How Can Artificial Intelligence Counter Attack Cyber Security Threats?

Artificial Intelligence technology is essential to business and governmental organizations when it comes to increasing creativity and performing automated office tasks. On the other hand, business and IT leaders who are concerned about threats to cyber security are worried about artificial intelligence. They think the possibilities that hackers might use AI tools to identify ‘weak spots’ where they can exploit and attack an organization’s security system.

What Security Threats can be determined by AI Technology?

The Defense Advanced Research Projects Agency supported the Cyber Grand Challenge hacking competition in Las Vegas, where each team must play a hacking game using seven high-tech machines to search and utilize the viruses against each other’s systems. The game is called as Capture the Flag, wherein TechCrunch dispersed servers to particularly execute tasks while having a new code filled with bugs, security holes, and inefficiencies.

Carnegie Mellon succeeded from the competition because of how it develops to repair its own servers by slowing down the system and be available as Wired reported offline. A startup named ForAllSecure conducted a research at Carnegie Mellon formed a bot called “Mayhem” where it acts as a system that catches bugs faster than humans.

The potential of AI Technology as a Cyber Weapon

In an interview by New York Times with Marc Goodman a law enforcement agency adviser and author of Future Crimes said: “The thing people don’t get is that cybercrime is becoming automated and it is scaling exponentially.”

He believed that AI’s technology can be developed as cyberweapons like the popular malicious program tool known as Blackshades. Blackshades was created in 2015 by Swedish Alex Yucel who was allegedly accused and imprisoned because of selling the particular malware on the black market.

By sharing this to other neural networks, it can damage any system’s security where users can expose any confidential video or audio. IDG mentioned that tools like Blackshades might eventually use an AI to “design entire attack strategies, launch them, and calculate the associated fee.”

What makes AI Technology an effective security tool?

According to Tome Weingarten, CEO of security firm SentineIOne, there’s a possibility that an AI-driven technology in the web will become vulnerable somehow. However, AI researchers and cyber security organizations are doing a counter attack to prevent any suspicious malware.

An AI Cylance device technology develops a system which identifies neural networks and can foresee the result and control the system. Jon Miller, chief research officer at Cylance also stated that the machine’s function can identify more than 99 percent of malware at a time.

AI will resolve suspicious malware that alters with different attacks according to John Clark, a computer engineer who wrote The Independent, and the new chair of computer and information systems at the University of Sheffield in the UK. Recently, an AI system called AI2 was developed by researchers from the Massachusetts of Technology’s Computer Science and Artificial Intelligence Laboratory.

It is said that 85 percent of attacks are can be detected by AI2 and decreases the number of wrong positives by a factor of five which is estimated to be three times better than its former benchmarks. AI2 detects any suspicious activities by searching through data and group them into significant patterns. The technology utilized an approximately 3.6 billion pieces of log lines or data during its testing phase from millions of users over a period of three months.

Conclusion

AI technology and human cyber security instinct can be crucial to maintaining balance as per Wired. Humans have its limitations when it comes to doing a load of work to maximize security and neither could totally depend on machines for detecting cyber intrusions. Humans can hope that AI2 can be the multifaceted way out of the dilemma.

Deep Neural Networks For More Effective Security Systems

Airports’ security is as important as national security. People, not only within the country but across the globe are going in and out of the country. The US Department of Homeland Security with the help of Google launched a contest in creating computer algorithms that will enable airports to identify hidden items in the images produced by checkpoint body scanner.

Gathering Data Scientists and their ideas

The government aims to enhance security through advanced screening technology at airports. Kaggle which is already owned by Google and a site that holds over a million data scientists operates the said contests. The government is giving $1.5 million dollars for the contests that will run for 6 months.

Anthony Goldbloom, founder and chief executive of Kaggle revealed that the said contest is an initiative to develop a technology named as ‘deep neural networks’. These are complex mathematical systems with the ability to learn a particular task through huge data analysis. For instance, a neural network can identify what a cat is after analyzing millions of cats’ images.

Utilizing Neural Network Technology

Neural network technology is already used by Google and Facebook in several tasks. These include translation of languages, recognition of verbal commands from smartphones and identifying human faces in online images. While building algorithms were used to identify symptoms of lung cancer in CT scans in the past, neural networks are now developed to work with automated systems and read more precise body scans. This technology will not only enhance security but will also make airports services faster.

When it comes to conducting a contest, several managers and administrators find it a good idea. John W. Halinski, a security consultant considers the said contest as ‘crowdsourcing’ idea that will gather skills from different data scientist. Meanwhile, John Fortune, a program manager working in the Department of Homeland Security believes that the contest will find many people with high problem-solving skills. Moreover, Homeland Security and other agencies are in a process of discovering how neural networks can be used at security checkpoint like in the airport.

Is it really Efficient to Use Neural Networks?

Proponents of neural networks believe that it can upgrade airport security due to its capacity to learn data in a short time. Recently, Homeland Security supplied over a thousand three-dimensional body scans. But the scans are not shared and are not used for the contest. Volunteers from Transportation Security Administration assisted the working team in creating data by walking through a set of test scanners done in New Jersey laboratory.

Data gathered were used for analysis. The result shows that neural networks are efficient in doing security-related tasks like identifying hidden items. On the other hand, experts say that the technology is not perfect. According to some research, law offenders can change items or displaced the system to fool the system run by neural networks. In this case, image-recognition system powered by neural networks might fail to see some concealed items.

Nevertheless, the government has seen the potential of using this technology to help human screeners in maintain top airport security. In the near future, the government and other organizations hope that neural networks will create breakthroughs in the security system and related tasks.

Upgrading Organizations’ Security System through Neural Networks

For the past decades, there are a number of researches on how to enhance the current technology and methods to defend systems from intrusions. Today, businesses and other organizations utilize ‘anomaly detection’ and misuse or ‘signature-based detection’ to find malicious network activities and identify if these are known or new security threats. Unfortunately, these methods are still subject to progress.

When it comes to cyber security, computer network protection is crucial. This is even compulsory for companies that need to protect their employees’ and clients’ sensitive information. One breach of a company’s data security would destroy its entire structure and reputation.

Security Measures are important to every Organization

To ensure the contribution of clients in keeping the classification, morality, and accessibility of all data assets against leakages, an association’s first guard was created which characterized with all-around procedure and administration of frameworks. Security mindfulness training is design for staff to keep vital information that protects a computer from leakages and malware, reduces the dangers against system hacks, and against other emerging threats.

Information Technology (IT) rules and methods can help guard an organization against assaults for the users to be aware, however, when a malicious interruption is attempted, technology is the thing that protects the systems to ensure IT resources. With regards to information security, conventional protection procedures ought to be in layers: firewalls, Intrusion Detection Systems (IDS) and Intrusion Prevention System (IPS) can be utilized.

Dealing with inconsistency and error can work effectively in a possible way, even without the need of human communication/supervision in the procedure demonstrates by in different studies and new improvements in the field of IDPS (Intrusion Detection and Prevention System).

How neural networks work?

A neural network method can adjust to specific imperatives, learn framework attributes, perceive examples and contrast late client activities with the standard conduct; this permits settling many issues even without human mediation. Some contextual investigations stress that the utilization of Artificial Neural Networks (ANN) can set up general examples and recognize assault qualities in circumstances where rules are not known. The innovation guarantees to recognize abuse and enhance the acknowledgment of malevolent occasions with more consistency. A neural network can identify any cases of possible abuse, enabling framework overseers to ensure their whole association through advanced protection against dangers.

Developments on IDS and IPS Applications

IT experts have come to depend more on recognition and aversion advances to secure accessibility of business-basic data assets and to defend information secrecy and respectability by computer interruptions, ending up more typical and a developing test to beat.

Intrusion Detection Systems (IDS) can be delegated: Host-based or System based on the previous checking singular machines’ logs and the last breaking down the substance of system parcels; On the web or Disconnected, fit for hailing a risk progressively or sometime later to caution of an issue; Abuse based or Peculiarity based, either particularly checking a deviation from a standard conduct or contrasting exercises and ordinary, known aggressors’ conduct.

While IDS is outlined to detect attacks and ready people to any noxious occasions to research, an IPS is utilized to prevent malicious acts or square suspicious activity on the system. There are four distinct sorts of IPS:

1. Arrange based interruption counteractive action framework (NIPS) that takes a gander at the convention action to spot suspicious activity

2. Remote interruption anticipation framework (WIPS) that examines remote systems administration conventions and is so imperative in the BYOD and versatile driven world

3. Organize conduct examination (NBA) that can spot assaults that make irregular movement, for example, disseminated foreswearing of administration (DDoS) assaults, and it can utilize inconsistency based recognition and stateful convention investigation

4. Host-based interruption aversion framework (HIPS) that can be introduced on single machines and can utilize signature-based and peculiarity based strategies to distinguish issues.

The Growing Field of Man-Made Brainpower: How Neural Networks Work

Neural Networks as ‘Artificial’ Brain

Human brain capacities can be imitated by neural networks and can portray a registering procedure. According to Andy Chun from City College of Hong Kong’s Bureau of Software engineering, with the use of neural networks, we can try to mimic how nature works when it comes learning the process of several things.

Chun added that neural systems show how data is passed in the middle of the neurons to “acquire knowledge and learn new things”. The human brain is for the most part comprised of neurons, which are associated with by inward wiring called ‘neurotransmitters’.

The Essence of Neural Networks

Chun added that there are neural networks’ researches about speech recognition and generation, recognizing faces and images. Neural systems are superior to most AI technology at taking care of issues that are “perceptual in nature”, like talking and seeing. Chun believes that neural systems exceed expectations at perceiving designs.

In any case, as neural systems learn individually, engineers need to simply compose a small amount of possibly long line of codes because it is given that it would take “a huge number of lines” of code to program more smart AI frameworks. Profound learning requires a gigantic measure of figuring power, with quicker processors and more mind-boggling neural structures. All things considered, profound learning is behind Tesla’s self-driving autos, which show themselves by watching film of human drivers from everywhere throughout the world, Chun says.

What’s more, AlphaGo, the AI technology program that as of late beat the best on the planet of the Go tabletop game, took in its moves “from observing all diversions at any point played by people on the web”, according to Chun.

Neural Networks’ Influence on the government

Later on, Chun predicts that administrations will have the capacity to learn distinctive things considerably more precisely about its citizens in living, work, and wellbeing propensities and in transportation needs. Furthermore, he added that forecast will be a key advantage from this profound learning.
Specifically, he distinguishes medical advantages like analyzing and learning wellbeing examples of people and be ready to perhaps offer projects to keep nationals from becoming ill in any case.

Chun further added that the greatest obstacles are security policies and regulations. When you have a self-sufficient AI, at that point governments need to make sense of how to appoint duty, if a defective self-driving auto gets into a crash, for instance. In any case, it is critical to take note of that as governments utilize resident information to learn and remove learning.

Developments in the use of Neural Networks

Assets to urge new businesses to concentrate on profound learning are given in Hong Kong, according to Chun. Additionally, Singapore’s Administration Innovation Organization has recognized profound learning as a key concentration for 2017.

In the interim, in China, engineers of Baidu search engine had built a chatbot to help specialists in noting patients’ inquiries and proposing treatment choices. Also, a group from Northeastern College in China has lately built up a neural system that can recognize the area of flawed flags in microgrids, which are littler lattices that are associated with the fundamental power framework yet, can work without it.

In conclusion, neural networks could in future ‘spare’ the world in an unexpected way while tackling issues that influence every one of our lives such security and the possible impact of technology.