This programme equips students with a critical understanding of intelligent systems for legal and administrative applications, computer forensics and cybercrime, an appreciation of the aspects of law that are most relevant to the work of IT-professionals, and a grounding in key management techniques. It is built around taught core modules on intelligent systems for legal and administrative applications, computer forensics and cybercrime, IT law for IT professionals and the principles of management. These modules are complemented by a wide range of optional modules that relate to various aspects of computing and management. The final part of the programme is an individual project that is closely linked to the main themes of the programme, containing a substantial computer science component of either a theoretical or practical nature.
Individual Project.
Lectures; tutorials; seminars; laboratory sessions; optional career planning workshops.
Assessed through: coursework; written examinations; final project report.
Aims
The aim of this module is to define, analyse and compare abstract models of computation and their associated programming paradigms.
Learning Outcomes
On successfully completing the module you should be able to demonstrate a deep knowledge and understanding of the fundamentals of formal languages and the principal models of computation and be able to work with theoretical/research-based knowledge at the forefront of the subject; judiciously apply and combine tools and techniques (frequently in novel ways) to solve a range of complex subject-specific problems with minimal direction; analyse subject material, draw inferences, and find relationships that demand that innovative thinking be engaged in and creativity be exhibited in formulating solutions; critically evaluate, exercise judgement, and compare and contrast relevant material with minimal guidance and to consider and argue for alternative, novel approaches; demonstrate a high degree of independence in managing your own learning and reflecting upon it in order to complete research tasks autonomously.
Provisional Syllabus
Introduction to abstract models of computation
Finite Automata, Push-Down Automata and applications to parsing
Turing machines
Functional calculi
Interaction-based systems
Concurrent computation
Aims
To introduce the underlying concepts and practical applications of information security engineering at a level that reflects the current state-of-the-art.
Learning Outcomes
On successful completion of this module, you should be able to understand in detail how different types of attacks on computer systems and networks are carried out; understand how such attacks can be reliably prevented or detected.
Provisional Syllabus
Minimal Linux and scripting
Physical attacks
DNS, queries and resolvers, DNS poisoning
ARP, principles, spoofing and poisoning
Network and port scanning
Network packet factory
Firewall and Intrusion Detection technologies
Web security: server side (SQL, Xpath and code injections)
Web security: client side (Phishing, XSS)
Email security
Aims
To convey an understanding of the basic elements of software measurement and testing, in particular focusing on automated test data generation and with a discussion of the relative strengths and weakness of each technique.
Learning Outcomes
To be able to describe, apply and critique several well-known software metrics. To be able to describe and apply several well-known software testing techniques. To be able to compare testing techniques and present arguments relating to the most appropriate choice thereof.
Provisional Syllabus
Measurement: Representation and modelling, Scales, Structural Measures
Unit testing: Control Flowgraph Based testing including Data Flow based testing
Finding test cases using test data generation techniques. Coverage
Slicing and static analysis techniques
Industrial applications and industrial relevance of testing
Comparative software testing techniques
Aims
To describe some techniques employed in the characterisation of agents and multi-agent systems. To provide a critical introduction to theories and methods regarding multi-agent computer systems and their component agents.
Learning Outcomes
On completion of the module, you will be expected to have acquired: A thorough, systematic understanding of key features of current theories and methods regarding multi-agent systems and their component agents; A sound appreciation of the conceptual issues involved in the characterisation of agents and their abilities; Knowledge of some of the main techniques employed in the formal characterisation of agents and multi-agent systems; An ability to critically evaluate current work in this field, and to evaluate the principal theories and methods.
Provisional Syllabus
Topics will be selected from:
Intelligent agents and their design
Knowledge in multi-agent systems
The Belief-Desire-Intention model of rational agents
Reactive and hybrid agent architectures
Agent Communication: KQML, FIPA
Auctions and Negotiations Game Theory Argumentation based Reasoning and Communication
Agent-based methodologies Applications
Aims
To introduce strategies for the design of algorithms which are efficient in terms of time and space requirements.
Learning Outcomes
On successfully completing this module you should understand the basic techniques for designing algorithms for fundamental computational problems.
Provisional Syllabus
Introduction:
Algorithms and computational complexity
Asymptotic notation
Pseudocode
Algorithm design techniques:
Divide-and-Conquer: Quicksort
Dynamic programming: matrix chain multiplication
Greedy algorithms: Huffman codes
Order statistics:
Selecting the k-th smallest element of a list - a practical method
Selecting the k-th smallest element of a list - an optimal method
Lower bound on the time complexity of computing the median
Data structures for set manipulation problems:
Fundamental operations on sets
The union-find algorithm
Partitioning
Representations of directed and undirected graphs:
Adjacency-matrix and adjacency-list representations
Breadth-first and depth-first search using adjacency lists
Computing connected components of a graph
Strongly-connected and biconnected components
Topological sorting
Algebraic algorithms:
Strassen matrix multiplication algorithm
The Four Russians boolean matrix multiplication
Winograd's algorithm
LUP decomposition of matrices
Applications of LUP decomposition
Integer and polynomial arithmetic:
Integer and polynomial multiplication and division
Greatest common divisors and Euclid's algorithm
Chinese remaindering
Aims
To understand the major concepts and problems of computational molecular biology. To appreciate the importance of these concepts in a wide diversity of practical applications. To learn which of the computational molecular biology problems have efficient algorithmic solutions and which are intractable (for example, which belong to the NP-complete complexity class). For some intractable problems, to understand how heuristic approaches to problem solutions may yield fast but only approximate solutions.
Learning Outcomes
On completing the module you should be able to design exact and efficient algorithms as well as approximation schemes and heuristics for some of the most important algorithms underlying the field of computational molecular biology, or bioinformatics. You will also be able to reason why efficient algorithms for certain problems might not be possible.
Provisional Syllabus
Basic concepts: Definitions and notions from Molecular Biology; DNA Sequence Analysis
Alignement:
Hamming and Levenshtein distances
Dynamic programming algorithm
Hirschberg's algorithm
Substitution matrices (BLOSSUM, PAM) and scoring
Seq vs Seq (Fasta, Dynamic Programming)
Seq vs Databank (BLAST)
Whole genome alignment
Multiple sequence alignment
Approximate string matching:
String matching with "don't cares"
Searching with differences
Searching with mismatches
Fragment assembly:
Shotgun sequencing
Gene detection:
Regular expressions, acceptor and donor sites
Alternative splicing
Secondary structure prediction:
RNA structure
Minimal free energy (Zuker's approach)
Protein Folding
Phylogeny:
Mapping and rearrangements
Clustering and classification techniques
Aims
The aim of this module is to define, analyse and compare abstract models of computation and their associated programming paradigms.
Learning Outcomes
On successfully completing the module you should be able to demonstrate a deep knowledge and understanding of the fundamentals of formal languages and the principal models of computation and be able to work with theoretical/research-based knowledge at the forefront of the subject; judiciously apply and combine tools and techniques (frequently in novel ways) to solve a range of complex subject-specific problems with minimal direction; analyse subject material, draw inferences, and find relationships that demand that innovative thinking be engaged in and creativity be exhibited in formulating solutions; critically evaluate, exercise judgement, and compare and contrast relevant material with minimal guidance and to consider and argue for alternative, novel approaches; demonstrate a high degree of independence in managing your own learning and reflecting upon it in order to complete research tasks autonomously.
Provisional Syllabus
Introduction to abstract models of computation
Finite Automata, Push-Down Automata and applications to parsing
Turing machines
Functional calculi
Interaction-based systems
Concurrent computation
Aims
To introduce both theoretical and practical aspects of cryptography, authentication and information security.
Learning Outcomes
On successful completion of this module, you should be able to understand the relevant mathematical techniques associated with cryptography; understand the principles of cryptographic techniques and perform implementations of selected algorithms in this area; appreciate the application of security techniques in solving real-life security problems in practical systems.
You should note that this module contains several advanced mathematical techniques. For students having a reasonable mathematical background, it should not be a problem. Explanations are given during the lectures/tutorials and examples are studied in details. Nevertheless, an in-depth understanding of these techniques is required for the examination and personal work has to be anticipated.
Provisional Syllabus
Basic terminology and concepts:
Goals of cryptography, terminology and notation players; Basic cryptographic functions
Number theory preliminaries:
Congruent modulo n, equivalent class modulo n; Integer modulo n (Zn):
Multiplicative inverse:
Relatively prime; Euler‟s theorem; Fermat‟s little theorem:
EEA (Extended Euclidean Algorithm)
CRT (Chinese Remainder Theorem)
Ciphers:
Block ciphers (substitution, transposition, product); Stream ciphers; Modes of operation (ECB, CBC, CFB, OFB)
Cryptosystems:
Block cipher: DES (Data Encryption Standard), AES (Advanced Encryption Standard)
Public-key: RSA (Rivest-Shamir-Adelman), El gamal
One-way hash function: SHA and MD5 (Message Digest 5)
Key-establishment protocols:
Symmetric and asymmetric techniques (Diffie-Hellman, Needham-Schroeder, Otway-Rees)
Public-key encryption, basic and advanced Kerberos protocols
Authentication and identification:
Concepts; Fiat-Shamir and Feige-Fiat-Shamir protocols; Zero-knowledge identification protocol
Digital signatures:
Classification; Digital signature schemes: RSA; El-Gamal; DSA (Digital Signature Algorithm) and DSS (Digital Signature Standard)
Information Security:
Password systems: number of acceptable passwords for a given password policy, exhaustive search
password ageing
Introduction to viruses, secure communication, social engineering (phishing), firewalls, buffer overflow, denial of services
Aims
The aims of this module are to study methods for handling and compressing various kinds of data, such as text, images, audio and video data and understand data compression techniques for multimedia and other applications, in particular to the Internet.
Learning Outcomes
On successfully completing this module you should have depth and systematic understanding of the principles of data compression, be able to apply different compression methods for text, image, audio, and video data, and extend their applications in different aspects of computing.
Provisional Syllabus
Introduction:
Raw multimedia data representation
Transmission medium characteristics
Data compression
Adaptive and non-adaptive methods
Lossy and lossless compression
Theoretical limits of compressibility
Text compression:
Run-length coding
Entropy coders: Huffman coding, arithmetic coding
Dictionary coding methods: LZ77, LZW
Other text compression methods: PPM
Standard text compression utilities: compress, zip
Image compression:
Monochrome and grayscale compression
Image formats: PCX, TIFF, BMP, DIB, GIF, EPS, WMF, TGA, CGM, HPGL, JPEG and PNG
GIF compression
JPEG compression (using Discrete Cosine Transform)
JPEG 2000 (using wavelets)
Video compression:
Frame-by-frame compression: M-JPEG
Inter-frame compression: MPEG
Video formats: M-JPEG, MPEG, AVI and MOV
Audio compression:
Speech coding: ADPCM, LPC
CD-quality audio: MPEG layer 3
Audio formats: WAV, VOC, SND and MIDI
Compression applications:
Computer system applications
Communication network applications
Broadcast media applications
Consumer electronics applications
Publishing applications
Entertainment applications
Healthcare applications
Managing compressed data:
Self-identifying compressed data
Error-proofing compression algorithms
Interaction between compression and other functions
Interaction between compression algorithms
Operating on compressed data
Archiving compressed data
Interactive multimedia:
Hypermedia and interactive applications, MHEG
Interactive virtual reality, VRML
Aims
To introduce various discrete optimisation problems, efficient algorithms for solving these problems, and general algorithmic techniques, which can be applied to a wide range of optimisation problems. The emphasis is put on network optimisation problems and on general optimisation techniques. To discuss applications of optimisation problems in communication systems, computer networks, manufacturing, scheduling, and resource allocation.
Learning Outcomes
On successfully completing this module you will be able to express computational problems from various application areas as (discrete) optimisation problems; will be familiar with commonly used algorithms and main algorithmic techniques for optimisation problems; will understand the principles underpinning the discussed algorithms; will be able to select an appropriate algorithm for a given optimisation problem or to develop a new algorithm based on a general algorithmic technique; will be able to analyse the running time of the developed algorithmic solutions.
Provisional Syllabus
Single-source shortest-paths problem:
Dijkstra's algorithm
The Bellman-Ford algorithm
Shortest paths in directed acyclic graphs
All-pairs shortest paths:
Johnson's algorithm
Network flow problems:
Maximum flows, Minimum-cost flows and Multicommodity flows, and their applications
Maximum matching problem and its applications to resource allocation problems
The Ford-Fulkerson method for the maximum-flow problem
The Successive-shortest-paths algorithm for the minimum-cost flow problem
Linear programming (LP):
Basic properties of LP problems
LP formulation of network flow problems
Integer programming
Computationally hard optimisation problems:
Polynomial-time problems and NP problems
NP-hard optimisation problems
Optimisation techniques for NP-hard problems:
Branch-and-bound method for finding exact solutions
Simulated annealing Genetic algorithms
Aims
To provide you with an introduction and overview to the computational aspects of parallel and distributed computing. To introduce several important parallel computing models that capture the essence of existing and proposed types of synchronous and asynchronous parallel computers. To study typical models for distributed computing. To study a few typical algorithms for each model, selected from various basic areas such as sorting, selection, graphs, matrices, numerical problems, and computational geometry. To provide an important skill for those who may work with large applications since these usually must be implemented on a parallel or distributed system, due to their memory space and speed requirements.
Learning Outcomes
On successfully completing this module you should understand a number of different models of parallel and distributed computing and understand the basic techniques for designing algorithms in these models.
Provisional Syllabus
PART I: Parallel Models and Algorithms
Models of Parallel Computation:
PRAMs; Scan Vector Model; Complexity measures
Designing Parallel Algorithms:
Basic PRAM techniques; Doubling technique; Summation trees and prefix summation
Interconnection networks:
Graph models of networks; Network properties; Searching and sorting on meshes
Sorting and Searching on PRAMs:
Merge sort; Compare-exchange sorts; Batcher‟s sorting algorithms; Computing the Median
Pointer-based algorithms:
List ranking; Tree contraction; Connected components; Minimum spanning tree; All-pairs shortest path
Geometric Algorithms:
Convex hulls; Closest pair of points; Visibility
PART II: Distributed Models and Algorithms
Concepts of distributed computation:
Termination; Failure tolerance; Network topology
Distributed Search:
Distributed BFS
Random walks; Introduction to Markov processes; Random walks (hitting time, cover time (s.t)-connectivity
Distributed networks:
Broadcasting; Robust distributed networks
Aims
To explore the practice of software architecting as applied to the development of enterprise systems. To learn about software architecture, architecture patterns, frameworks, design patterns, pattern languages, layers of change, the architecting process and the practical process of software design and implementation. Ideas are put into practical perspective through an introduction to the UML2 superstructure, enterprise component middleware.
Learning Outcomes
To be able to function as a software architect; to have an advanced knowledge of the issues, techniques and processes involved in architecture design; ability to design .NET-based enterprise software systems; expert proficiency in the UML2 superstructure to design architectures; to be able to work with and design metamodels and model transformations.
Provisional Syllabus
Components and connectors in the UML2 superstructure Architectural styles Domain-specific metamodels and case studies A metamodel for enterprise components
Semantics through the OCL
Metamodelling with the Meta Object Facility Model driven engineering and model-driven architecture
Aims
This module will cover different approaches for building internet applications and the choice of design techniques and technologies involved from a software engineering perspective, considering issues of efficiency, modularity and maintainability.
Learning Outcomes
You will gain the ability to design and implement internet-based applications in a modular manner, using appropriate languages and techniques. You will also gain the ability to specify and design medium-sized enterprise information systems using appropriate languages and techniques.
Provisional Syllabus
Model-driven architecture and application to internet applications
Client-side processing techniques: JavaScript, HTML
Server-side processing techniques: JSP, Servlets, database interfaces, sessions, connection pools
Modularity and maintainability of internet applications
Java EE architecture and patterns
Web services
Aims
This unit is devoted to algorithms processing strings and texts efficiently. These types of algorithms are used for software design in the domains of operating systems utilities, search engines on the Internet, data retrieval systems, analysis of genetic sequences, and natural language processing, for example.
Learning Outcomes
On completing the module, you should be able to design and implement exact and efficient algorithms for matching patterns in textual data, building indexes for files, and more generally for solving algorithmic problems on strings and sequences.
Provisional Syllabus
Basic concepts:
Periods in strings
Finite automata and regular expressions
Exact pattern matching:
Brute-force algorithms for pattern matching
The Knuth-Morris-Pratt algorithm
The Boyer-Moore algorithm
The Karp-Rabin algorithm
Horspool's algorithm
Multiple pattern matching:
The Aho-Corasick automaton
Two-dimensional pattern matching
Structures for indexes:
Suffix arrays
Suffix trees
Suffix automata
Regular Pattern Matching
From regular expression to automata
Simulation of deterministic automata
Aims
To provide an overall understanding of the communication model used on the Internet. To provide an in-depth understanding of the main underlying software components of the Internet. To provide an overview of the main languages used on the Internet. To provide an understanding of security threats to Internet application and the main technologies used to tackle them. To give you an understanding of the motivations behind internet technologies, suitable for you to evaluate current and future options.
Learning Outcomes
On successfully completing this module you will:
Development of Knowledge and Understanding
Have a comprehensive and detailed knowledge of the computational model underlying the Internet and recent developments in this area.
Know some of the languages used to display, represent, and manipulate information on the Internet.
Understand the security mechanisms used to protect Internet applications from unauthorised use.
Understand how protocols and languages combine to solve communication problems.
Be aware of the issues concerning privacy of personal information on the internet, and be able to apply techniques to protect privacy in any Internet applications you develop.
Cognitive/Intellectual Skills
Be able to analyse Internet-related problems using appropriate techniques and evaluating alternatives.
Be able to design solutions to such problems and evaluate the success of those solutions, possibly developing novel approaches.
Apply your knowledge of the Internet and its languages to these solutions.
Key/transferable skills
Know how to access and evaluate different sources of information regarding Internet standards, and technology standards more generally.
Have experience of solving technical, Internet-related, problems, and arguing for solutions to them.
Practical Skills
Be able to apply your knowledge of languages to meet appropriate software requirements.
Provisional Syllabus
Introduction to the Internet
Historical perspective and current context
Architecture of the Internet
Internet infrastructure
Addressing models
Web servers
Communication model
Internet reference model
TCP/IP
IPv4 and IPv6
Connection handling and flow control
HTTP and other application layer protocols
Resource location
Web languages fundamentals
HTML/XHTML
Form handling
XML
XML validation with schemas
Web service languages
Security on the Internet
Authentication methods
Digital certificates
Public and private key encryption
SSL
Introduction to Web models of information
Semantic Web
Grid computing
2:1 honours British BSc degree, or equivalent, in computer science/IT. We may lower entry qualifications for students with substantial relevant work experience. Note that English Language requirements are stricter for this programme.
Your application will be reviewed by an admissions tutor and we aim to respond to applications within four to six weeks, although this may take longer during busy and holiday periods.
Please submit a one page personal statement with your application, explaining why you wish to apply for this programme and why you feel it matches your interests, academic background, and, if relevant, your career plans. Please include transcripts of subjects taken in the relevant degrees and copies of all certificates and relevant qualifications mentioned in your application.
In addition to a wide and interesting range of taught course units, I am currently writing a thesis which has allowed me to focus on a specific area of Computer Science, developing my skills in analysis and planning.
The staff in the department have been helpful and inspiring, and are there to provide guidance and support when necessary. As well as a personal and project supervisor, Informatics students have access to extensive support networks provided by the Student's Union and Careers Service. King's proximity to the City of London makes it a great place to launch a career, and has been of great help to me in securing a position in my chosen field of Information Forensics and Cybercrime.
After my Bachelors in Information Technology, I wanted to undertake a MSc in Computer Science. I started with online research on courses offered by UK universities - looking out for a significant course at an eminent university. I learnt about "Computing, IT Law and Management" at King's College London and pursuing this course was the obvious choice for me.
With so many engineers graduating every year, I wanted to do something unique and different. I browsed through the modules offered by this course including: "IT Law for IT Professionals", "Intelligent Systems for Legal and Administrative Applications" and modules on Computer Security. I knew that these were what I needed to enhance my technical skills. The modules were the right mixture of computing and management concepts, and with the exposure to Information Technology Law, this course has moulded me into a well equipped software engineer. True to my expectations, the modules offered were one of a kind - interesting and inspiring. As a student of the Department of Informatics I also got the opportunity to attend the "Spring School on Security".
My overall experience at the University and in the Department of Informatics has been of the highest quality.