentals of information security in the context of distributed systems and networks. Threat examination and application of security measures, including firewalls and intrusion detection systems. Recommended preparation: Prior degree in computer science, mathematics, computer engineering, or informatics. It is recommended that students have a working understanding of communication networks and computer architecture, and some programming facility.
|32404D||048||Lecture||6:40-9:20pm||Thursday||14 of 25||Blaine Burnham||OHE120|
|32414D||034||Lecture||6:40-9:20pm||Thursday||3 of 15||Blaine Burnham||DEN@Viterbi|
Analysis of computer security and why systems are not secure. Concepts and techniques applicable to the design of hardware and software for Trusted Systems. Recommended preparation: Prior degree in computer science, mathematics, computer engineering, or informatics; advanced knowledge of computer architecture, operating systems, and communications networks will be valuable.
|32405D||048||Lecture||2:00-3:20pm||Tue, Thu||14 of 25||Roger Schell||OHE100B|
|32415D||034||Lecture||2:00-3:20pm||Tue, Thu||3 of 25||Roger Schell||DEN@Viterbi|
The administrators role in information system testing, certification, accreditation, operation and defense from cyber attacks. Security assessment. Examination of system vulnerabilities. Policy development. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, informatics, and/or information security undergraduate program. Also, it is highly recommended that students have successfully completed coursework involving policy and network security.
The process of designing, developing and fielding secure information systems. Developing assurance evidence. Completion of a penetration analysis. Detecting architectural weaknesses. Case studies. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; moderate to intermediate understanding of the fundamentals of information assurance, and distributed systems and network security. Knowledge and skill in programming.
|32407D||048||Lecture||11:00-12:20pm||Tue, Thu||11 of 25||Roger Schell||KAP163|
Preservation, identification, extraction and documentation of computer evidence stored on a computer. Data recovery; cryptography; types of attacks; steganography; network forensics and surveillance. Recommended preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; a working understanding of number theory and some programming knowledge will be helpful.
|32408D||048||Lecture||9:30-10:50am||Tue, Thu||9 of 20||Joseph Greenfield||GFS204||PDF (482736 KB)|
Fundamentals of big data informatics techniques. Data lifecycle; the data scientist; machine learning; data mining; NoSQL databases; tools for storage/processing/analytics of large data set on clusters; in-data techniques. Recommended preparation: Basic understanding of engineering and/or technology principles; basic programming skills; background in probability, statistics, linear algebra and machine learning.
|32430D||048||Lecture||5:00-7:50pm||Monday||25 of 26||Seon Kim||KAP113|
Function and design of modern storage systems, including cloud; data management techniques; data modeling; network attached storage, clusters and data centers; relational databases; the map-reduce paradigm. Recommended preparation: INF 550 taken previously or concurrently; understanding of operating systems, networks, and databases; experience with probability, statistics, and programming.
|32431D||048||Lecture||2:00-4:50pm||Monday||37 of 39||Carl Kesselman||VKC157|
Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on Map Reduce. Case studies. Recommended preparation: INF 550, INF 551 and INF 552. Knowledge of probability, linear algebra, basic programming, and machine learning.
|32433D||048||Lecture||2:00-3:20pm||Tue, Thu||21 of 25||Ann Chervenak||KAP147|
Research leading to the master's degree; maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.
- Restriction: Registration open to the following major(s): INF
|32449D||048||Lecture||TBA||TBA||3 of 20||Roger Schell,|
Jelena Mirkovic,Craig Knoblock