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Accelerating Cyber-Breach Investigations Through Novel Use of Artificial Immune System algorithms 42nd

Accelerating Cyber-Breach Investigations Through Novel Use o...

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42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

作者： Donnachie, Benjamin Verrall, Jason Hopgood, Adrian Wong, Patrick Kennedy, Ian Open Univ Walton Hall Milton Keynes England Univ Portsmouth Winston Churchill Ave Portsmouth England

ISBN: (纸本)9783031214400;9783031214417

The use of artificial immune systems for investigation of cyber-security breaches is presented. Manual reviews of disk images are impractical because of the size of the dataset. Machine-learning algorithms for detection of misuse require labelled training data, which are generally unavailable. They are also necessarily retrospective, so they are unlikely to detect new forms of intrusion. For those reasons, this article proposes the use of artificial immune systems for unsupervised anomaly detection. Specifically, a deterministic dendritic cell algorithm (dDCA) has been implemented that has successfully detected automated SQL injection attacks from sample disk images. For comparison, it outperformed an unsupervised k-means clustering algorithm. However, many significant anomalies were not detected, so further work is required to refine the algorithm using more extensive datasets, and to encode complementary expert knowledge.

关键词： Anomaly detection Artificial Immune Systems Cybersecurity dendritic cell algorithm Unsupervised learning

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Danger Theory or Trained Neural Network - A Comparative Study for Behavioural Detection 10

Danger Theory or Trained Neural Network - A Comparative Stud...

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Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) / 19th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Lau, Henry Y. K. Lee, Nicole M. Y. Univ Hong Kong Dept Ind & Mfg Syst Engn Pokfulam Hong Kong Peoples R China

ISBN: (纸本)9781538626337

Danger Theory stipulates a powerful defensive mechanism underpinning the human immune system, which is a sophisticated classification metaphor, namely, dendritic cell algorithm (DCA) which has been demonstrated in many real-life applications. In this paper, the DC-inspired metaphor is adopted in the domain of the behavioural detection for field operation training. This signal-based classification algorithm empowers with a robust learning capability and self-organizing control mechanism, whereby the "danger signals" correspond to the safety and procedural related activities, are being differentiated from the information which are captured in immersive virtual environments including the Training of Ramp Operations in Virtual Environment (TROVE) and Detective Boulevard supported by the imseCAVE virtual reality system. As such, the performance of the trainees can be assessed and enumerated by the DCA autonomously, that can improve the quality of assessment made by the trainers or coaches. In a benchmark study, the operational training of aircraft door handling is considered, in particular to study the behaviours in operational procedures and safety concerns. According to the experimental results, Artificial Neural Network (ANN) outperformed DCA in the given domain with respect to the performance of classification accuracy and run time.

关键词： Artificial Immune Systems dendritic cell algorithm Artificial Neural Network Behavioural Detection Immersive Virtual Training

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An Immune Intelligent Approach for Security Assurance

An Immune Intelligent Approach for Security Assurance

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International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)

作者： Enache, Adriana-Cristina Ionita, Mihai Sgarciu, Valentin Univ Politehn Fac Automat Control & Comp Sci Bucharest Romania Mil Tech Acad Fac Comp Sci Bucharest Romania

ISBN: (纸本)9781467367974

Information Security Assurance implies ensuring the integrity, confidentiality and availability of critical assets for an organization. The large amount of events to monitor in a fluid system in terms of topology and variety of new hardware or software, overwhelms monitoring controls. Furthermore, the multi-facets of cyber threats today makes it difficult even for security experts to handle and keep up-to-date. Hence, automatic "intelligent" tools are needed to address these issues. In this paper, we describe a 'work in progress' contribution on intelligent based approach to mitigating security threats. The main contribution of this work is an anomaly based IDS model with active response that combines artificial immune systems and swarm intelligence with the SVM classifier. Test results for the NSL-KDD dataset prove the proposed approach can outperform the standard classifier in terms of attack detection rate and false alarm rate, while reducing the number of features in the dataset.

关键词： IDS dendritic cell algorithm Binary Bat algorithm SVM

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A Sense of 'Danger' for Windows Processes

A Sense of 'Danger' for Windows Processes

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8th International Conference on Artificial Immune Systems

作者： Manzoor, Salman Shafiq, M. Zubair Tabish, S. Momina Farooq, Muddassar NUCES FAST Next Generat Intelligent Networks Res Ctr NexGIN Islamabad 44000 Pakistan

ISBN: (纸本)9783642032455

The sophistication of modern computer malware demands run-time malware detection strategies which are not only efficient but also robust to obfuscation and evasion attempts. In this paper;we investigate the suitability of recently proposed dendritic cell algorithms (DCA), both classical DCA (cDCA) and deterministic DCA (dDCA), for malware detection at run-time. We have collected API call traces of real malware and benign processes running on Windows operating system. We evaluate the accuracy of cDCA and dDCA for classifying between malware and benign processes using API call sequences. Moreover, we also study the effects of antigen multiplier and time-windows on the detection accuracy of both algorithms.

关键词： API Call Sequence Artificial Immune System dendritic cell algorithm Malware Detection

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Intrusion Detection Systems Adapted from Agent-based Artificial Immune Systems

Intrusion Detection Systems Adapted from Agent-based Artific...

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IEEE International Conference on Fuzzy Systems (FUZZ 2011)

作者： Ou, Chung-Ming Wang, Yao-Tien Ou, C. R. Kainan Univ Dept Informat Management Luchu 33857 Taiwan Hungkuang Univ Dept Comp Sci & Informat Engn Taichung Taiwan Hsiuping Inst Technol Dept Elect Engn Taichung Taiwan

ISBN: (纸本)9781424473175

Agent-based artificial immune system (ABAIS) is applied to intrusion detection systems (IDS). A multiagent-based IDS (ABIDS) inspired by the danger theory of human immune system is proposed. The intelligence behind ABIDS is based on the functionality of dendritic cells in human immune systems and the danger theory, while dentritic cells agents (DC agent) are emulated for innate immune subsystem and artificial T-cell agents (TC agent) are for adaptive immune subsystem. Antigens are profiles of system calls while corresponding behaviors are regarded as signals. This ABIDS is based on the dual detections of DC agent for signals and TC agent for antigen, where each agent coordinates with other to calculate danger value (DV). ABAIS is an intelligent system with learning and memory capabilities. According to DVs, immune response for malicious behaviors is activated by either computer host or Security Operating Center (SOC). Multiple agents are "embedded" to ABIDS, where agents coordinate one another to calculate mature context antigen value (MCAV) and update activation threshold for security responses. Accordingly, computer hosts met with malicious intrusions can be effectively detected via input signals and temporary output signals such as PAMP, danger and safe signals.

关键词： intrusion detection multiagent artificial immune systems dendritic cell algorithm

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Securing the Internet of Things with Responsive Artificial Immune Systems 15

Securing the Internet of Things with Responsive Artificial I...

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17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Greensmith, Julie Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

ISBN: (纸本)9781450334723

The Internet of Things is a network of 'smart' objects, transforming everyday objects into entities which can measure, sense and understand their environment. The devices are uniquely identifiable, rely on near field connectivity, often in embedded devices. The Internet of Things is designed to be deployed without human intervention or interaction. One application is the 'smart house', with components including household appliances, networked with the user able to control devices remotely. However, the security inherent in these systems is added as somewhat of an afterthought. One hypothetical scenario is where a malicious party could exploit this technology with potentially disastrous consequences, turning on a cooker remotely leading to digital arson. Reliance on standard methods is insufficient to provide the user with adequate levels of security, an area where AIS may be extremely useful. There are currently limitations with AIS applied in security, focussing on detection without providing automatic responses. This problem provides an opportunity to advance AIS in providing both an ideal scenario for testing their real-world application and to develop novel responsive AIS. A responsive version of the deterministic dendritic cell algorithm will be proposed to demonstrate how responsive AIS will need to be developed to meet these future challenges through proposing the incorporation of a model of T-cell responses.

关键词： Artificial Immune Systems Internet of Things Automated Responses Security dendritic cell algorithm

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Design of TCP SYN Flood DDoS Attack Detection Using Artificial Immune Systems 6

Design of TCP SYN Flood DDoS Attack Detection Using Artifici...

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6th International Conference on System Engineering and Technology (ICSET)

作者： Ramadhan, Gilang Kurniawan, Yusuf Kim, Chang-Soo Inst Teknol Bandung Sch Elect Engn & Informat Jalan Ganesha 10 Bandung Indonesia Pukyong Natl Univ 45 Yongso Ro Busan South Korea

ISBN: (纸本)9781509050895

TCP Flood DDoS attack is one of the most commonly-used attacks. DDoS has a huge impact on the victim because DDoS attack can multiply the power of attack and makes the victim server unavailable for the normal user. Recently, there are many types of research about DDoS attacks and the detection or mitigation techniques. One of the novel methods to detect DDoS is using dendritic cell algorithm (DCA). DCA is a kind of artificial immune system in the evolutionary algorithm that can be used as an anomaly detection. The DCA is also designed to solve the problem in network intrusion detection. In this paper tries to make a design of TCP Flood DDoS attack detection using artificial immune systems, especially dendritic cell algorithm.

关键词： Artificial Immune System dendritic cell algorithm DDoS Transport Layer

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An Efficient Botnet Detection System for P2P Botnet

An Efficient Botnet Detection System for P2P Botnet

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IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Thangapandiyan, M. Anand, P. M. Rubesh Hindustan Univ Dept Elect & Commun Engn Madras Tamil Nadu India

ISBN: (纸本)9781467393379

Peer-to-Peer (P2P) botnets are exploited by the botmasters for their resiliency against the take down efforts. As the modern botnets are stealthier, the traditional botnet detection approaches are not suitable for the botnet detection. In this paper, an efficient botnet detection system is proposed for detecting the P2P botnet. The proposed botnet detection system estimates the flow export using NetFlow protocol. The packet flow is analyzed using three main components namely, Exporter, Collector, and Analyzer. The exporter captures the packet and monitors the contents of the packet. The collector captures the flow traffic and the analyzer component initiates an automated analysis of traffic with the captured packet information. The packet flow information is collected by virtual interface and physical probe. The virtual interface is used for collecting the malicious traffic information between the Virtual Machines (VMs) and the physical probe gathers malicious traffic information between the network bridges connecting VMs. The information collected from these techniques are analyzed for detecting the botnets in inter VM and intra VM. Compared to the existing dendritic cell algorithm (DCA), the proposed VM based botnet detection system has minimal time consumption, increased detection speed, and higher attack prevention ratio.

关键词： P2P botnet Virtual Machine dendritic cell algorithm NetFlow Protocol Intrusion Detection

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The Designation of Bio-Inspired Intrusion Detection System Model in Cloud Computing Based on Machine Learning

The Designation of Bio-Inspired Intrusion Detection System M...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Yufei Ge Hong Liang Lin Chen Qian Zhang College of Computer and Communication Engineering China University of Petroleum

ISBN: (纸本)9781510803084

The paper proposed a new model by applying bio-inspired theory into intrusion detection system in cloud computing. dendritic cell algorithm(DCA) based on Danger Theory get involved in the model designation in allusion to the existing problem, high false negative rate, low detection efficiency, low real-time process, and lack of adaptive of the unknown aggressive behavior for nowadays detection mechanisms. Combing the pertinent knowledge of machine learning theory, then to design one improved bio-inspired intrusion detection system model in cloud computing based on machine learning. Finally we introduce our system module as well as the detailed work processes.

关键词： Intrusion Detection System Bio-Inspired dendritic cell algorithm Cloud Computing Model Designation

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Danger Theory or Trained Neural Network - A Comparative Study for Behavioural Detection

Danger Theory or Trained Neural Network - A Comparative Stud...

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International Symposium on Advanced Intelligent Systems

作者： Henry Y. K. Lau Nicole M. Y. Lee University of Hong Kong Hong Kong HK

ISBN: (纸本)9781538626344;9781538626337

关键词： Artificial immune systems dendritic cell algorithm Artificial neural network Behavioural detection Immersive virtual training

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