Artificial Immune System Threat Detection and DTNs
January 27, 2016
Today we will examine the concept of the artificial immune systems. In one of my previous blog posts I discussed biomimicry and how it relates to information security. We will revisit biomimicry in the form of artificial immune systems. I will reveal how DTNs (disruption tolerant network/delay tolerant networks) function and how they may be used to improve security in the Internet of Things.
I will discuss a new whitepaper for an in development decentralized application called IOTA which utilizes DAGs. Finally I will combine all of these different technologies into an example of how it might in the near future converge to be used.
“The thesis presents nine design principles for the second generation’s artificial immune systems. The first principle is that artificial immune systems are represented as autonomous agents. The second principle states problems when AIS are represented as antigens or external (intrusion) signals. The third principle states that the aim of the second generation AIS is to maintain themselves and their environments. The fourth principle defines the functions of agents being to capture antigens, to process, to present, to recognize, to monitor, process and produce signals . The fifth design principle states that agents have a life cycle. The sixth design principle states that agents communicate with the environment at multiple levels. The seventh design principle states that signals can be externally or internally produced. The eight design principle states that receptors can be specific, internal or external signals. Te last principle states that agents can specialize in specific tasks ” (Singh, 2015).
Using artificial immune systems for intrusion detection
The design principles above show that you can represent an artificial immune system as autonomous agents. In the human body homeostasis must be maintained, and similarly in a network, the equivalent of homeostasis must be maintained.
Artificial immune systems in the context of network security can be used to detect an anomaly or intrusion (IDS) and then respond to the anomoly or intrusion (IRS). Similar to an immune system in the human body, when an intrusion is detected, then in real time the intrusion response system activates. From there an immune sequence takes place to neutralize the threat.
In order to follow the architecture illustrated in Singh’s paper, it would require a sensor network of secure autonomous agents, which are given the task of performing vulnerability analysis, intrusion detection, incident response and security management.
What are delay tolerant networking / disruption tolerant networks?
To provide clarity, depending on which circle you are in you may have heard DTNs referred to as either delay tolerant networks or disruption tolerant networks. Both of these different acronyms are equivalent in how they function, but disruption tolerant networking is favored by DARPA and connected groups.
Disruption tolerant networking was developed for use in space and in military situations where connectivity might vary due to certain conditions, but where the delivery of the message is critical in spite of the fact that connectivity patterns vary. Ad-hoc mobile networks benefit from a DTN. These ad-hoc mobile networks can be incredibly resilient as they (the nodes) can be made up of drones, wearable computers, vehicles. All which may constantly be in motion and have payloads which must wait until a peer is found which is capable of receiving it.
DTNs will be increasingly relevent in the Internet of Things era of computing because MANET (mobile ad-hock networks) and VANETs (vehicle ad-hock networks), will be among the “things” in the IoT.
What is Iota and why is it relevant?
Iota is a design for a micropayment platform. At this time it is unknown whether it will be a success, but from what is known about the project, it uses a DAG (directed acyclic graph) to allow for micropayments without a global blockchain.
A graph can represent the nodes within an ad-hock network. The DAG produces a casual graph which functions as an immutable history of the relationships between nodes. In the case of transactions, it may be possible to use a DAG to secure transactions by utilizing “cumulative weights” (Popov, 2015). It must be noted that Iota has not been tested and what is currently presented is theoretical rather than a practical empirical result.
What could we do with these technologies in combination?
Combining DTN, Iota and the artificial immune system approach to security may produce some intriguing results. DTNs are incredibly resilient, useful for mobile networks within a city, and useful for building ad-hock networks from which to utilize an IoT.
Iota with it’s experimental micropayments platform could allow for secure payments so that all mobile nodes in the ad-hock network transmit value to pay a toll for information storage and transmission. The artificial immune system if developed properly, could be used to prevent various connected components from being hacked, such as components in a vehicle or inter-connected gadgets. In the case where there is an anonmoly or an intrusion of any of the lesser components in the network, then greater components in the network could theoretically develop an immunity in realtime to contain the threat.
About the Guest Author
Dana Edwards is a technological visionary, an information security expert and a
social futurist. Born and raised in Boston Massachusetts, he
obtained a Bachelors degree in ethics, social & political philosophy
from UMass, a Masters degree in Cybersecurity from UMUC, and is CompTIA
He has been fascinated by and continuously studied computer
technology and information security since 1997 when he received his
first computer. As a student, teacher and problem solver, he wishes to
share some of his knowledge with the world, and to inspire, conduct, and
promote innovative experiments in cybersecurity.
Cochran, T. O. (2015). Immunology Inspired Detection of Data Theft from Autonomous Network Activity.
Popov, S. (n.d.). The tangle. Retrieved November 24, 2015, from http://126.96.36.199/tangle.pdf