- What is AI & ML ?
- Impact of AI & ML in Cyber Security .
- Cyber Security Before AI & ML .
- Role of AI & ML in Cyber Security
- Why AI & ML Needed For Cyber Security .
- AI & ML Based Security Products In Market.
- SOCRadar (Security Orchestration Automation & Response)
- Challenges With AI & ML
Concept of Artificial Intelligence
- Cell body (Soma): The central part of a neuron that contains the nucleus and other cellular components.
- Dendrites: Branch-like extensions that receive signals from other neurons and transmit them to the cell body.
- Axon: A long, slender extension that carries electrical signals away from the cell body to other neurons or target cells.
- Axon terminals: The branched endings of an axon that transmit signals to other neurons or target cells.
- Synapse: The small gap between the axon terminal of one neuron and the dendrites or cell body of another neuron, where communication occurs.
- Input: Values or features provided to the perceptron.
- Weights: Strengths assigned to input features.
- Bias: Constant term added to adjust the output.
- Weighted sum: Result of multiplying inputs by weights and summing them.
- Activation function: Determines the perceptron’s output based on the weighted sum and bias.
- Output: Final result or prediction of the perceptron.
- Error: Difference between the perceptron’s output and the desired output.
- Learning rule: Algorithm that updates weights and bias to minimize error and improve accuracy.
What is AI and ML?
AI stands for Artificial Intelligence and refers to the ability of machines to perform tasks that typically require human intelligence.
ML stands for Machine Learning, a subfield of AI that focuses on developing algorithms and models that enable computers to learn from and make predictions or decisions based on data.(Researched by S.M Raza Jaffri)
Some Example :
Fraud Detection , Personalized Marketing , Autonomous Vehicle, Image & Voice Recognition, Predictive Maintenance & Cyber Security
Impact Of AI & ML In Cyber Security
Artificial Intelligence (AI) in cybersecurity eliminates time-consuming tasks done manually by human experts. It scans vast data and identifies potential threats and reduces false positives by filtering out non-threatening activities. This helps human experts focus on more critical security tasks .
Machine learning (ML) is a subfield of AI that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data. In other words, ML is a way of teaching computers to learn from data, without being explicitly programmed.
How Cyber Security Was Treated Previously
- Firewall : Non-AI firewall uses pre-defined rules to filter traffic , AI/ML firewall uses machine learning algorithms to detect and prevent advanced cyber attacks .
- Antivirus : These programs relied on signature-based detection methods to identify known viruses and malware .
- IDS : Monitor network traffic for suspicious activity and to alert security teams when a potential attack was detected.
- Vulnerability Scanning : By using traditional methods such as manual testing and automated tools based on predefined rules and signatures.
- Reactive Approach : Organizations would wait for a breach or attack to occur and then respond to it.
- Lack of Automation: This meant that security teams had to manually configure and manage security tools, and manually analyze data to identify potential threats .
- Lack of Predictive Capabilities : This meant that organizations were unable to predict or anticipate potential threats, leaving them vulnerable to attack.
- Limited Scalability : Traditional cybersecurity measures were often limited in their scalability. As the volume of data generated by organizations grew, it became increasingly difficult to analyze and manage security effectively.
- Siloed Security: Traditional security measures were often siloed and lacked integration. This meant that security teams had to use multiple tools to manage security .
Role Of AI & ML In Cyber Security
- Automated Threat Detection & Response : AI and ML, organizations can also automate the process of identifying and prioritizing threats, freeing up security teams to focus on more strategic tasks.
- Phishing Protection: AI and ML algorithms can analyze email traffic patterns to identify suspicious emails that may be part of a phishing campaign.
- Malware Detection : Machine learning algorithms can be trained to identify new and unknown malware based on their similarities to known malware.
- Vulnerability Management : AI-powered vulnerability scanners can scan networks and systems to identify potential vulnerabilities.
- Intrusion Detection : This can help organizations to detect intrusions much earlier and respond more quickly, reducing the impact of a breach.
- Endpoint security : AI and ML algorithms can help organizations to detect and respond to endpoint-based threats in real-time .
- Anomaly Detection : Machine learning can identify network traffic patterns that differ from normal usage, indicating a potential attack.
- Predictive Analytics : Machine learning can identify patterns of security incidents and predict future incidents.
- Network Security : Machine learning can analyze network traffic to detect and block distributed denial of service (DDoS) attacks.
- User Behavior Analysis : Machine learning can analyze user behavior to detect insider threats.
- False Positive : AI and ML algorithms can help organizations to reduce false positive alerts, making it easier to identify and respond to real threats.
Why AI & ML is necessary for Cyber security ?
- Volume & Velocity Of Data : AI and ML can process vast amounts of data at a high velocity, identifying potential threats in real-time .
- Complexity of Threats: AI/ML can detect complex cyber threats by analyzing data patterns that humans may miss, improving cybersecurity.
- Proactive Security : AI/ML enables proactive security by identifying potential threats before they occur, unlike traditional cybersecurity measures that are reactive and wait for an attack to happen.
- Cyber Security Skill Gap : AI/ML can help address the cybersecurity skills gap by automating routine tasks, allowing human analysts to focus on more complex tasks that require specialized skills.
- Adaptability : AI/ML is adaptable to evolving cyber threats, continuously learning and improving to provide more effective protection in real-time, unlike traditional cybersecurity measures that may struggle to keep up.
AI & ML Enabled Security Products In Market
- IBM Watson : is an artificial intelligence-powered platform designed to assist in detecting and responding to cyber threats by analyzing vast amounts of security data and providing actionable insights to security analysts.
- Palo Alto Networks Cortex XDR : Palo Alto Networks Cortex XDR is a comprehensive extended detection and response platform that combines endpoint, network, and cloud security data to detect and respond to advanced threats in real-time, offering centralized visibility and protection across the entire environment.
- Cisco Umbrella : is a cloud-based security platform that provides secure internet access and blocks malicious threats by enforcing security policies and DNS filtering, protecting users from accessing harmful websites and preventing malware infections.
- Symantec Endpoint Protection : is a comprehensive security solution that combines advanced threat prevention, detection, and response capabilities to protect endpoints from malware, ransomware, and other cyber threats, providing robust security for organizations’ devices and data.
- Fortinet Forti Analyzer : is a centralized logging and reporting solution that collects, analyzes, and correlates network and security events in real-time, providing organizations with comprehensive visibility, threat intelligence, and compliance reporting for their Fortinet security infrastructure.
- McAfee Advanced Threat Defense: Threat Defense is a comprehensive security solution that uses sandboxing technology and machine learning to detect and analyze advanced threats and malware in real-time, providing organizations with advanced threat protection and actionable insights for rapid response and remediation.
Challenges To AI & ML in Cyber Security
- While AI and ML offer significant potential for enhancing cyber security, they also come with certain challenges. Here are some key challenges associated with AI and ML in the context of cyber security:
- Integration With Existing Systems
- Regulation And Compliance
- Lack of Transparency
- Adversarial Attacks
- Limited or Bias Data
- Data Quality
- Skill Gap
Readers, This is researched & information were gathered in true spirit to share the best knowledge, I have. This is Yours Blogger & Cyber Security Specialist S.M Raza Jaffri