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Gangadhar, S., Sterbenz, J. P. G..  2017.  Machine learning aided traffic tolerance to improve resilience for software defined networks. 2017 9th International Workshop on Resilient Networks Design and Modeling (RNDM). :1–7.

Software Defined Networks (SDNs) have gained prominence recently due to their flexible management and superior configuration functionality of the underlying network. SDNs, with OpenFlow as their primary implementation, allow for the use of a centralised controller to drive the decision making for all the supported devices in the network and manage traffic through routing table changes for incoming flows. In conventional networks, machine learning has been shown to detect malicious intrusion, and classify attacks such as DoS, user to root, and probe attacks. In this work, we extend the use of machine learning to improve traffic tolerance for SDNs. To achieve this, we extend the functionality of the controller to include a resilience framework, ReSDN, that incorporates machine learning to be able to distinguish DoS attacks, focussing on a neptune attack for our experiments. Our model is trained using the MIT KDD 1999 dataset. The system is developed as a module on top of the POX controller platform and evaluated using the Mininet simulator.

Kotel, S., Sbiaa, F., Zeghid, M., Machhout, M., Baganne, A., Tourki, R..  2016.  Efficient Hybrid Encryption System Based on Block Cipher and Chaos Generator. 2016 IEEE International Conference on Computer and Information Technology (CIT). :375–382.

In recent years, more and more multimedia data are generated and transmitted in various fields. So, many encryption methods for multimedia content have been put forward to satisfy various applications. However, there are still some open issues. Each encryption method has its advantages and drawbacks. Our main goal is expected to provide a solution for multimedia encryption which satisfies the target application constraints and performs metrics of the encryption algorithm. The Advanced Encryption Standard (AES) is the most popular algorithm used in symmetric key cryptography. Furthermore, chaotic encryption is a new research direction of cryptography which is characterized by high initial-value sensitivity and good randomness. In this paper we propose a hybrid video cryptosystem which combines two encryption techniques. The proposed cryptosystem realizes the video encryption through the chaos and AES in CTR mode. Experimental results and security analysis demonstrate that this cryptosystem is highly efficient and a robust system for video encryption.

Pritchard, S. W., Hancke, G. P., Abu-Mahfouz, A. M..  2017.  Security in software-defined wireless sensor networks: Threats, challenges and potential solutions. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). :168–173.
A Software-Defined Wireless Sensor Network (SD-WSN) is a recently developed model which is expected to play a large role not only in the development of the Internet of Things (IoT) paradigm but also as a platform for other applications such as smart water management. This model makes use of a Software-Defined Networking (SDN) approach to manage a Wireless Sensor Network (WSN) in order to solve most of the inherent issues surrounding WSNs. One of the most important aspects of any network, is security. This is an area that has received little attention within the development of SDWSNs, as most research addresses security concerns within SDN and WSNs independently. There is a need for research into the security of SDWSN. Some concepts from both SDN and WSN security can be adjusted to suit the SDWSN model while others cannot. Further research is needed into consolidating SDN and WSN security measures to consider security in SDWSN. Threats, challenges and potential solutions to securing SDWSN are presented by considering both the WSN and SDN paradigms.
Rubin, S. H., Grefe, W. K., Bouabana-Tebibel, T., Chen, S. C., Shyu, M. L., Simonsen, K. S..  2017.  Cyber-Secure UAV Communications Using Heuristically Inferred Stochastic Grammars and Hard Real-Time Adaptive Waveform Synthesis and Evolution. 2017 IEEE International Conference on Information Reuse and Integration (IRI). :9–15.
Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (\textbackslashtextless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- \textbackslashtextbar\textbackslashtextbar
Shao, S., Tunc, C., Satam, P., Hariri, S..  2017.  Real-Time IRC Threat Detection Framework. 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W). :318–323.

Most of the social media platforms generate a massive amount of raw data that is slow-paced. On the other hand, Internet Relay Chat (IRC) protocol, which has been extensively used by hacker community to discuss and share their knowledge, facilitates fast-paced and real-time text communications. Previous studies of malicious IRC behavior analysis were mostly either offline or batch processing. This results in a long response time for data collection, pre-processing, and threat detection. However, since the threats can use the latest vulnerabilities to exploit systems (e.g. zero-day attack) and which can spread fast using IRC channels. Current IRC channel monitoring techniques cannot provide the required fast detection and alerting. In this paper, we present an alternative approach to overcome this limitation by providing real-time and autonomic threat detection in IRC channels. We demonstrate the capabilities of our approach using as an example the shadow brokers' leak exploit (the exploit leveraged by WannaCry ransomware attack) that was captured and detected by our framework.

Paramathma, M. K., Devaraj, D., Reddy, B. S..  2016.  Artificial neural network based static security assessment module using PMU measurements for smart grid application. 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS). :1–5.

Power system security is one of the key issues in the operation of smart grid system. Evaluation of power system security is a big challenge considering all the contingencies, due to huge computational efforts involved. Phasor measurement unit plays a vital role in real time power system monitoring and control. This paper presents static security assessment scheme for large scale inter connected power system with Phasor measurement unit using Artificial Neural Network. Voltage magnitude and phase angle are used as input variables of the ANN. The optimal location of PMU under base case and critical contingency cases are determined using Genetic algorithm. The performance of the proposed optimization model was tested with standard IEEE 30 bus system incorporating zero injection buses and successful results have been obtained.

Yap, B. L., Baskaran, V. M..  2016.  Active surveillance using depth sensing technology \#8212; Part I: Intrusion detection. 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). :1–2.

In part I of a three-part series on active surveillance using depth-sensing technology, this paper proposes an algorithm to identify outdoor intrusion activities by monitoring skeletal positions from Microsoft Kinect sensor in real-time. This algorithm implements three techniques to identify a premise intrusion. The first technique observes a boundary line along the wall (or fence) of a surveilled premise for skeletal trespassing detection. The second technique observes the duration of a skeletal object within a region of a surveilled premise for loitering detection. The third technique analyzes the differences in skeletal height to identify wall climbing. Experiment results suggest that the proposed algorithm is able to detect trespassing, loitering and wall climbing at a rate of 70%, 85% and 80% respectively.

Shahrak, M. Z., Ye, M., Swaminathan, V., Wei, S..  2016.  Two-way real time multimedia stream authentication using physical unclonable functions. 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). :1–4.

Multimedia authentication is an integral part of multimedia signal processing in many real-time and security sensitive applications, such as video surveillance. In such applications, a full-fledged video digital rights management (DRM) mechanism is not applicable due to the real time requirement and the difficulties in incorporating complicated license/key management strategies. This paper investigates the potential of multimedia authentication from a brand new angle by employing hardware-based security primitives, such as physical unclonable functions (PUFs). We show that the hardware security approach is not only capable of accomplishing the authentication for both the hardware device and the multimedia stream but, more importantly, introduce minimum performance, resource, and power overhead. We justify our approach using a prototype PUF implementation on Xilinx FPGA boards. Our experimental results on the real hardware demonstrate the high security and low overhead in multimedia authentication obtained by using hardware security approaches.

Yu, F., Chen, L., Zhang, H..  2016.  Virtual TPM Dynamic Trust Extension Suitable for Frequent Migrations. 2016 IEEE Trustcom/BigDataSE/ISPA. :57–65.

This paper has presented an approach of vTPM (virtual Trusted Platform Module) Dynamic Trust Extension (DTE) to satisfy the requirements of frequent migrations. With DTE, vTPM is a delegation of the capability of signing attestation data from the underlying pTPM (physical TPM), with one valid time token issued by an Authentication Server (AS). DTE maintains a strong association between vTPM and its underlying pTPM, and has clear distinguishability between vTPM and pTPM because of the different security strength of the two types of TPM. In DTE, there is no need for vTPM to re-acquire Identity Key (IK) certificate(s) after migration, and pTPM can have a trust revocation in real time. Furthermore, DTE can provide forward security. Seen from the performance measurements of its prototype, DTE is feasible.

Tan, B., Biglari-Abhari, M., Salcic, Z..  2016.  A system-level security approach for heterogeneous MPSoCs. 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP). :74–81.

Embedded systems are becoming increasingly complex as designers integrate different functionalities into a single application for execution on heterogeneous hardware platforms. In this work we propose a system-level security approach in order to provide isolation of tasks without the need to trust a central authority at run-time. We discuss security requirements that can be found in complex embedded systems that use heterogeneous execution platforms, and by regulating memory access we create mechanisms that allow safe use of shared IP with direct memory access, as well as shared libraries. We also present a prototype Isolation Unit that checks memory transactions and allows for dynamic configuration of permissions.

Baloglu, B..  2016.  How to find and fix software vulnerabilities with coverity static analysis. 2016 IEEE Cybersecurity Development (SecDev). :153–153.
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. This hands-on workshop will combine minimal instruction with strong hands-on emphasis on practical lab exercises for attendees to practice the topics covered - the expected duration will be 90 minutes. The hands-on labs will give attendees the opportunity to run real-time Coverity analysis on a number of given codebases, to find vulnerabilities in both (a) and (b) categories (buffer overruns, cross-site scripting XSS, SQL Injections etc. with actionable advice on approaches and options to eliminate them The workshop attendees will be given access to their own dedicated virtual machines in our cloud-based lab platform. The VMs will be pre-configured with all the necessary software and sample codebases. Participant should bring their laptops with Wi-Fi network card, and a contemporary browser, such as Chrome, IE, FF, Safari or similar with HTML5 support, which they should run to validate compliance with our pre-check test. At the session start, attendees will be provided with access codes to remotely connect to their virtual lab machines using the Wi-Fi connectivity provided by the IEEE Sec Dev 2016 Conference.
Cao, B., Wang, Z., Shi, H., Yin, Y..  2015.  Research and practice on Aluminum Industry 4.0. 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP). :517–521.

This paper presents a six-layer Aluminum Industry 4.0 architecture for the aluminum production and full lifecycle supply chain management. It integrates a series of innovative technologies, including the IoT sensing physical system, industrial cloud platform for data management, model-driven and big data driven analysis & decision making, standardization & securitization intelligent control and management, as well as visual monitoring and backtracking process etc. The main relevant control models are studied. The applications of real-time accurate perception & intelligent decision technology in the aluminum electrolytic industry are introduced.

Burnap, P., Javed, A., Rana, O. F., Awan, M. S..  2015.  Real-time classification of malicious URLs on Twitter using machine activity data. 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). :970–977.

Massive online social networks with hundreds of millions of active users are increasingly being used by Cyber criminals to spread malicious software (malware) to exploit vulnerabilities on the machines of users for personal gain. Twitter is particularly susceptible to such activity as, with its 140 character limit, it is common for people to include URLs in their tweets to link to more detailed information, evidence, news reports and so on. URLs are often shortened so the endpoint is not obvious before a person clicks the link. Cyber criminals can exploit this to propagate malicious URLs on Twitter, for which the endpoint is a malicious server that performs unwanted actions on the person's machine. This is known as a drive-by-download. In this paper we develop a machine classification system to distinguish between malicious and benign URLs within seconds of the URL being clicked (i.e. `real-time'). We train the classifier using machine activity logs created while interacting with URLs extracted from Twitter data collected during a large global event - the Superbowl - and test it using data from another large sporting event - the Cricket World Cup. The results show that machine activity logs produce precision performances of up to 0.975 on training data from the first event and 0.747 on a test data from a second event. Furthermore, we examine the properties of the learned model to explain the relationship between machine activity and malicious software behaviour, and build a learning curve for the classifier to illustrate that very small samples of training data can be used with only a small detriment to performance.

Tchilinguirian, G. J., Erickson, K. G..  2015.  Securing MDSplus for the NSTX-U Digital Coil Protection System. 2015 IEEE 26th Symposium on Fusion Engineering (SOFE). :1–4.

NSTX used MDSplus extensively to record data, relay information and control data acquisition hardware. For NSTX-U the same functionality is expected as well as an expansion into the realm of securely maintaining parameters for machine protection. Specifically, we designed the Digital Coil Protection System (DCPS) to use MDSplus to manage our physical and electrical limit values and relay information about the state of our acquisition system to DCPS. Additionally, test and development systems need to use many of the same resources concurrently without causing interference with other critical systems. Further complications include providing access to critical, protected data without risking changes being made to it by unauthorized users or through unsupported or uncontrolled methods either maliciously or unintentionally. To achieve a level of confidence with an existing software system designed with minimal security controls, a number of changes to how MDSplus is used were designed and implemented. Trees would need to be verified and checked for changes before use. Concurrent creation of trees from vastly different use-cases and varying requirements would need to be supported. This paper will further discuss the impetus for developing such designs and the methods used to implement them.

D. Kergl.  2015.  "Enhancing Network Security by Software Vulnerability Detection Using Social Media Analysis Extended Abstract". 2015 IEEE International Conference on Data Mining Workshop (ICDMW). :1532-1533.

Detecting attacks that are based on unknown security vulnerabilities is a challenging problem. The timely detection of attacks based on hitherto unknown vulnerabilities is crucial for protecting other users and systems from being affected as well. To know the attributes of a novel attack's target system can support automated reconfiguration of firewalls and sending alerts to administrators of other vulnerable targets. We suggest a novel approach of post-incident intrusion detection by utilizing information gathered from real-time social media streams. To accomplish this we take advantage of social media users posting about incidents that affect their user accounts of attacked target systems or their observations about misbehaving online services. Combining knowledge of the attacked systems and reported incidents, we should be able to recognize patterns that define the attributes of vulnerable systems. By matching detected attribute sets with those attributes of well-known attacks, we furthermore should be able to link attacks to already existing entries in the Common Vulnerabilities and Exposures database. If a link to an existing entry is not found, we can assume to have detected an exploitation of an unknown vulnerability, i.e., a zero day exploit or the result of an advanced persistent threat. This finding could also be used to direct efforts of examining vulnerabilities of attacked systems and therefore lead to faster patch deployment.

D. L. Schales, X. Hu, J. Jang, R. Sailer, M. P. Stoecklin, T. Wang.  2015.  "FCCE: Highly scalable distributed Feature Collection and Correlation Engine for low latency big data analytics". 2015 IEEE 31st International Conference on Data Engineering. :1316-1327.

In this paper, we present the design, architecture, and implementation of a novel analysis engine, called Feature Collection and Correlation Engine (FCCE), that finds correlations across a diverse set of data types spanning over large time windows with very small latency and with minimal access to raw data. FCCE scales well to collecting, extracting, and querying features from geographically distributed large data sets. FCCE has been deployed in a large production network with over 450,000 workstations for 3 years, ingesting more than 2 billion events per day and providing low latency query responses for various analytics. We explore two security analytics use cases to demonstrate how we utilize the deployment of FCCE on large diverse data sets in the cyber security domain: 1) detecting fluxing domain names of potential botnet activity and identifying all the devices in the production network querying these names, and 2) detecting advanced persistent threat infection. Both evaluation results and our experience with real-world applications show that FCCE yields superior performance over existing approaches, and excels in the challenging cyber security domain by correlating multiple features and deriving security intelligence.

X. Feng, Z. Zheng, P. Hu, D. Cansever, P. Mohapatra.  2015.  "Stealthy attacks meets insider threats: A three-player game model". MILCOM 2015 - 2015 IEEE Military Communications Conference. :25-30.

Advanced persistent threat (APT) is becoming a major threat to cyber security. As APT attacks are often launched by well funded entities that are persistent and stealthy in achieving their goals, they are highly challenging to combat in a cost-effective way. The situation becomes even worse when a sophisticated attacker is further assisted by an insider with privileged access to the inside information. Although stealthy attacks and insider threats have been considered separately in previous works, the coupling of the two is not well understood. As both types of threats are incentive driven, game theory provides a proper tool to understand the fundamental tradeoffs involved. In this paper, we propose the first three-player attacker-defender-insider game to model the strategic interactions among the three parties. Our game extends the two-player FlipIt game model for stealthy takeover by introducing an insider that can trade information to the attacker for a profit. We characterize the subgame perfect equilibria of the game with the defender as the leader and the attacker and the insider as the followers, under two different information trading processes. We make various observations and discuss approaches for achieving more efficient defense in the face of both APT and insider threats.

Rui Zhou, Rong Min, Qi Yu, Chanjuan Li, Yong Sheng, Qingguo Zhou, Xuan Wang, Kuan-Ching Li.  2014.  Formal Verification of Fault-Tolerant and Recovery Mechanisms for Safe Node Sequence Protocol. Advanced Information Networking and Applications (AINA), 2014 IEEE 28th International Conference on. :813-820.

Fault-tolerance has huge impact on embedded safety-critical systems. As technology that assists to the development of such improvement, Safe Node Sequence Protocol (SNSP) is designed to make part of such impact. In this paper, we present a mechanism for fault-tolerance and recovery based on the Safe Node Sequence Protocol (SNSP) to strengthen the system robustness, from which the correctness of a fault-tolerant prototype system is analyzed and verified. In order to verify the correctness of more than thirty failure modes, we have partitioned the complete protocol state machine into several subsystems, followed to the injection of corresponding fault classes into dedicated independent models. Experiments demonstrate that this method effectively reduces the size of overall state space, and verification results indicate that the protocol is able to recover from the fault model in a fault-tolerant system and continue to operate as errors occur.

Ravindran, K., Mukhopadhyay, S., Sidhanta, S., Sabbir, A..  2014.  Managing shared contexts in distributed multi-player game systems. Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on. :1-8.

In this paper, we consider the impact of a weaker model of eventual consistency on distributed multi-player games. This model is suitable for networks in which hosts can leave and join at anytime, e.g., in an intermittently connected environment. Such a consistency model is provided by the Secure Infrastructure for Networked Systems (SINS) [24], a reliable middleware framework. SINS allows agents to communicate asynchronously through a distributed transactional key-value store using anonymous publish-subscribe. It uses Lamport's Paxos protocol [17] to replicate state. We consider a multi-player maze game as example to illustrate our consistency model and the impact of network losses/delays therein. The framework based on SINS presented herein provides a vehicle for studying the effect of human elements participating in collaborative simulation of a physical world as in war games.

Boleng, J., Novakouski, M., Cahill, G., Simanta, S., Morris, E..  2014.  Fusing Open Source Intelligence and Handheld Situational Awareness: Benghazi Case Study. Military Communications Conference (MILCOM), 2014 IEEE. :1421-1426.

This paper reports the results and findings of a historical analysis of open source intelligence (OSINT) information (namely Twitter data) surrounding the events of the September 11, 2012 attack on the US Diplomatic mission in Benghazi, Libya. In addition to this historical analysis, two prototype capabilities were combined for a table top exercise to explore the effectiveness of using OSINT combined with a context aware handheld situational awareness framework and application to better inform potential responders as the events unfolded. Our experience shows that the ability to model sentiment, trends, and monitor keywords in streaming social media, coupled with the ability to share that information to edge operators can increase their ability to effectively respond to contingency operations as they unfold.

Dressler, J., Bowen, C.L., Moody, W., Koepke, J..  2014.  Operational data classes for establishing situational awareness in cyberspace. Cyber Conflict (CyCon 2014), 2014 6th International Conference On. :175-186.

The United States, including the Department of Defense, relies heavily on information systems and networking technologies to efficiently conduct a wide variety of missions across the globe. With the ever-increasing rate of cyber attacks, this dependency places the nation at risk of a loss of confidentiality, integrity, and availability of its critical information resources; degrading its ability to complete the mission. In this paper, we introduce the operational data classes for establishing situational awareness in cyberspace. A system effectively using our key information components will be able to provide the nation's leadership timely and accurate information to gain an understanding of the operational cyber environment to enable strategic, operational, and tactical decision-making. In doing so, we present, define and provide examples of our key classes of operational data for cyber situational awareness and present a hypothetical case study demonstrating how they must be consolidated to provide a clear and relevant picture to a commander. In addition, current organizational and technical challenges are discussed, and areas for future research are addressed.

Major, S., Fekovic, E..  2014.  Securing intelligent substations: Real-time situational awareness. Energy Conference (ENERGYCON), 2014 IEEE International. :711-715.

A system implementing real-time situational awareness through discovery, prevention, detection, response, audit, and management capabilities is seen as central to facilitating the protection of critical infrastructure systems. The effectiveness of providing such awareness technologies for electrical distribution companies is being evaluated in a series of field trials: (i) Substation Intrusion Detection / Prevention System (IDPS) and (ii) Security Information and Event Management (SIEM) System. These trials will help create a realistic case study on the effectiveness of such technologies with the view of forming a framework for critical infrastructure cyber security defense systems of the future.

Yu Li, Rui Dai, Junjie Zhang.  2014.  Morphing communications of Cyber-Physical Systems towards moving-target defense. Communications (ICC), 2014 IEEE International Conference on. :592-598.

Since the massive deployment of Cyber-Physical Systems (CPSs) calls for long-range and reliable communication services with manageable cost, it has been believed to be an inevitable trend to relay a significant portion of CPS traffic through existing networking infrastructures such as the Internet. Adversaries who have access to networking infrastructures can therefore eavesdrop network traffic and then perform traffic analysis attacks in order to identify CPS sessions and subsequently launch various attacks. As we can hardly prevent all adversaries from accessing network infrastructures, thwarting traffic analysis attacks becomes indispensable. Traffic morphing serves as an effective means towards this direction. In this paper, a novel traffic morphing algorithm, CPSMorph, is proposed to protect CPS sessions. CPSMorph maintains a number of network sessions whose distributions of inter-packet delays are statistically indistinguishable from those of typical network sessions. A CPS message will be sent through one of these sessions with assured satisfaction of its time constraint. CPSMorph strives to minimize the overhead by dynamically adjusting the morphing process. It is characterized by low complexity as well as high adaptivity to changing dynamics of CPS sessions. Experimental results have shown that CPSMorph can effectively performing traffic morphing for real-time CPS messages with moderate overhead.

Miloslavskaya, N., Senatorov, M., Tolstoy, A., Zapechnikov, S..  2014.  Information Security Maintenance Issues for Big Security-Related Data. Future Internet of Things and Cloud (FiCloud), 2014 International Conference on. :361-366.

The need to protect big data, particularly those relating to information security (IS) maintenance (ISM) of an enterprise's IT infrastructure, is shown. A worldwide experience of addressing big data ISM issues is briefly summarized and a big data protection problem statement is formulated. An infrastructure for big data ISM is proposed. New applications areas for big data IT after addressing ISM issues are listed in conclusion.

Ling-Xi Peng, Tian-Wei Chen.  2014.  Automated Intrusion Response System Algorithm with Danger Theory. Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2014 International Conference on. :31-34.

Intrusion response is a new generation of technology basing on active defence idea, which has very prominent significance on the protection of network security. However, the existing automatic intrusion response systems are difficult to judge the real "danger" of invasion or attack. In this study, an immune-inspired adaptive automated intrusion response system model, named as AIAIM, was given. With the descriptions of self, non-self, memory detector, mature detector and immature detector of the network transactions, the real-time network danger evaluation equations of host and network are built up. Then, the automated response polices are taken or adjusted according to the real-time danger and attack intensity, which not only solve the problem that the current automated response system models could not detect the true intrusions or attack actions, but also greatly reduce the response times and response costs. Theory analysis and experimental results prove that AIAIM provides a positive and active network security method, which will help to overcome the limitations of traditional passive network security system.