CIS 2700 Exam 2 (CH6-CH10)

A comprehensive vocabulary review covering Chapters 6 through 10, including E-commerce, Database Management, Data Analytics, Ethics in the Information Age, and Cybersecurity.

Electronic Commerce (E-commerce)

Any transaction conducted online through the Internet.

E-commerce Market Types (B2B, B2C, B2G, C2C)

B2B: business sells to business; B2C: business sells to consumers; B2G: business sells to government, often through contracts/RFPs; C2C: consumers sell to other consumers.

Advantages and Concerns of E-commerce

Advantages include convenience, ubiquity, flexibility, global reach, increased sales and competitiveness, and reduced transaction costs. Concerns include data theft/security, ethical practices, competitor analysis, and customer loyalty.

Personalization vs. Customization

Targets marketing messages and offerings to specific markets; customization adjusts products or services based on an individual's preferences or previous purchasing behavior.

Re-commerce

Selling used, old, or obsolete products online for cash or toward a new product.

User Generated Content (UGC)

Online text, images, videos, reviews, etc. created by unpaid users, customers, or fans. It can increase brand awareness, credibility, engagement, and influence; negative content can also harm reputation.

M-commerce

Buying, selling, and other commerce conducted through wireless mobile devices, such as mobile banking, shopping apps, Apple/Google Pay, and in-app purchases.

Location Based Services (LBS)

Services that use device location (GPS/GIS). Applications include geosocial (connecting people using location), geoadvertising (targeting ads based on location), and geoinformation (supplying facts about nearby places).

Chatbots

Automated conversational tools, often using NLP, used for FAQs, basic inquiries, 24/7 customer service, faster responses, and multilingual support. Major concerns include frustration, inaccurate answers, limited understanding, and privacy.

Database Management System (DBMS)

Software used to create, process, administer, and organize a database.

Database

A collection of related tables, relationships, and metadata storing information about people, objects, places, or events.

Elements of a Database

Entity: person, place, thing, or event being described; field: one attribute/column; record: related fields describing one instance/row; table/data file: related records; database: related tables/data files.

Relational Database

A database that organizes structured data in tables (rows/records and columns/fields), linked through shared fields and defined relationships.

Primary Key and Foreign Key

Uniquely identifies a record in its table; a foreign key is a field referencing a primary key in another table, linking the records and supporting referential integrity.

Query

A request for data from one or more tables, commonly using SQL, used to quickly filter/search, retrieve targeted records, calculate/summarize results, and automate data tasks.

Data Centralization

Storing data in one place, which results in fewer conflicting copies, more consistent records, easier maintenance, and better data integrity.

Data Integrity

The state in which data is accurate, reliable, consistent, and appropriate for the organization's needs.

Relational Database Relationship Types

One-to-one (one record corresponds to one record, e.g., driver/license); one-to-many (one corresponds to many, e.g., customer/orders); many-to-many (many correspond to many, e.g., students/courses).

Cloud Database

A database built and accessed via an Internet-hosted cloud platform that avoids dedicated local hardware, can be provider- or self-managed, and is accessible through the web or APIs.

Big Data and the 4Vs

Managing and analyzing large and complex data sets characterized by volume (amount), variety (types/formats), velocity (speed generated/processed), and veracity (reliability/uncertainty).

Data Warehouse and Data Mart

A data warehouse is a central repository integrating data from multiple sources for organizational analysis and decisions. A data mart is a smaller, department- or function-focused subset or standalone analytical store.

Data Warehouse vs. Data Mart

Both store data for analysis and decisions; a warehouse is enterprise-wide, broad, and integrated, whereas a data mart is narrower, typically focused on one department/function, and may draw from the warehouse.

Types of Data Marts

Dependent mart: extracts from an existing central warehouse (top-down); independent mart: standalone, separate from warehouse; hybrid mart: combines warehouse data with other sources.

Online Analytical Processing (OLAP)

Multidimensional analysis (data cubes/slices) used for identifying trends, complex calculations, forecasting, budgeting, planning, and what-if analysis.

Data Mining

Searching large data sets for previously hidden patterns, relationships, and likely outcomes using algorithms, statistics, machine learning, and related methods.

Database Security and Its Three Layers

Protection of DBMS and data against unauthorized use and attacks across three layers: database level (DBMS rules), access level (user permissions), and perimeter level (network/infrastructure defenses).

Data Scrubbing (Cleansing)

Detecting and correcting or removing invalid data, such as duplicate records, misspellings, wrong names/addresses, and formatting/syntax errors.

Data Analytics

Investigating data to uncover patterns or correlations and answer defined questions for decision-making.

Types of Data Analytics

Descriptive (asks what happened/is happening using past/current trends); predictive (estimates what may happen using models, statistics, ML); prescriptive (recommends the best course of action).

Key Tools for Data Analysts

Spreadsheets; databases and query languages (e.g., SQL); and data visualization tools.

Data Visualization

The graphical or structured presentation of data—such as charts, graphs, tables, and maps—to make trends and findings easier to understand.

Ethical Standards

Principles or guidelines for how people should act and make responsible decisions.

Data Collection Challenges in the Information Age

Rapid data collection and sharing threaten personal privacy and intellectual-property ownership; misinformation/deepfakes, identity theft, fraud, tracking, and bias complicate ethical use.

Privacy and Internet Privacy

The right to control what happens to personal information; Internet privacy concerns how personal information is stored, used, distributed, and displayed online.

Spyware

Software that secretly gathers information, including keystrokes, passwords, account numbers, and other confidential data.

Keystroke Loggers and Packet Analyzers

Records what users type; packet analyzers (sniffers) capture network data packets and can be abused to inspect transmitted information.

Opt-in vs. Opt-out Method

Opt-in requires the person to actively agree (e.g., check a blank box); opt-out assumes participation until the person declines (e.g., uncheck a preselected box).

Intellectual Property and Software Piracy

Legally protected human knowledge and creations; software piracy is the unauthorized use, copying, or distribution of licensed software.

Patents, Trademarks, Copyrights, and Trade Secrets

Patent: exclusive rights to an invention for a limited period; trademark: brand names, logos, and identifiers; copyright: original creative works and software; trade secret: valuable confidential business information.

Antivirus Software

Software that scans files and systems to identify, block, quarantine, or remove computer viruses and other malware.

Ethical Challenges of AI

Privacy and personal-data security, bias from training data, copyright/attribution, lack of transparency, employee overreliance, limited expertise, and rapid AI change outpacing policies.

Chief Information Security Officer (CISO)

An executive role that leads information-security strategy, risk reduction, security incidents, tools and protocols, compliance reporting, and security-related vendor oversight.

Cyber Threats

A circumstance, actor, or event that could harm systems, data, or other information assets.

Cybersecurity Vulnerabilities

A weakness or flaw in security design, software, procedures, or controls that could be exploited accidentally or deliberately.

Cybersecurity Exploits and Breaches

An exploit is a technique or code that takes advantage of a vulnerability; a breach occurs when unauthorized access to systems, data, or information is gained.

Computer Viruses, DDoS, and Trojan Horses

Virus: malicious code that executes and replicates/spreads through hosts; DDoS: many sources flood a service/network to make it unavailable; Trojan horse: seemingly legitimate program that performs malicious actions but does not self-replicate.

Social Engineering and Phishing

Manipulates people into disclosing sensitive information or granting access; phishing is social engineering through deceptive emails, messages, or sites that impersonate trusted parties.

Cybersecurity Mitigation and Components

Policies, procedures, and tools to reduce security incidents. The three components are prevention (protect beforehand), identification (detect active threats), and cure (contain/remediate impact).

Goals of Cybersecurity

Protect systems, data, and information against theft, compromise, and disruption. The three common goals are confidentiality, integrity, and availability (CIA triad).

Authentication

Verifies the identity of a user, device, or process (or origin of information) before granting appropriate access.

Internal vs. External Threats

Internal threats originate inside an organization (accidents, negligence, malicious employees); external threats originate outside (hackers, malware, phishing, DDoS).

Ransomware

Malware that encrypts/locks data or threatens disclosure and demands payment. Victims should not pay because recovery is not guaranteed, payment funds criminals, attackers may retain/copy data, and payment can invite further extortion.

Insider Threats

Risks from people with legitimate access or organizational knowledge, including employees, contractors, vendors, and other trusted parties.

Intentional vs. Unintentional Insider Threats

Intentional insider threats are deliberate harmful acts for gain or grievance; unintentional threats result from mistakes, negligence, or accidental disclosure.

Identity Verification Methods

Methods include passwords/PINs, two-factor authentication (2FA), multifactor authentication (MFA), knowledge-based questions, biometrics (fingerprints/retina), and token-based authentication.

Confidentiality and Data Availability

Keeps information from unauthorized disclosure; availability ensures authorized users can access systems and data when needed.

CIA Triad and Cybersecurity Measures

CIA triad consists of confidentiality (access controls, authentication, encryption), integrity (permissions, validation, change controls), and availability (backups, redundancy, recovery, DDoS protections). Security measures also include firewalls, IDS, IPS, and MFA.

Firewalls

A system that monitors and controls incoming and outgoing network traffic using predetermined security rules.

Intrusion Detection Systems (IDS) vs. Intrusion Prevention Systems (IPS)

IDS is a passive defense that monitors and alerts about suspicious activity; IPS is an active defense that detects and blocks or stops threats.

Supply Chain Attacks

An attack that compromises an organization through a trusted vendor, software/hardware component, service provider, or partner with access to its systems, spreading malware or enabling unauthorized access.