#cybersecurity in defense

[amrutmnm]

World Defense Budgets: Key Players and Regional Spending Trends

The World Defense Budget Analysis Market size is estimated to be USD 2,004.7 billion in 2023 and is expected to reach USD 2,546.9 billion by 2028 at a CAGR of 4.90% from 2023 to 2028. The world defense budget has experienced substantial growth over the years, reflecting the global focus on national security, military modernization, and the evolving nature of security threats. Factors driving the growth of the defense budget include geopolitical tensions, regional conflicts, and the need to address emerging challenges such as cyber warfare and terrorism. Technological advancements and the race for military superiority have also fueled increased defense spending.

Governments across the globe are investing in advanced weaponry, modernizing their armed forces, and adopting cutting-edge technologies like artificial intelligence, unmanned systems, and cyber capabilities. Furthermore, economic growth in certain regions has given governments the financial capacity to allocate more resources to defense. Rising defense budgets are also attributed to the desire to maintain military readiness, support global military operations, and safeguard national interests. However, challenges such as budget constraints, competing domestic priorities, and public scrutiny over defense spending remain. Nevertheless, the growth of the World Defense Budget Analysis Industry is expected to continue as nations navigate the evolving security landscape and strive to ensure their defense capabilities are robust, agile, and well-equipped to address both conventional and unconventional threats.

[cypheroxide]

The Hacker's Guidebook:

Aspiring hackers! I’ve created a guide covering core cybersecurity concepts new hackers should master before tools. I break down networking, OS internals, & hacking tactics. Recognize hacking as lifelong journey—arm yourself with the basics!

Core Concepts for Budding Cybersecurity Enthusiasts The Building Blocks of Ethical Hacking So you want to become an ethical hacker and enter the exciting world of cybersecurity. That’s awesome! However, before you dive headfirst into firing up Kali Linux and hacking everything in sight, it’s vital to build up your foundational knowledge across several InfoSec domains. Mastering the fundamentals…

[jcmarchi]

Toward a code-breaking quantum computer

New Post has been published on https://thedigitalinsider.com/toward-a-code-breaking-quantum-computer/

Toward a code-breaking quantum computer

The most recent email you sent was likely encrypted using a tried-and-true method that relies on the idea that even the fastest computer would be unable to efficiently break a gigantic number into factors.

Quantum computers, on the other hand, promise to rapidly crack complex cryptographic systems that a classical computer might never be able to unravel. This promise is based on a quantum factoring algorithm proposed in 1994 by Peter Shor, who is now a professor at MIT.

But while researchers have taken great strides in the last 30 years, scientists have yet to build a quantum computer powerful enough to run Shor’s algorithm.

As some researchers work to build larger quantum computers, others have been trying to improve Shor’s algorithm so it could run on a smaller quantum circuit. About a year ago, New York University computer scientist Oded Regev proposed a major theoretical improvement. His algorithm could run faster, but the circuit would require more memory.

Building off those results, MIT researchers have proposed a best-of-both-worlds approach that combines the speed of Regev’s algorithm with the memory-efficiency of Shor’s. This new algorithm is as fast as Regev’s, requires fewer quantum building blocks known as qubits, and has a higher tolerance to quantum noise, which could make it more feasible to implement in practice.

In the long run, this new algorithm could inform the development of novel encryption methods that can withstand the code-breaking power of quantum computers.

“If large-scale quantum computers ever get built, then factoring is toast and we have to find something else to use for cryptography. But how real is this threat? Can we make quantum factoring practical? Our work could potentially bring us one step closer to a practical implementation,” says Vinod Vaikuntanathan, the Ford Foundation Professor of Engineering, a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of a paper describing the algorithm.

The paper’s lead author is Seyoon Ragavan, a graduate student in the MIT Department of Electrical Engineering and Computer Science. The research will be presented at the 2024 International Cryptology Conference.

Cracking cryptography

To securely transmit messages over the internet, service providers like email clients and messaging apps typically rely on RSA, an encryption scheme invented by MIT researchers Ron Rivest, Adi Shamir, and Leonard Adleman in the 1970s (hence the name “RSA”). The system is based on the idea that factoring a 2,048-bit integer (a number with 617 digits) is too hard for a computer to do in a reasonable amount of time.

That idea was flipped on its head in 1994 when Shor, then working at Bell Labs, introduced an algorithm which proved that a quantum computer could factor quickly enough to break RSA cryptography.

“That was a turning point. But in 1994, nobody knew how to build a large enough quantum computer. And we’re still pretty far from there. Some people wonder if they will ever be built,” says Vaikuntanathan.

It is estimated that a quantum computer would need about 20 million qubits to run Shor’s algorithm. Right now, the largest quantum computers have around 1,100 qubits.

A quantum computer performs computations using quantum circuits, just like a classical computer uses classical circuits. Each quantum circuit is composed of a series of operations known as quantum gates. These quantum gates utilize qubits, which are the smallest building blocks of a quantum computer, to perform calculations.

But quantum gates introduce noise, so having fewer gates would improve a machine’s performance. Researchers have been striving to enhance Shor’s algorithm so it could be run on a smaller circuit with fewer quantum gates.

That is precisely what Regev did with the circuit he proposed a year ago.

“That was big news because it was the first real improvement to Shor’s circuit from 1994,” Vaikuntanathan says.

The quantum circuit Shor proposed has a size proportional to the square of the number being factored. That means if one were to factor a 2,048-bit integer, the circuit would need millions of gates.

Regev’s circuit requires significantly fewer quantum gates, but it needs many more qubits to provide enough memory. This presents a new problem.

“In a sense, some types of qubits are like apples or oranges. If you keep them around, they decay over time. You want to minimize the number of qubits you need to keep around,” explains Vaikuntanathan.

He heard Regev speak about his results at a workshop last August. At the end of his talk, Regev posed a question: Could someone improve his circuit so it needs fewer qubits? Vaikuntanathan and Ragavan took up that question.

Quantum ping-pong

To factor a very large number, a quantum circuit would need to run many times, performing operations that involve computing powers, like 2 to the power of 100.

But computing such large powers is costly and difficult to perform on a quantum computer, since quantum computers can only perform reversible operations. Squaring a number is not a reversible operation, so each time a number is squared, more quantum memory must be added to compute the next square.

The MIT researchers found a clever way to compute exponents using a series of Fibonacci numbers that requires simple multiplication, which is reversible, rather than squaring. Their method needs just two quantum memory units to compute any exponent.

“It is kind of like a ping-pong game, where we start with a number and then bounce back and forth, multiplying between two quantum memory registers,” Vaikuntanathan adds.

They also tackled the challenge of error correction. The circuits proposed by Shor and Regev require every quantum operation to be correct for their algorithm to work, Vaikuntanathan says. But error-free quantum gates would be infeasible on a real machine.

They overcame this problem using a technique to filter out corrupt results and only process the right ones.

The end-result is a circuit that is significantly more memory-efficient. Plus, their error correction technique would make the algorithm more practical to deploy.

“The authors resolve the two most important bottlenecks in the earlier quantum factoring algorithm. Although still not immediately practical, their work brings quantum factoring algorithms closer to reality,” adds Regev.

In the future, the researchers hope to make their algorithm even more efficient and, someday, use it to test factoring on a real quantum circuit.

“The elephant-in-the-room question after this work is: Does it actually bring us closer to breaking RSA cryptography? That is not clear just yet; these improvements currently only kick in when the integers are much larger than 2,048 bits. Can we push this algorithm and make it more feasible than Shor’s even for 2,048-bit integers?” says Ragavan.

This work is funded by an Akamai Presidential Fellowship, the U.S. Defense Advanced Research Projects Agency, the National Science Foundation, the MIT-IBM Watson AI Lab, a Thornton Family Faculty Research Innovation Fellowship, and a Simons Investigator Award.

[nationallawreview]

The Cybersecurity Maturity Model Certification (CMMC) Program – Defense Contractors Must Rapidly Prepare and Implement

The Department of Defense (DoD) has officially launched the Cybersecurity Maturity Model Certification (CMMC) Program, which requires federal contractors and subcontractors across the Defense Industrial Base (DIB) to comply with strict cybersecurity standards. The CMMC program aims to protect Federal Contract Information (FCI) and Controlled Unclassified Information (CUI) in DoD contracts from…

[trendynewsnow]

Japan and EU Establish Security Partnership Amid Global Tensions

Japan and EU Forge New Security Partnership Amid Rising Global Tensions On Friday, Japan and the European Union (EU) unveiled a comprehensive security partnership aimed at enhancing joint military drills and fostering defense industry collaboration. This initiative comes in response to escalating tensions involving China, North Korea, and Russia’s increased military maneuvers in the Indo-Pacific…

[bytesandideas]

Playing the NOVA Cybersecurity Lab game was a real eye-opener for me about the challenges and strategies involved in staying safe online. As I went through the different levels, I started to understand that cybersecurity isn’t just about having strong passwords or avoiding shady links—it’s about building a layered defense to keep personal and organizational data safe.

The game took me through a coding challenge, which showed me how even small programming errors can be exploited by attackers, and a password-cracking challenge that made me realize how quickly weak passwords can be cracked. I also learned about social engineering, where attackers try to trick people into giving away sensitive information. This made me see how much of cybersecurity is actually about human psychology and awareness, not just technical skills.

As I earned stars and coins to “buy” defenses for my fictional company, it became clear how important it is to invest in multiple security measures rather than relying on a single line of defense. Overall, the game gave me a practical and more intuitive understanding of cybersecurity, and I now feel more prepared to spot potential threats in my digital life.

[bob3160]

How AI is Fighting Cybercrime

[theseoblogspace]

10 Essential Security Measures to Protect Your Website from Hackers and Malware

In today’s digital world, keeping your website safe is a big worry for Aussie businesses. Sadly, Australia loses heaps of money every year fixing the damage from hackers1. With 56% of WordPress site attacks coming from old plugins2, it’s clear we need to act fast to protect our online stuff and customer info. This guide gives you 10 key steps to keep your website safe from malware and cyber…

[tecnolynxglobal]

Defending the Digital Realm: The Critical Role of Cybersecurity

In today’s highly connected digital age, cybersecurity has shifted from a secondary concern to a top priority. As cyber threats continue to rise in both volume and complexity, individuals, businesses, and governments alike face unprecedented risks, including severe data breaches, financial losses, and damage to their reputations. The need for robust cybersecurity practices has never been more pressing. With that in mind, here are some essential strategies to help safeguard your digital assets.

To start, encouraging the creation of strong passwords can motivate readers to take that critical first step in securing their accounts. A combination of letters, numbers, and symbols creates a powerful first line of defense against unauthorized access.

Phishing scams are another significant threat in today’s digital landscape. These schemes are designed to trick people into sharing sensitive information. By helping readers recognize the warning signs of these increasingly advanced scams, you can equip them to navigate the digital world more safely.

Additionally, emphasizing the importance of regular software updates and reliable antivirus protection is vital. These measures help close security gaps and bolster defenses against potential cyber threats. By stressing the necessity of strong passwords, a keen awareness of phishing scams, and the habit of keeping software up to date, we can significantly enhance our digital security.

Ultimately, knowledge and proactive measures are our best tools for safely navigating the ever-evolving cyber landscape. Remember, vigilance and awareness are essential to staying secure in today’s digital world.

[jcmarchi]

Unmasking Bias in Artificial Intelligence: Challenges and Solutions

New Post has been published on https://thedigitalinsider.com/unmasking-bias-in-artificial-intelligence-challenges-and-solutions/

Unmasking Bias in Artificial Intelligence: Challenges and Solutions

The recent advancement of generative AI has seen an accompanying boom in enterprise applications across industries, including finance, healthcare, transportation. The development of this technology will also lead to other emerging tech such as cybersecurity defense technologies, quantum computing advancements, and breakthrough wireless communication techniques. However, this explosion of next generation technologies comes with its own set of challenges.

For example, the adoption of AI may allow for more sophisticated cyberattacks, memory and storage bottlenecks due to the increase of compute power and ethical concerns of biases presented by AI models. The good news is that NTT Research has proposed a way to overcome bias in deep neural networks (DNNs), a type of artificial intelligence.

This research is a significant breakthrough given that non-biased AI models will contribute to hiring, the criminal justice system and healthcare when they are not influenced by characteristics such as race, gender. In the future discrimination has the potential to be eliminated by using these kinds of automated systems, thus improving industry wide DE&I business initiatives. Lastly AI models with non-biased results will improve productivity and reduce the time it takes to complete these tasks. However, few businesses have been forced to halt their AI generated programs due to the technology’s biased solutions.

For example, Amazon discontinued the use of a hiring algorithm when it discovered that the algorithm exhibited a preference for applicants who used words like “executed” or “captured” more frequently, which were more prevalent in men’s resumes. Another glaring example of bias comes from Joy Buolamwini, one of the most influential people in AI in 2023 according to TIME, in collaboration with Timnit Gebru at MIT, revealed that facial analysis technologies demonstrated higher error rates when assessing minorities, particularly minority women, potentially due to inadequately representative training data.

Recently DNNs have become pervasive in science, engineering and business, and even in popular applications, but they sometimes rely on spurious attributes that may convey bias. According to an MIT study over the past few years, scientists have developed deep neural networks capable of analyzing vast quantities of inputs, including sounds and images. These networks can identify shared characteristics, enabling them to classify target words or objects. As of now, these models stand at the forefront of the field as the primary models for replicating biological sensory systems.

NTT Research Senior Scientist and Associate at the Harvard University Center for Brain Science Hidenori Tanaka and three other scientists proposed overcoming the limitations of naive fine-tuning, the status quo method of reducing a DNN’s errors or “loss,” with a new algorithm that reduces a model’s reliance on bias-prone attributes.

They studied neural network’s loss landscapes through the lens of mode connectivity, the observation that minimizers of neural networks retrieved via training on a dataset are connected via simple paths of low loss. Specifically, they asked the following question: are minimizers that rely on different mechanisms for making their predictions connected via simple paths of low loss?

They discovered that Naïve fine-tuning is unable to fundamentally alter the decision-making mechanism of a model as it requires moving to a different valley on the loss landscape. Instead, you need to drive the model over the barriers separating the “sinks” or “valleys” of low loss. The authors call this corrective algorithm Connectivity-Based Fine-Tuning (CBFT).

Prior to this development, a DNN, which classifies images such as a fish (an illustration used in this study) used both the object shape and background as input parameters for prediction. Its loss-minimizing paths would therefore operate in mechanistically dissimilar modes: one relying on the legitimate attribute of shape, and the other on the spurious attribute of background color. As such, these modes would lack linear connectivity, or a simple path of low loss.

The research team understands mechanistic lens on mode connectivity by considering two sets of parameters that minimize loss using backgrounds and object shapes as the input attributes for prediction, respectively. And then asked themselves, are such mechanistically dissimilar minimizers connected via paths of low loss in the landscape? Does the dissimilarity of these mechanisms affect the simplicity of their connectivity paths? Can we exploit this connectivity to switch between minimizers that use our desired mechanisms?

In other words, deep neural networks, depending on what they’ve picked up during training on a particular dataset, can behave very differently when you test them on another dataset. The team’s proposal boiled down to the concept of shared similarities. It builds upon the previous idea of mode connectivity but with a twist – it considers how similar mechanisms work. Their research led to the following eye-opening discoveries:

minimizers that have different mechanisms can be connected in a rather complex, non-linear way

when two minimizers are linearly connected, it’s closely tied to how similar their models are in terms of mechanisms

simple fine-tuning might not be enough to get rid of unwanted features picked up during earlier training

if you find regions that are linearly disconnected in the landscape, you can make efficient changes to a model’s inner workings.

While this research is a major step in harnessing the full potential of AI, the ethical concerns around AI may still be an upward battle. Technologists and researchers are working to combat other ethical weaknesses in AI and other large language models such as privacy, autonomy, liability.

AI can be used to collect and process vast amounts of personal data. The unauthorized or unethical use of this data can compromise individuals’ privacy, leading to concerns about surveillance, data breaches and identity theft. AI can also pose a threat when it comes to the liability of their autonomous applications such as self-driving cars. Establishing legal frameworks and ethical standards for accountability and liability will be essential in the coming years.

In conclusion, the rapid growth of generative AI technology holds promise for various industries, from finance and healthcare to transportation. Despite these promising developments, the ethical concerns surrounding AI remain substantial. As we navigate this transformative era of AI, it is vital for technologists, researchers and policymakers to work together to establish legal frameworks and ethical standards that will ensure the responsible and beneficial use of AI technology in the years to come. Scientists at NTT Research and the University of Michigan are one step ahead of the game with their proposal for an algorithm that could potentially eliminate biases in AI.

[nationallawreview]

Department of Defense Issues Final CMMC Rule

On October 11, 2024, the Department of Defense (“DoD”) issued the first part of its final rule establishing the Cybersecurity Maturity Model Certification (“CMMC”) program. As expected, the final rule requires companies entrusted with national security information to implement cybersecurity standards at progressively advanced levels, (CMMC level 1, CMMC level 2, and CMMC level 3) depending on the…

[code-of-conflict]

AI, Cybersecurity, and National Sovereignty

Introduction: The Role of AI in Cybersecurity

As artificial intelligence (AI) becomes integral to national security, cyber threats increasingly exploit AI-driven vulnerabilities. Both India and China face the challenge of securing their cyber infrastructure while mitigating espionage and offensive cyber operations. The risks include large-scale data breaches, intellectual property theft, and attacks on critical infrastructure. With AI enhancing the scope and speed of cyberattacks, national sovereignty is increasingly threatened by cyber vulnerabilities that transcend borders.

AI-Driven Cyber Threats and Espionage

China has heavily integrated AI into its cyber capabilities, using it to enhance espionage, cyber warfare, and information manipulation. AI-enabled cyber operations allow China to gather vast amounts of intelligence data through advanced hacking techniques. These tools are often deployed through state-sponsored groups, exploiting zero-day vulnerabilities and penetrating government and corporate networks worldwide​.

For example, in 2021, China was accused of orchestrating a large-scale cyber-attack targeting Microsoft Exchange servers, affecting over 30,000 organizations globally. This attack was designed to facilitate espionage, capturing sensitive information ranging from corporate intellectual property to government data​. China's cyber operations underscore the increasing use of AI in orchestrating sophisticated, large-scale intrusions that threaten national sovereignty.

India, while lagging behind China in offensive cyber capabilities, faces persistent cyber espionage threats from Chinese state-sponsored actors. The most notable incidents occurred during the 2020 India-China border standoff, where Chinese hackers targeted India's critical infrastructure, including power grids and government networks​. These attacks highlight the vulnerabilities in India's cybersecurity architecture and its need to enhance AI-driven defenses.

Vulnerabilities and National Sovereignty

AI-driven cyber threats pose significant risks to national sovereignty. For India, the challenges are magnified by the relatively underdeveloped nature of its cybersecurity infrastructure. Although the establishment of the Defence Cyber Agency in 2018 marked a step forward, India still lacks the offensive cyber capabilities and AI sophistication of China​. India's defensive posture primarily focuses on securing critical infrastructure and mitigating cyber intrusions, but it remains vulnerable to cyber espionage and attacks on its digital economy.

China's integration of AI into both military and civilian cyber systems, through its Military-Civil Fusion policy, has bolstered its ability to conduct large-scale cyber operations with deniability. This fusion allows China to leverage private sector innovations for military purposes, making it a formidable cyber power in the Indo-Pacific region​.

Case Studies: Cyber Confrontations

In 2019, a significant cyberattack targeted India's Kudankulam Nuclear Power Plant, which was traced back to North Korea, but was believed to be part of a broader effort involving Chinese actors. This incident highlighted the potential for AI-enhanced malware to target critical infrastructure, posing severe risks to national security.

Similarly, the 2020 Mumbai blackout, reportedly linked to Chinese hackers, emphasized how AI-driven cyberattacks can disrupt essential services, creating chaos in times of geopolitical tension​. These incidents illustrate how AI-driven cyber capabilities are increasingly weaponized, posing severe risks to India's sovereignty and its ability to protect critical infrastructure.

Implications for Future Conflicts

As AI continues to evolve, the cyber domain will become a primary battleground in future conflicts between India and China. AI-enhanced cyber operations provide both nations with the ability to conduct espionage, sabotage, and information warfare remotely, without direct military engagement. For China, these tools are integral to its broader geopolitical strategy, while India must develop its AI and cybersecurity capabilities to protect its national sovereignty and counteract cyber threats​.

Conclusion

The integration of AI into cybersecurity poses both opportunities and challenges for India and China. While China has aggressively developed AI-driven cyber capabilities, India faces an urgent need to enhance its defenses and develop its offensive cyber tools. As cyberattacks become more sophisticated, driven by AI, both nations will continue to grapple with the implications of these developments on national sovereignty and global security.

[mehmetyildizmelbourne-blog]

Why Drone Manufacturers in Taiwan Are Being Cyber-Targeted: A Technologist’s Perspective

Why Drone Manufacturers in Taiwan Are Being Cyber-Targeted: A Technologist’s Perspective

This story explores the intersection of technology and geopolitics. It covers the cyberattacks targeting Taiwan’s drone manufacturers and what they reveal about global power struggles and technological vulnerabilities. Taiwan drone manufacturers under siege: Technology meets geopolitics In the world of technology and defense, Taiwan has become a central player, particularly in the field of…