Distributed web hacking by adaptive consensus-based reinforcement learning
In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ilić, Nemanja [verfasserIn] Dašić, Dejan [verfasserIn] Vučetić, Miljan [verfasserIn] Makarov, Aleksej [verfasserIn] Petrović, Ranko [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
Distributed reinforcement learning |
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| Übergeordnetes Werk: |
Enthalten in: Artificial intelligence - Amsterdam : Elsevier, 1970, 326 |
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| Übergeordnetes Werk: |
volume:326 |
| DOI / URN: |
10.1016/j.artint.2023.104032 |
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| Katalog-ID: |
ELV066046688 |
|---|
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| 520 | |a In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. | ||
| 650 | 4 | |a Distributed reinforcement learning | |
| 650 | 4 | |a Multi-agent system | |
| 650 | 4 | |a Adaptive consensus-based algorithm | |
| 650 | 4 | |a Distributed Q-learning | |
| 650 | 4 | |a Ethical web hacking | |
| 650 | 4 | |a Penetration testing | |
| 650 | 4 | |a Capture the flag | |
| 700 | 1 | |a Dašić, Dejan |e verfasserin |0 (orcid)0000-0001-5050-6331 |4 aut | |
| 700 | 1 | |a Vučetić, Miljan |e verfasserin |4 aut | |
| 700 | 1 | |a Makarov, Aleksej |e verfasserin |4 aut | |
| 700 | 1 | |a Petrović, Ranko |e verfasserin |4 aut | |
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10.1016/j.artint.2023.104032 doi (DE-627)ELV066046688 (ELSEVIER)S0004-3702(23)00178-9 DE-627 ger DE-627 rda eng 004 690 VZ 54.72 bkl Ilić, Nemanja verfasserin aut Distributed web hacking by adaptive consensus-based reinforcement learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. Distributed reinforcement learning Multi-agent system Adaptive consensus-based algorithm Distributed Q-learning Ethical web hacking Penetration testing Capture the flag Dašić, Dejan verfasserin (orcid)0000-0001-5050-6331 aut Vučetić, Miljan verfasserin aut Makarov, Aleksej verfasserin aut Petrović, Ranko verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 326 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:326 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 326 |
| spelling |
10.1016/j.artint.2023.104032 doi (DE-627)ELV066046688 (ELSEVIER)S0004-3702(23)00178-9 DE-627 ger DE-627 rda eng 004 690 VZ 54.72 bkl Ilić, Nemanja verfasserin aut Distributed web hacking by adaptive consensus-based reinforcement learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. Distributed reinforcement learning Multi-agent system Adaptive consensus-based algorithm Distributed Q-learning Ethical web hacking Penetration testing Capture the flag Dašić, Dejan verfasserin (orcid)0000-0001-5050-6331 aut Vučetić, Miljan verfasserin aut Makarov, Aleksej verfasserin aut Petrović, Ranko verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 326 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:326 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 326 |
| allfields_unstemmed |
10.1016/j.artint.2023.104032 doi (DE-627)ELV066046688 (ELSEVIER)S0004-3702(23)00178-9 DE-627 ger DE-627 rda eng 004 690 VZ 54.72 bkl Ilić, Nemanja verfasserin aut Distributed web hacking by adaptive consensus-based reinforcement learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. Distributed reinforcement learning Multi-agent system Adaptive consensus-based algorithm Distributed Q-learning Ethical web hacking Penetration testing Capture the flag Dašić, Dejan verfasserin (orcid)0000-0001-5050-6331 aut Vučetić, Miljan verfasserin aut Makarov, Aleksej verfasserin aut Petrović, Ranko verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 326 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:326 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 326 |
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10.1016/j.artint.2023.104032 doi (DE-627)ELV066046688 (ELSEVIER)S0004-3702(23)00178-9 DE-627 ger DE-627 rda eng 004 690 VZ 54.72 bkl Ilić, Nemanja verfasserin aut Distributed web hacking by adaptive consensus-based reinforcement learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. Distributed reinforcement learning Multi-agent system Adaptive consensus-based algorithm Distributed Q-learning Ethical web hacking Penetration testing Capture the flag Dašić, Dejan verfasserin (orcid)0000-0001-5050-6331 aut Vučetić, Miljan verfasserin aut Makarov, Aleksej verfasserin aut Petrović, Ranko verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 326 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:326 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 326 |
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10.1016/j.artint.2023.104032 doi (DE-627)ELV066046688 (ELSEVIER)S0004-3702(23)00178-9 DE-627 ger DE-627 rda eng 004 690 VZ 54.72 bkl Ilić, Nemanja verfasserin aut Distributed web hacking by adaptive consensus-based reinforcement learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. Distributed reinforcement learning Multi-agent system Adaptive consensus-based algorithm Distributed Q-learning Ethical web hacking Penetration testing Capture the flag Dašić, Dejan verfasserin (orcid)0000-0001-5050-6331 aut Vučetić, Miljan verfasserin aut Makarov, Aleksej verfasserin aut Petrović, Ranko verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 326 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:326 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 326 |
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Ilić, Nemanja @@aut@@ Dašić, Dejan @@aut@@ Vučetić, Miljan @@aut@@ Makarov, Aleksej @@aut@@ Petrović, Ranko @@aut@@ |
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Ilić, Nemanja |
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Ilić, Nemanja ddc 004 bkl 54.72 misc Distributed reinforcement learning misc Multi-agent system misc Adaptive consensus-based algorithm misc Distributed Q-learning misc Ethical web hacking misc Penetration testing misc Capture the flag Distributed web hacking by adaptive consensus-based reinforcement learning |
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Distributed web hacking by adaptive consensus-based reinforcement learning |
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In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. |
| abstractGer |
In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. |
| abstract_unstemmed |
In this paper, we propose a novel adaptive consensus-based learning algorithm for automated and distributed web hacking. We aim to assist ethical hackers in conducting legitimate penetration testing and improving web security by identifying system vulnerabilities at an early stage. Ethical hacking is modeled as a capture-the-flag style task addressed within a distributed reinforcement learning framework. To achieve our goal, we employ interconnected intelligent agents that interact with their copies of the environment simultaneously to reach the target. They perform local information processing to optimize their policies and exchange information with neighboring agents. We propose a novel adaptive consensus scheme for inter-agent communications, which enables the agents to efficiently share network-wide information in a decentralized manner. The scheme dynamically adjusts its weights based on heuristics, involving both recency and frequency metrics of actions selected at a given state by an individual agent, similar to eligibility traces. We extensively analyze the convergence properties of our algorithm and introduce a new communication scheme design. We demonstrate that this design ensures the fastest convergence to the desired asymptotic values under the general setting of asymmetric communication topologies. Additionally, we provide a comprehensive review of the current state of the field and propose a web agent model with improved scalability compared to existing solutions. Numerical simulations are conducted to illustrate the key characteristics of our algorithm. The results demonstrate that it outperforms both non-cooperative and average consensus schemes. Moreover, our algorithm significantly reduces hacking times when compared to baseline algorithms that rely on more complex models. These findings offer valuable insights to system security administrators, enabling them to address identified shortcomings and vulnerabilities effectively. |
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| score |
7.402316 |