Artificial immune system based mobile agent platform protection

Venkatesan, S.; Baskaran, R.; Chellappan, C.; Vaish, Anurika; Dhavachelvan, P.

doi:10.1016/j.csi.2012.10.003

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…In spite of recent popularity of the above mentioned techniques and extensive academic research, there is a need to exploit this knowledge of the system in detection of operational behaviour since sensors are been controlled in real-time similar to how the human body immune system detects and removes or destroys threats from numerous pathogens such as viruses, bacteria and parasites [26,33]. Various techniques and models have been developed and applied in securing Information system infrastructures such as ICS devices and different network devices and platforms against APT and cyber threats.…”

Section: Introductionmentioning

confidence: 99%

The use of machine learning algorithms for detecting advanced persistent threats

Eke

Petrovski

Ahriz

2019

Proceedings of the 12th International Conference on Security of Information and Networks

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Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. Due to their capability to navigates around defenses and to evade detection for a prolonged period of time, targeted APT attacks present an increasing concern for both cyber security and business continuity personnel. This paper explores the application of Artificial Immune System (AIS) and Recurrent Neural Networks (RNNs) variants for APT detection. It has been shown that the variants of the suggested algorithms provide not only detection capability, but can also classify malicious data traffic with respect to the type of APT attacks. CCS CONCEPTS • Computing methodologies → Artificial intelligence; Machine learning; Neural networks.

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Section: Introductionmentioning

confidence: 99%

The use of machine learning algorithms for detecting advanced persistent threats

Eke

Petrovski

Ahriz

2019

Proceedings of the 12th International Conference on Security of Information and Networks

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show abstract

“…The scanner provides an alarm to the DM if any suspected content exists. An immune system is proposed in [51]. Actually, the work [51] is considered a development of the work [50], where performance was the axis of the enhancement.…”

Section: )mentioning

confidence: 99%

“…An immune system is proposed in [51]. Actually, the work [51] is considered a development of the work [50], where performance was the axis of the enhancement. The key idea is to employ the pipelining concept in scanning, predicting, and extracting the malicious piece of code.…”

Section: )mentioning

confidence: 99%

“…Therefore, the attacker (malicious agent) has the ability of revealing some entities of the system within the DMs. For example, in [50] and [51], the policy file used for protecting the agent's platform is known by the visiting agent. The policy file may reflect the sensitivity level of the system installed on the DM where the mobile agent is executed.…”

Section: Drawn Conclusion Frommentioning

confidence: 99%

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A Survey: Agent-based Software Technology Under the Eyes of Cyber Security, Security Controls, Attacks and Challenges

Alluhaybi¹,

Alrahhal²,

Alzhrani³

et al. 2019

IJACSA

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Recently, agent-based software technology has received wide attention by the research community due to its valuable benefits, such as reducing the load on networks and providing an efficient solution for the transmission challenge problem. However, the major concern in building agent-based systems is related to the security of agents. In this paper, we explore the techniques used to build controls that guarantee both the protection of agents against malicious destination machines and the protection of destination machines against malicious agents. In addition, statistical-based analyses are employed to evaluate the level of maturity of the protection techniques to preserve the protection goals (the code and data, state, and itinerary of the agent), with and without the threat of attacks. Challenges regarding the security of agents are presented and highlighted by seven research questions related to satisfying cyber security requirements, protecting the visiting agent and the visited host machine from each other, providing robustness against advanced attacks that target protection goals, quantifying the security in agent-based systems, and providing features of self-protection and self-communication to the agent itself.

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“…Afterwards, unknown tokens are executed and tested in an isolated environment to check for their malicious intentions and updates the KB containing malicious codes, with the newly diagnosed (if any) vicious code. In order to fend off this waiting time of the agent, Venkatesan et al [20] further included the agent clones to handle multiple incoming agents simultaneously. Additionally, the pipelining concept was introduced by separating the operations i.e.…”

mentioning

confidence: 99%

N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents

Bagga¹,

Hans²,

Sharma³

2017

IJIMAI

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Artificial immune system based mobile agent platform protection

Cited by 13 publications

References 19 publications

The use of machine learning algorithms for detecting advanced persistent threats

The use of machine learning algorithms for detecting advanced persistent threats

A Survey: Agent-based Software Technology Under the Eyes of Cyber Security, Security Controls, Attacks and Challenges

N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents

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