Ethical hacking involves an authorized attempt to gain unauthorized access to a computer system, application, or data. Carrying out an ethical hack involves duplicating strategies and actions of malicious attackers. This practice helps to identify security vulnerabilities which can then be resolved before a malicious attacker has the opportunity to exploit them.
What Is An Ethical Hacker?
Also known as “white hats,” ethical hackers are security experts that perform these security assessments. The proactive work they do helps to improve an organization’s security posture. With prior approval from the organization or owner of the IT asset, the mission of ethical hacking is opposite from malicious hacking.
Concepts To Know In Ethical Hacking
Hacking experts follow four key protocol concepts:
- Stay legal. Obtain proper approval before accessing and performing a security assessment.
- Define the scope. Determine the scope of the assessment so that the ethical hacker’s work remains legal and within the organization’s approved boundaries.
- Report vulnerabilities. Notify the organization of all vulnerabilities discovered during the assessment. Provide remediation advice for resolving these vulnerabilities.
- Respect data sensitivity. Depending on the data sensitivity, ethical hackers may have to agree to a non-disclosure agreement, in addition to other terms and conditions required by the assessed organization.
Ethical Hacking Versus Malicious Hacking
Ethical hackers use their knowledge to secure and improve the technology of organizations. They provide an essential service to these organizations by looking for vulnerabilities that can lead to a security breach.
An ethical hacker reports the identified vulnerabilities to the organization. Additionally, they provide remediation advice. In many cases, with the organization’s consent, the ethical hacker performs a re-test to ensure the vulnerabilities are fully resolved.
Malicious hackers intend to gain unauthorized access to a resource (the more sensitive the better) for financial gain or personal recognition. Some malicious hackers deface websites or crash backend servers for fun, reputation damage, or to cause financial loss. The methods used and vulnerabilities found remain unreported. They aren’t concerned with improving the organization’s security posture.
What skills and certifications should an ethical hacker obtain?
An ethical hacker should have a wide range of computer skills. They often specialize, becoming subject matter experts (SME) on a particular area within the ethical hacking domain.
All ethical hackers should have:
- Expertise in scripting languages.
- Proficiency in operating systems.
- A thorough knowledge of networking.
- A solid foundation in the principles of information security.
Some of the most well-known and acquired certifications include:
- EC Council: Certified Ethical Hacking Certification
- Offensive Security Certified Professional (OSCP) Certification
- CompTIA Security+
- Cisco’s CCNA Security
- SANS GIAC
What problems does hacking identify?
While assessing the security of an organization’s IT asset(s), ethical hacking aims to mimic an attacker. In doing so, they look for attack vectors against the target. The initial goal is to perform reconnaissance, gaining as much information as possible.
Once the ethical hacker gathers enough information, they use it to look for vulnerabilities against the asset. They perform this assessment with a combination of automated and manual testing. Even sophisticated systems may have complex countermeasure technologies which may be vulnerable.
They don’t stop at uncovering vulnerabilities. Ethical hackers use exploits against the vulnerabilities to prove how a malicious attacker could exploit it.
Some of the most common vulnerabilities discovered by ethical hackers include:
- Injection attacks
- Broken authentication
- Security misconfigurations
- Use of components with known vulnerabilities
- Sensitive data exposure
After the testing period, ethical hackers prepare a detailed report. This documentation includes steps to compromise the discovered vulnerabilities and steps to patch or mitigate them.
What Is Cognitive Computing?
Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. The phrase is closely associated with IBM’s cognitive computer system, Watson.
Computers are faster than humans at processing and calculating, but they have yet to master some tasks, such as understanding natural language and recognizing objects in an image. Cognitive computing is an attempt to have computers mimic the way a human brain works.
To accomplish this, cognitive computing makes use of artificial intelligence (AI) and other underlying technologies, including the following:
- Expert systems
- Neural networks
- Machine learning
- Deep learning
- Natural language processing (NLP)
- Speech recognition
- Object recognition
Cognitive computing uses these processes in conjunction with self-learning algorithms, data analysis, and pattern recognition to teach computing systems. The learning technology can be used for speech recognition, sentiment analysis, risk assessments, face detection, and more. In addition, it is particularly useful in fields such as healthcare, banking, finance, and retail.
How Does Cognitive Computing Work?
Systems used in the cognitive sciences combine data from various sources while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition, and NLP to mimic human intelligence.
Using computer systems to solve the types of problems that humans are typically tasked with requires vast amounts of structured and unstructured data fed to machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and the way they process data. They become capable of anticipating new problems and modeling possible solutions.
For example, by storing thousands of pictures of dogs in a database, an AI system can be taught how to identify pictures of dogs. The more data a system is exposed to, the more it is able to learn and the more accurate it becomes over time.
To achieve those capabilities, cognitive computing systems must have the following attributes:
- Adaptive. These systems must be flexible enough to learn as information changes and as goals evolve. They must digest dynamic data in real time and adjust as the data and environment change.
- Interactive. Human-computer interaction is a critical component of cognitive systems. Users must be able to interact with cognitive machines and define their needs as those needs change. The technologies must also be able to interact with other processors, devices, and cloud platforms.
- Iterative and stateful. Cognitive computing technologies can ask questions and pull in additional data to identify or clarify a problem. They must be stateful in that they keep information about similar situations that have previously occurred.
- Contextual. Understanding context is critical in thought processes. Cognitive systems must understand, identify and mine contextual data, such as syntax, time, location, domain, requirements, and a user’s profile, tasks, and goals. The systems may draw on multiple sources of information, including structured and unstructured data and visual, auditory, and sensor data.
Examples and applications of cognitive computing
Cognitive computing systems are typically used to accomplish tasks that require the parsing of large amounts of data. For example, in computer science, cognitive computing aids in big data analytics, identifying trends and patterns, understanding human language, and interacting with customers.
It is also used alongside HTML and CSS to make websites and web applications. The market for application development in 2022 is huge. Freelancing as a developer or pursuing a full-time job are lucrative options for anyone dedicated and determined to learn programming skills.
- Comment a Lot
Using comments is important when learning any programming language. Comments help make your code more readable and understandable.
In the beginning, you will frequently forget what certain syntax means or what a particular line you wrote does in your code. To save yourself some headaches, write comments about any line that you feel you might forget for later reference. In fact, in the beginning, you should be commenting more than actually writing code.
With time, your grasp of the language will increase and your need to comment on your code will decrease. Eventually, your code will have very few comments or not at all.
- Do Programming Exercises
- Leverage Multiple Resources
- Always Make Documentation for Your Projects
Documentation can include a ‘How to’ that tells how to run the project. You can also include ‘Read Me’ files to tell you what the project does.
The point is to make documentation of all your projects. In the beginning, you will be making really simple documentation that only gives basic information. Later on, you will be adding more and more details.
Documentation will improve your understanding of what you have done. Beginners often follow tutorials and just code along with them. Unfortunately, such practices usually end up with beginners forgetting what they’ve done and not being able to understand their code.
Four Ways AI Can Improve Your Next Meeting
It may not be noticeable to most, but AI is now rooted in many aspects of our lives. From voice assistants to the cars we drive, to social media and shopping – AI is integrated into a multitude of everyday processes.
It should be of little surprise that AI is also becoming heavily embedded in our businesses. And while some people feel uncomfortable about this intersection of human and machine, it truly offers an abundance of transformative opportunities.
Here are four reasons why AI will continue to be important today and in the future:
- Automated note-taking allows brainstorms to go full speed
The days of being the meeting scribe and not absorbing what’s been said around you are over. Automated note-taking and accurate meeting transcripts are one of the simplest ways AI can help free up meeting attendees to focus on the discussion taking place.
Using this software means that transcripts can be searched for important keywords and ideas, allowing participants to fully absorb details after the meeting has concluded. Giving everyone at the meeting the ability to participate without the burden of constant note-taking fosters a lively and uninhibited discussion, encouraging a seamless flow of ideas.
- AI-powered action items, agenda updates, and deadline management
AI technology is founded on rules-based responses to decisions, meaning it can be taught to recognize keywords. Organizers can plug in important words such as “follow up” or “action item” and the AI can recognize them and react for easier sharing and review after a meeting.
In addition, AI can help to record deadlines and, if programmed to do so, could send out reminders as deadlines approach. With something like Natural Language Processing (NLP) embedded, AI can also know which parts of the meeting are most important, based on vocal tones, and can automatically record and share those parts with attendees, ensuring that none of the actions are forgotten.
- Automated capture of nonverbal cues
We all know those golden moments during a meeting where ideas are born and everyone reacts in a positive way – but they can be hard to identify, particularly if you’re engaging with remote workers on the phone or via video conference.
Wouldn’t it be great if AI was able to more easily recognize and record those moments, because they are generally identified by nonverbal cues such as facial expressions, nods, laughter, or peaks in the audio when everyone has that aha moment? A human note-taker may not be able to accurately capture this, but AI may be able to.
- Improved overall efficiency prevents meetings from dragging on
Everyone has experienced a meeting that seems to drag on endlessly, or watched co-workers talk in circles. This can happen when people are not paying attention because they’re scribbling on notepads and typing on laptops, bringing up topics that were already discussed. This is what turns meetings into chores instead of the energizing moments of team collaboration they are meant to be.
When AI removes the more mundane aspects of a meeting like scheduling or taking attendance, attendees can move through administrative tasks and housekeeping items rapidly, knowing the AI will have it all recorded for later reference, and move into free-flowing exchanges of ideas.
And for those routine meetings that occur frequently and don’t always entail a major brainstorming, AI also facilitates effective and concise meetings, so everyone can get into the meeting quickly, be productive with the time set out, and then get back into more inspiring work.
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