How Machine Learning Can Be Used in Cyber Threat Detection

Did you know cyber threats are growing alarmingly, posing severe risks to businesses and individuals? Did you know Cybersecurity Ventures estimates global cybercrime costs will reach $10.5 trillion annually by 2025, a significant jump from $3 trillion in 2015? Also, in 2023 alone, there were over 4,100 publicly disclosed data breaches, exposing more than 22 billion records, according to the Identity Theft Resource Center. Did you know a report by CyberEdge Group states that 68% of organizations were affected by ransomware in 2022, with an average ransom payment of $812,000?

Midway through 2023, analysts may have to revisit their presumptions as UK and global attack figures are rising significantly. Attacker-reported ransomware attacks increased by 87% in the UK during the first half of 2023 compared to the latter half of 2022, while global rates have risen by 37% over the same period. Moreover, evidence suggests the cryptocurrency revenues of known threat actors have correlated with the recent rise in total attack rates.

In January, we also witnessed the successful takedown of Hive ransomware in a novel operation by bilateral law enforcement. Several US and European crime agencies collaborated to infiltrate Hive’s infrastructure, distribute decryption keys to victims, and thwart a reported $130 million in ransom payments.

While a positive sign, Hive’s takedown, unfortunately, had little impact on overall attack rates despite the concerted effort of several national crime agencies over six months, thus demonstrating the scale of tackling ransomware head-on. Moreover, there is a high probability that the threat actors responsible for Hive have redeployed elsewhere using a different form of ransomware or under a new name.

UK attack rates rose steadily in March and April, along with global totals, before perhaps the defining mass vulnerability exploitation event of 2023 in late May – the MOVEit breach.

The mass exploitation of software vulnerabilities is perhaps the most clear-cut contributing factor to the rise of ransomware attacks in 2023. Several vulnerabilities discovered in widely used platforms (Rackspace, Zimbra, and most notably, MOVEit) have contributed to rising attack figures.

CL0P may challenge Lockbit as the most prevalent variant globally in 2023. As illustrated, CL0P’s attack figures spiked following the MOVEit 0-day. Subsequently, the group’s reported incidents have continued to rise, not only as a direct result of the MOVEit breach. 

In a broader strategy shift, many attackers such as CL0P are now typically choosing to skip the classic step of network encryption, opting only to exfiltrate data as the primary means of extorting victims. This transition from ransomware deployment to pure ‘cyber extortion’ lessens the time and effort needed for end-to-end attack execution, but more importantly, means that targeted organizations must possess data valuable enough to hold ransom, which has likely contributed to another 2023 trend – the increased exploitation of the financial services, professional services, and IT sectors.

With these staggering numbers in mind, it’s evident that traditional cybersecurity methods are struggling to keep pace. But don’t worry; the IT world has an IT hero to help overcome these threats. Machine learning (ML) is a revolutionary force in cyber threat detection!

Machine learning is a branch of (Artificial Intelligence) that allows systems to learn patterns from data automatically. They can then, in effect, make predictions or decisions on their own without further coding. In cyber threat detection, the ML learns the massive datasets to discover the anomalies and possible threats.

These are the benefits of integrating machine learning concepts in detecting cyber threats:

1. Enhanced Accuracy:Since the ML models can accommodate previous data, they learn from history and are accurate.

2. Real-Time Threat Detection: The ML algorithms' real-time threat detection and response capabilities can detect threats as records are received and require no delay.

3. Scalability:Due to its unlimited scalability, businesspeople will be relieved that an ML solution can be designed and implemented for a company of any size.

Machine learning can be applied to different levels of cyber threat identification, such as anomaly detection, predictive, and user behavior analysis.

Anomaly Detection:Anomaly detection means obtaining the detection of events that deviate from the standard pattern and can be threats. For instance, an ML model can also study networks and related traffic and quickly identify activities such as increased transfer rates and unauthorized access attempts.

Predictive Analysis:Predictive analysis involves using historical data in an organization’s database to foretell upcoming dangers. By analyzing patterns that bring about cyber-attacks, ML models may indicate similar threats that may occur in the future. This approach enables organizations to implement preventive measures.

User Behavior Analytics: User behavior analytics (UBA) capture user activities to identify insider threats. They can also set up alarms for such activities as logging in at hours when they should not and accessing sensitive data that is likely to be exploited by an intruder.

IBM's QRadar Advisor with Watson:Watson, integrated into IBM’s security platform, collects data and uses machine learning for analysis and correlation. IBM has recorded a nearly 60 percent cut in the time it takes to investigate threats.

Darktrace:Darktrace, a cybersecurity firm, utilizes ML to work in real-time as it hunts for threats. The company could associate its technology with a 99% detection rate of threats that other systems fail to detect.

FireEye - Helix Platform:Machine learning is applied in FireEye’s Helix platform to improve its threat identification function. Security teams using FireEye’s platform report that it has cut their alert fatigue by 80% and their response times by 50%.

Cylance—CylancePROTECT: CylancePROTECT is developed using AI and ML to create enhanced endpoint protection. The company states that its technologies can effectively ward off more than 99 percent of all malware threats, thus significantly minimizing the chances of clients falling prey to cyber threats.

Vectra AI - Cognito Platform:Vectra AI’s Cognito platform is an AI-based tool that analyzes network traffic for previously unidentified threats. The platform has also been demonstrated to decrease the attack dwell time by 34%, which assists organizations in early detection and minimizing the impact of threats.

While ML offers significant advantages, there are challenges to consider:

Data Quality:This means that for any given ML model, the data quality will determine its efficiency. Their prediction is based on incomplete or biased information, often yielding poor results.

False Positives:The application of ML models is not without drawbacks. For example, identifying fake positive cases entails extra notices the organization or company does not require.

Model Interpretability: Non-linear and deep learning methods become more complicated in producing answers, making it rather hard to trace the course of a specific program's decision-making.

While cybersecurity is one of the most popular targets for ML application, it is still in its infancy, but the possibilities are endless. This report also states that Gartner concludes that, by 2025, the use cases for machine learning and AI will be included in more than 60% of digital security products. It is worth mentioning that advancing cyber threats will require the continuous presence of adaptive and resilient security solutions, which ML will be deemed instrumental in.

Among all the technologies, machine learning (ML) has become primary in this context, providing new revolutionary possibilities for cyber threat identification. Since it can process big data in real-time, identifying tendencies and anticipated risks contributes to improving cybersecurity seamlessly. Its significant advantages, such as enhanced accuracy, streaming-processing-based threat detection, and versatility, arm organizations with the tools necessary to protect their digital equities.

The application of ML in cybersecurity is an ideal example of how it can be applied and how its efficiency has been proven. IBM QRadar Advisor with Watson, Darktrace, FireEye’s Helix Platform, Cylance PROTECT, Vectra AI’s Cognito Platform, and CrowdStrike Falcon Platform have enhanced threat detection and response. These use cases are a testament to the tremendous ways in which ML makes a difference in investigation time, alert noise, and detection effectiveness, as well as in the translation of these into asserting the organization’s defenses against cyber threats.

Despite this, ML use in cybersecurity is not flawless and has some inconveniences. Availability, accuracy, spurious correlation, and model explainability must be met or circumvented to achieve the highest impact from a ROC-AUC. The mentioned challenges require such measures as guaranteeing the quality of the data collected and used, constant improvement of the models to reduce false positives, and elevating the level of transparency of the models in question.

Thus, the further development and use of ML in cybersecurity will only become even more critical and essential. Predicting that by 2025, ML and AI will become a significant factor in at least 60 percent of digital security products supports this. Due to the evolving nature of threats, the future of security solutions will heavily depend on incorporating ML into organizations’ frameworks.

Therefore, Artificial intelligence can be broadly described as a groundbreaking technology that can produce proactive and elaborate methods to approach cyber security threats. Due to its learning capability, it remains an essential tool in combating cybercrime. Incorporating ML into cybersecurity will help improve the organization’s cybersecurity standing, lower financial losses and repercussions, and counter criminals. With the growth of ML technology in cybersecurity, this technology will undoubtedly influence the future, and cybersecurity will become more secure as it adapts to new challenges.