You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


---
abstract: 'A new defense mechanism for different jamming attack on [*Wireless Sensor Network (WSN)*]{} based on ant system it is introduced. The artificial sensitive ants react on network attacks in particular based on their sensitivity level. The information is re-directed from the attacked node to its appropriate destination node. It is analyzed how are detected and isolated the jamming attacks with mobile agents in general and in particular with the newly ant-based sensitive approach.'
address: 'Technical University Cluj Napoca, North University Center Baia Mare, Romania'
author:
- 'Camelia-M. Pintea'
- 'Petrica C. Pop'
title: |
    Sensitive Ants for Denial Jamming Attack\
    on Wireless Sensor Network
---

Introduction
============

Jamming attacks on wireless networks, special cases of [*Denial of Service (DoS)*]{} are the attacks that disturb the transceivers’ operations on wireless networks [@adamy04]. A [*Radio Frequency (RF)*]{} signal emitted by a jammer corresponds to the ‘useless’ information received by the sensor nodes of a network.

Nowadays are several techniques used to reduce the effect of jamming attacks in wireless networks. In [@yujin12] is proposed a traffic rerouting scheme for [*Wireless Mesh Network (WMN)*]{}. There are determined multiple candidates of a detour path which are physically disjoint. In a stochastic way, is selected just one candidate path as a detour path to distribute traffic flows on different detour paths. The mechanism on packet delivery ratio and end-to-end delay is improved when compared with a conventional scheme.

Securing [*WSN*]{}s against jamming attacks is a very important issue. Several security schemes proposed in the [*WSN*]{} literature are categorized in: [*detection techniques, proactive countermeasures, reactive countermeasures*]{} and [*mobile agent-based countermeasures*]{}. The advantages and disadvantages of each method are described in [@Mpitziopoulos]. Our current interest is in [*mobile agent-based solutions*]{}.

Artificial intelligence has today a great impact in all computational models. Multi-agent systems are used for solving difficult Artificial Intelligence problems [@wooldridge; @Iantovics]. Multi-agents characteristics includes organization, communication, negotiation, coordination, learning, dependability, learning and cooperation [@his; @wooldridge; @stoean]. Other features are their knowledge [@Popescu] and their actual/future relation between self awareness and intelligence. There are also specific multi-agents with their particular properties, as robots-agents [@pintea2010; @pinteacisis13]. One of the commonly paradigm used with [*MAS*]{} systems is the pheromone, in particular cases artificial ants pheromone. Ant-based models are some of the most successfully nowadays techniques used to solve complex problems [@crisan07; @crisan; @dorigo97; @pintea2010; @Reihaneh; @stoean2010].

Our goal is to improve the already existing ant systems on solving jamming attacks on [*WSN*]{} using the ants sensitivity feature. The second section describes the already known mechanisms of Jamming Attack on Wireless Sensor Network and the particular unjamming techniques based on Artificial Intelligence. The next section is about ants sensitivity. The section includes also the newly introduced sensitive ant-model for detecting and isolated jamming in a [*WSN*]{}. Several discussions about the new methods and the future works concludes the paper.

About Jamming Attack on Wireless Sensor Network using Artificial Intelligence
=============================================================================

The current section describes the main concepts and the software already implemented on Jamming Attack on Wireless Sensor Network including the [*Artificial Intelligence*]{} models.

Jamming Attack on Wireless Sensor Network
-----------------------------------------

Jamming attacks are particular cases of Denial of Service (DoS) attacks[@wood02]. The main concepts related to this domain follows. At first several considerations on Wireless Sensor Network (WSN).

A Wireless Sensor Network (WSN) consists of hundreds/thousands of sensor nodes randomly deployed in the field forming an infrastructure-less network.

A sensor node of a [*WSN*]{} collects data and routes it back to the Processing Element (PE) via ad-hoc connections with neighbor sensor nodes.

A Denial of Service attack is any event that diminishes or eliminates a network’s capacity to perform its expected function.

Jamming is defined as the emission of radio signals aiming at disturbing the transceivers’ operation.

There are differences between jamming and radio frequency interference.

-   the jamming attack is [*intentional*]{} and [*against a specific target*]{};

-   the radio frequency interference is unintentional, as a result of nearby transmitters, transmitting in the same or very close frequencies levels.

An example of radio frequency interference is the coexistence of multiple wireless networks on the same area with the same frequency channel [@Mpitziopoulos].

Noise is considered the undesirable accidental fluctuation of electromagnetic spectrum, collected by an antenna.

The Signal-to-Noise Ratio is $$SNR= \frac{P_{signal}}{P_{noise}}$$ where $P$ is the average power.

A jamming attack can be considered [*effective*]{} if the [*Signal-to-Noise Ratio(SNR)*]{} is less than one ($SNR<1$).

There are several jamming techniques [@Mpitziopoulos]: Spot Jamming, Sweep Jamming, Barrage Jamming and Deceptive Jamming.

‘Jammer’ refers to the equipment and its capabilities that are exploited by the adversaries to achieve their goal.

There are several types of jammers, from simple transmitter or jamming stations with special equipment, used against wireless networks.[@xu05]

-   the constant jammer - emitting totally random continuous radio signals; [*target*]{}: keeping the [*WSN*]{}s channel busy; disrupting nodes’ communication; causing interference to nodes that have already commenced data transfers and corrupt their packets.

-   the deceptive jammer;

-   the random jammer - sleeps for a random time and jams for a random time;

-   the reactive jammer - in case of activity in a [*WSN*]{} immediately sends out a random signal to collide with the existing signal on the channel.

Sensitive Ant-based Technique for Jamming Attack on Wireless Sensor Network
===========================================================================

The current section describes the concept of sensitive ants proposed in [@chira07; @pintea2010]. Effective metaheuristics for complex problems, as large scale routing problems (e.g. the [*Generalized Traveling Salesman Problem*]{}) based on sensitive ants are illustrated in [@chira07; @his; @pintea2010]. Several concepts are defined and described further.

Sensitive ants refers to artificial ants with a Pheromone Sensitivity Level (PSL) expressed by a real number in the unit interval $0\leq PSL \leq 1$.

A pheromone blind ant is an ant completely ignoring stigmergic information, with the smallest PSL value, zero.

A maximum pheromone sensitivity ant has the highest possible PSL value, one.

The sensitive-explorer ants have small\
Pheromone Sensitivity Level values indicating that they normally choose very high pheromone level moves.

The sensitive-exploiter ants have high\
Pheromone Sensitivity Level values indicating that they normally choose any pheromone marked move.

The sensitive-explorer ants are also called small PSL-ants, hPSL and sensitive-exploiter ants are called high PSL-ants, hPSL. They intensively exploit the promising search regions already identified by the sensitive-explorer ants. For some particular problems the sensitivity level of hPSL ants have been considered to be distributed in the interval (0.5, 1) and for the sPSL ants the sensitivity level in the interval (0, 0.5).

Based on the already described notions it is introduced a new ant-based concept with sensitivity feature. The [*Sensitive Ant Algorithm for Denial Jamming Attack on Wireless Sensor Network*]{} is further called [*Sensitive Ant Denial Jamming*]{} on [*WSN*]{} algorithm.

As we know, not all ants react in the same way to pheromone trails and their sensitivity levels are different there are used several groups of ants with different levels of sensitivity. For an easy implementation are used just two groups-colonies.

In [@Muraleedharan] is firstly introduced an ant system for jamming attack detection on [*WSN*]{}. The performance of the ant system is given by the node spacing and several parameters: $Q$ an arbitrary parameter, $\rho$ trail memory, $\alpha$ power applied to the pheromones in probability function and $\beta$ power of the distance in probability function.

It is considered a [*WSN*]{} in a two dimensional Euclidean space. There are several key elements of AS for keeping the network robust and de-centralized. One is the information on the resource availability on every node used to predict the link for the ant’s next visit.

Other key elements are the pheromones intensity and dissipate energy of ants as they traverse the nodes based on path probabilities. The key factor for making decisions in [@Muraleedharan] is the transition probability (\[eq:1\]). In the newly [*Sensitive Ant Denial Jamming*]{} on [*WSN*]{} only the ants with small pheromone level are using this probability.

$$\label{eq:1} 
P_{ij}=\frac{(\varphi_{ij}\cdot\eta_{ij})^\alpha\cdot(\frac{1}{D_{ij}})^\beta}{\sum_k (\varphi_{ik}\cdot\eta_{ik})^\alpha\cdot(\frac{1}{D_{ik}})^\beta}$$

where $\eta_{ij}$ is the normalized value (\[eq:2\]) of Hop, $H_{ij}$, Energy, $E_{ij}$, Bit Error Rate, $B_{ij}$, Signal to Noise ratio, $
SNR_{ij}$, Packet Delivery, $Pd_{ij}$ and Packet Loss, $Pl_{ij}$ [@Liang]. $$\label{eq:2} 
\eta_{ij}=H_{ij}\cdot E_{ij}\cdot B_{ij}\cdot SNR_{ij}\cdot Pd_{ij}\cdot Pl_{ij}$$

In the sensitive ant model the ants with high pheromone level are choosing the next node based on (\[eq:3\]) from the neighborhood $J$ of node $j$. $$\label{eq:3}
j=argmax_{u\in J_{ik}}\{(\varphi_{iu}\cdot\eta_{iu})^\alpha\cdot(\frac{1}{D_{iu}})^\beta\}.$$

$\varphi_{ij}$is the pheromone intensity between the source node $i$ and destination node $j$; The normalized value is the difference between total and actual value of the performance parameters. The performance value is used to compute the transition probabilities in a route. The link being active or dead in a tour taken by an ant is incorporated in the pheromone. The pheromone is globally updated [@dorigo] following each complete tour by ant system with the update rule (\[eq:4\]) is following [@Muraleedharan]. $$\label{eq:4} 
\varphi_{ij}(t)=\rho(\varphi_{ij}(t-1))+\frac{Q}{D_t\cdot \eta_t}$$ where $D_t$ is the total distance traveled by ants during the current tour. The trails formed by the ant is dependent on the link factor. The tabu list includes now updated values of the energy available in the nodes for a particular sub-optimal route with high reach-ability. A run of the algorithm returns the valid path of the wireless network. That is how the information in [*WSN*]{} is re-routed. Termination criteria is given by a given number of iterations.

[**Discussions.**]{} Sensitive ants with lower pheromone level are able to explore the wireless network and the ants with high pheromone level intensively exploit the promising search regions already identified in the network. The ant’s behavior emphasizes search intensification. The ants “learn” during their lifetime and are capable to improve their performances. That is how they modifies their level of sensitivity: the PSL value increase or decrease based on the search space topology encoded in the ant’s experience.

The introduced model [*Sensitive Ant Algorithm for Denial Jamming Attack on WSN*]{} seems to improve the jamming attack detection and re-routing in wireless sensor network.

Conclusions
===========

The paper shows the main jamming attacks and several countermeasure. It is introduced a new [*Sensitive Ant Denial Jamming*]{} on [*Wireless Sensor Network*]{} based on ant system as mobile-agents class. In general mobile agent techniques including ant models proved to have a medium defense effectiveness, a medium cost but a good compatibility with existing software. The introduced sensitive model brings a new feature that improves the reactions of agents in the network in case of jamming attacks and redirect the information also to the processing element in order to re-routing information.

Acknowledgement. {#acknowledgement. .unnumbered}
================

This research is supported by Grant PN II TE 113/2011, New hybrid metaheuristics for solving complex network design problems, funded by CNCS Romania.

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