A New Design for Protecting Cyberattacks in IoT Communication Network with Efficient Deep Learning-Based Detection System!

The Internet of Things, or IoT, is a revolutionary technology with vast potential. Cybercrime on IoT networks is possible since less complex endpoint equipment have less processing power, less storage space, and less available network bandwidth than more complex ones. So, as the number of linked devices increases, so does the possibility of cyberattacks, which can have disastrous results. 

The attack surface grows as more devices are connected, allowing hackers additional opportunities to take advantage of loopholes and obtain unauthorized access to private information. Therefore, to protect IoT networks against these threats, researchers have been attempting to develop various new and creative approaches.

In 2018, researchers from the University of California, San Diego demonstrated the effectiveness of their proposed system for detecting and mitigating Distributed Denial of Service (DDoS) attacks in IoT networks. Using a testbed of IoT devices, they carried out a number of studies to demonstrate how well their system could identify DDoS attacks with a low false positive rate and efficiently neutralize the attack by filtering out malicious traffic.

Moreover, a study published in the Journal of Network and Computer Applications in 2019 examined the use of machine learning algorithms to improve the security of IoT networks.

Using information gathered from a variety of IoT devices, the researchers built a machine learning model to identify malicious activities in real time. They discovered that their system had a low false alarm rate and could identify a variety of attacks with great accuracy.

Challenges in Cyberattack Protection for IoT Networks

It is critical to understand the current challenges in this domain before designing an effective cyberattack prevention system for IoT networks. Due to their interconnected structure and large volume of data collecting, IoT networks are vulnerable to numerous sorts of cyberattacks. The term “data collection” refers to the steps used to acquire information about VOS within a dataset in a structured, recorded manner useful for testing hypotheses, elucidating research questions, and assessing findings. 

The IoT network’s typical data samples are the lawful data packets it handles. Incorrect data samples are packets of data that have been modified in some way. This makes it possible to carry out attacks that were previously more complex, such as alterations to network packet header settings. Each sample has 43 properties, including time, procedure, service, and others, making a total of 148,517 samples in both scenarios.

Another challenge is the limited resource availability of IoT devices. In order to guarantee their durability and affordability, many Internet of Things devices are built with constrained memory, processing power, and energy resources. These limitations, however, make the implementation of standard security measures, including complex encryption algorithms or resource-intensive intrusion detection systems, impractical. As a result, developing lightweight yet effective security solutions become critical.

A contemporary design for cyber attack protection

The proposed new design for cyber attack protection in IoT networks is a game-changing approach that takes advantage of efficient deep learning-based detection systems to protect the future of connected devices. As the Internet of Things expands, the need for strong security measures becomes even more critical.

Traditional security mechanisms are enhanced with the power of deep learning algorithms in this design. Deep learning enables the system to analyze massive amounts of data from IoT devices, network traffic, and other relevant sources to identify patterns and anomalies that may indicate a cyber attack.

 The main benefit of this design is its efficiency. The system can detect malicious activities quickly and accurately by leveraging deep learning capabilities, reducing the time window for potential damage. This proactive approach protects IoT networks from a wide range of cyber threats, such as malware, botnets, and unauthorized access.

Introduction to Deep Learning and its potential in Cyber Attack Detection

Deep learning has emerged as a significant technique in a variety of domains, and its potential in detecting cyberattacks in IoT networks is no exception. Deep learning is a sort of machine learning in which artificial neural networks are trained on large datasets. It can be used to prevent cyber-attacks on IoT devices in a variety of ways:

• Anomaly detection: Deep learning models can be trained to recognize normal patterns of behavior in an IoT network and detect anomalies that may indicate a cyber-attack. For example, a model could be trained to recognize normal patterns of network traffic and alert administrators if it detects an unusually high volume of traffic or traffic from unusual sources.  
• Intrusion detection: Deep learning models can be used to analyze network traffic and identify known attack patterns, such as those associated with specific types of malwares or malicious activity.
• Network security: Deep learning models can be used to analyze network traffic and identify patterns that may indicate a vulnerability that could be exploited by an attacker. This can help administrators identify and patch vulnerabilities before they are exploited.
• Password security: Deep learning models can be used to analyze patterns in password usage and identify weak or commonly used passwords that may be vulnerable to attack. This can help administrators enforce stronger password policies and reduce the risk of password-based attacks.

Therefore, deep learning has enormous potential for detecting cyberattacks in IoT networks. Its ability to learn and adapt automatically from data, combined with its ability to handle complex and high-dimensional data, makes it a potential solution for protecting the future of IoT networks from cyber threats.

KDD 199 dataset and the NSL-KDD database

The KDD 199 dataset and the NSL-KDD database are two international data sets that address possible threats to the IoT. KDD 199 was developed as an intrusion detection system (IDS) for networks that can distinguish between “good” and “bad” connections, DARPA’s intrusion detection assessment programme funded the development of KDD 199.

The most recent version, NSL-KDD, has been used to enhance KDD’99 by adding new, non-duplicative attack reports with distinct levels of complexity. While the original KDD’99 dataset was much larger, the NSL-KDD dataset has all the same characteristics. 

The NSL-KDD training dataset is a collection of data used to evaluate the performance of network intrusion detection systems. It is made up of a series of records, each of which represents a network connection and its distinctive features.

The 41 attributes of each record can be divided into three categories:

• Basic features: These are features that describe the basic characteristics of the connection, such as the protocol used (e.g., TCP, UDP), the type of service (e.g., HTTP, FTP), and the duration of the connection.
• Content features: These are features that describe the content of the connection, such as the number of bytes transferred, the number of failed login attempts, and the number of root access attempts. 
• Traffic features: These are features that describe the traffic generated by the connection, such as the number of packets sent and received, the number of errors encountered, and the number of urgent packets.

Conclusion

In conclusion, securing IoT networks is of utmost importance in today’s digitally interconnected world. With the increasing number of devices being connected to the internet, the potential for cyber attacks and data breaches has also grown exponentially. To protect the integrity and privacy of Internet of Things networks, it is imperative to understand the need of putting strong security measures in place.

Written and curated by: Vipul Bansal, Technology Specialist Software Engineer, Chicago Mercantile Exchange, USA. You can follow him on Linkedin