Course Gaps at Kaduna State University

Review of data structures; linear data structures; hashing; trees; graphs; recursion; Complexity classes; empirical measurements of performance; time and space tradeoffs analysis; Algorithmic strategies: Brute-force algorithms; greedy algorithms; divide-and-conquer; backtracking; branch-and-bound; minimum spanning tree; heuristics; pattern matching and string/text algorithms; numerical approximation algorithms; Tractable and intractable problems

Dynamics of microbial populations in air; water and soil; Distribution and survival of aerial and aquatic microorganisms; Biogeochemical cycling of nutrients and chemical elements; Application of microbial systems in water purification; waste management and pollution control; Recent advances in biotransformation and bioremediation

Basic algorithmic analysis. Asymptotic analysis of Upper and average complexity bounds. Standard Complexity Classes. Time and space trade-offs in analysis recursive algorithms. Algorithmic Strategies. Fundamental computing algorithms. Numerical algorithms. Sequential and Binary search algorithms. Sorting algorithms, Binary Search trees. Hash tables. Graphs and their representation.

Overview of Artificial Intelligence. History of AI. Goals of AI. AI Technique. Types of AI. Branches and applications of AI. Advantages and Disadvantages. Introduction to Intelligent Agents. Agent Performance, Examples of Agents, Agent Faculties, Rationality, Agent Environment. Agent Architectures. Search. General Classes of AI Search Algorithm Problems. Problem Solving by Search. Types of AI Search Techniques and Strategies. Introduction to the types of problems and techniques in AI. Problem-Solving methods. Major structures used in AI programmes. Knowledge Representation. KR and Reasoning Challenges. KR Languages. Knowledge representation techniques such as predicate logic, non-monotonic logic, and probabilistic reasoning. Semantic Network - types of relationships, semantic network inheritance, types and components. Introduction to Frames. Natural Language Processing (NLP). Introduction to natural language understanding and various syntactic and semantic structures. Introduction to Expert Systems - characteristics, components, types, requirements, technology, development. Programming Languages for AI. Introduction to computer image recognition. Lab work: Group practical in (i) Turing test practical - Students can act out their own version of the Turing test (ii) Facial recognition practical to aid in teaching students how machine learning works with students simulating a facial recognition algorithm. Practical applications of NLP in groups – (i) Question Answering focuses on building systems that automatically answer the questions asked by humans in a natural language (ii) Spam detection application for detecting unwanted e-mails getting to a user's inbox (iii) Sentiment analysis/opinion mining should be used on the web to analyse the attitude, behaviour, and emotional state of the sender, implemented through a combination of NLP and statistics (iv) Practical exercise of machine translation used to translate text or speech from one natural language to another natural language such as the Google Translator (v) Developing a model to provide word processor software for the spelling correction (vi) Developing a model for speech recognition for converting spoken words into text (vii) Implementing a Chatbot to provide the staff/student's chat services. OR Group Practical exercise on agents and its environment using simulation of a colony of ants foraging for food; model simulating a message between agents; model simulating the flocking behaviour of birds; model to apply standard search algorithm to the classic search problem of missionaries and cannibals, and how to use communicating agents for searching networks. Some computer AI animation exercises for any branch of AI. Practical exercise on simple robots coupling and programming. Group project of building a lawn robot for trimming grasses, or any simple design and implementation of robotics.

Introduction to biometrics and digital image processing. Matlab in biometric image/signal processing. Biometric algorithms and systems with emphasis on face, fingerprint, eyes (iris), speech (voice). Automated biometric identification multimodal biometrics. Biometric data: raw data, template data, and data methods. Biometric matching basics: biometric authentication, enrolment, correct user, and incorrect user. Match threshold and matching performance. Setting a threshold. Biometric authentication: matching data, ground truth, calculating errors rates and graphs. Biometric data: Storage of biometric data elements, transactions, errors and quality upgrades. Data security and integrity. Privacy issues and other aspects of biometrics. Applications of biometrics and future trends. Challenging issues: security strength and recognition rates. Alternatives of passwords and smart cards. Lab work: Practical exercise on biometric capture, image processing, matching threshold and performance. Learn the practical aspect of automated biometric identification of multimodal, authentication and calculation of error rates. Work on biometric algorithms, privacy and security of stored biometric data.

Introduction to cloud computing, cloud computing vendors, cloud computing threats, cloud reference model. Cloud-enabling technologies. Services, Service-Oriented Architectures. Cloud service models. Cloud deployment models. Introduction to data centres: servers, data storage, networking and virtualisation. Data centre networking. Introduction to server virtualisation software: VMware VSphere. Virtual machine management: configuration, placement and resource allocation. Power efficiency in virtual data centres. Fault tolerance in virtual data centres. The cloud cube model and security for cloud computing. Security in the cloud. Cloud threats, threat mitigation and security risks. Real world issues with cloud computing. Cloud security alliance. National Institute of Standards and Technology, Information Assurance Framework. Cloud audit. Cloud management audit/assurance programme, Cloud business continuity planning. Building a cloud. Architectural best practices: Designing for the cloud. Economics of the cloud. Cloud strategy. Cloud standards and the future. Security of the cloud.

Introduction to cryptography, symmetric and asymmetric cryptography, key management, and encryption algorithms. Introduction to simple cryptosystems. Cryptanalysis. Stream ciphers, Block ciphers and Feistel ciphers. Multiple encryption. Hash functions. Data integrity, authentication, and perfect secrecy. Public-key cryptography and discrete algorithms-ELGamal cryptography. Algorithms for the discrete logarithm problem. Algorithmic number theory. Probabilistic primality testing. Security of ELGamal and RSA Encryption, and RSA Key Generation. Discrete logarithm cryptographic schemes. Conventional and public-key cryptography. Selected cryptosystems, including Data Encryption Standard (DES) and Rivest- Shamir-Adleman (RSA) algorithm. AES encryption algorithm, a symmetric 128-bit block data encryption technique. PKI, SSL, and VPN. Digital signatures, pseudo-random number generation, cryptographic protocols and cryptanalytic techniques. Use of protocols, hashing and certificates and certificate authorities. Policies, procedures, and methods for the proper use of cryptography in secure systems. Applications of cryptography to signal. Lab work: Practical exercise on writing cryptography algorithms. Work on cryptographic techniques. Practice cryptanalysis of cipher and how to use protocols. Understand hash functions and learn how to hash, produce secured digital signatures and certificates. Learn the procedures and methods for the proper use of cryptography in secure systems. Practice primality testing. Practical assignments on ELGamal, DES and RSA encryption security, generation of RSA key and discrete logarithm cryptographic schemes. 400 Level

Techniques for detecting, responding to and defeating organised cybercrimes and cyberwar activities. Analysing successful and unsuccessful advanced persistent threats and malware campaigns. Analyse divergent national and international policies for combating cyber terrorism and terrorist tactics worldwide. Understanding Cyber threat intelligence - defining threats, Understanding risk, Cyber threat intelligence and its rule, Expectations of organisations and analysts, and indicators of compromise. Tactical threat intelligence. Role of a tactical threat intelligence analyst, expected skills and tradecraft. The Kill Chain and Intrusion Analysis. Indicator lifecycle. Introduction to operational threat intelligence - Role of an operational threat intelligence analyst, Need for information sharing and peers. Models and methods for managing intelligence, campaigns and threat actors. Introduction to strategic threat Intelligence - role of a strategic threat intelligence analyst. Threat modelling, Organisational change and security posturing. Event recording and incident sharing. Evolution of counterterrorism and cyber conflict.

Introduction to digital forensics, digital evidence, and increasing awareness of digital evidence. Challenging aspects of digital evidence. Best practices in securing, processing, acquiring, examining and reporting on digital evidence. Cyber trail and challenging aspects of the cyber trail. Brief history of computer crime and cybercrime investigation. Cyber auditing. Evolution of investigative tools. Language of computer crime investigation. The role of computers in crime, technology and law, jurisdiction, pornography and obscenity, child pornography, privacy, copyrights and the “theft” of digital intellectual property. The investigative process and investigative reconstruction, with digital evidence. Examine techniques and tools used by computer forensics investigations such as acquisition, preservation, recovery, and analysis of evidence obtained from portable and stationary computer storage devices, personal digital assistants (PDAs), and cell phones. Current technologies and methods as well as leading edge techniques with practical based exercises/projects and research opportunities. Lab work: Practical exercises on how to make use of various techniques and tools for computer forensics investigations and cyber trail during cybercrime investigations. Practice cyber auditing skills. Work on applying the best practices in securing, processing, acquiring, examining and reporting on digital evidence with current technologies and methods in forensics investigation.