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Chi, Po-Wen, Wang, Ming-Hung, Zheng, Yu.  2020.  SandboxNet: An Online Malicious SDN Application Detection Framework for SDN Networking. 2020 International Computer Symposium (ICS). :397—402.

Software Defined Networking (SDN) is a concept that decouples the control plane and the user plane. So the network administrator can easily control the network behavior through its own programs. However, the administrator may unconsciously apply some malicious programs on SDN controllers so that the whole network may be under the attacker’s control. In this paper, we discuss the malicious software issue on SDN networks. We use the idea of sandbox to propose a sandbox network called SanboxNet. We emulate a virtual isolated network environment to verify the SDN application functions. With continuous monitoring, we can locate the suspicious SDN applications. We also consider the sandbox-evading issue in our framework. The emulated networks and the real world networks will be indistinguishable to the SDN controller.

Kumar, Rahul, Sethi, Kamalakanta, Prajapati, Nishant, Rout, Rashmi Ranjan, Bera, Padmalochan.  2020.  Machine Learning based Malware Detection in Cloud Environment using Clustering Approach. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—7.

Enforcing security and resilience in a cloud platform is an essential but challenging problem due to the presence of a large number of heterogeneous applications running on shared resources. A security analysis system that can detect threats or malware must exist inside the cloud infrastructure. Much research has been done on machine learning-driven malware analysis, but it is limited in computational complexity and detection accuracy. To overcome these drawbacks, we proposed a new malware detection system based on the concept of clustering and trend micro locality sensitive hashing (TLSH). We used Cuckoo sandbox, which provides dynamic analysis reports of files by executing them in an isolated environment. We used a novel feature extraction algorithm to extract essential features from the malware reports obtained from the Cuckoo sandbox. Further, the most important features are selected using principal component analysis (PCA), random forest, and Chi-square feature selection methods. Subsequently, the experimental results are obtained for clustering and non-clustering approaches on three classifiers, including Decision Tree, Random Forest, and Logistic Regression. The model performance shows better classification accuracy and false positive rate (FPR) as compared to the state-of-the-art works and non-clustering approach at significantly lesser computation cost.

Kishore, Pushkar, Barisal, Swadhin Kumar, Prasad Mohapatra, Durga.  2020.  JavaScript malware behaviour analysis and detection using sandbox assisted ensemble model. 2020 IEEE REGION 10 CONFERENCE (TENCON). :864—869.

Whenever any internet user visits a website, a scripting language runs in the background known as JavaScript. The embedding of malicious activities within the script poses a great threat to the cyberworld. Attackers take advantage of the dynamic nature of the JavaScript and embed malicious code within the website to download malware and damage the host. JavaScript developers obfuscate the script to keep it shielded from getting detected by the malware detectors. In this paper, we propose a novel technique for analysing and detecting JavaScript using sandbox assisted ensemble model. We extract the payload using malware-jail sandbox to get the real script. Upon getting the extracted script, we analyse it to define the features that are needed for creating the dataset. We compute Pearson's r between every feature for feature extraction. An ensemble model consisting of Sequential Minimal Optimization (SMO), Voted Perceptron and AdaBoost algorithm is used with voting technique to detect malicious JavaScript. Experimental results show that our proposed model can detect obfuscated and de-obfuscated malicious JavaScript with an accuracy of 99.6% and 0.03s detection time. Our model performs better than other state-of-the-art models in terms of accuracy and least training and detection time.

Herrera, Adrian.  2020.  Optimizing Away JavaScript Obfuscation. 2020 IEEE 20th International Working Conference on Source Code Analysis and Manipulation (SCAM). :215—220.

JavaScript is a popular attack vector for releasing malicious payloads on unsuspecting Internet users. Authors of this malicious JavaScript often employ numerous obfuscation techniques in order to prevent the automatic detection by antivirus and hinder manual analysis by professional malware analysts. Consequently, this paper presents SAFE-DEOBS, a JavaScript deobfuscation tool that we have built. The aim of SAFE-DEOBS is to automatically deobfuscate JavaScript malware such that an analyst can more rapidly determine the malicious script's intent. This is achieved through a number of static analyses, inspired by techniques from compiler theory. We demonstrate the utility of SAFE-DEOBS through a case study on real-world JavaScript malware, and show that it is a useful addition to a malware analyst's toolset.

Lee, Jae-Myeong, Hong, Sugwon.  2020.  Host-Oriented Approach to Cyber Security for the SCADA Systems. 2020 6th IEEE Congress on Information Science and Technology (CiSt). :151—155.
Recent cyberattacks targeting Supervisory Control and Data Acquisition (SCADA)/Industrial Control System(ICS) exploit weaknesses of host system software environment and take over the control of host processes in the host of the station network. We analyze the attack path of these attacks, which features how the attack hijacks the host in the network and compromises the operations of field device controllers. The paper proposes a host-based protection method, which can prevent malware penetration into the process memory by code injection attacks. The method consists of two protection schemes. One is to prevent file-based code injection such as DLL injection. The other is to prevent fileless code injection. The method traces changes in memory regions and determine whether the newly allocated memory is written with malicious codes. For this method, we show how a machine learning method can be adopted.
Zhang, Yunan, Xu, Aidong Xu, Jiang, Yixin.  2020.  Scalable and Accurate Binary Code Search Method Based on Simhash and Partial Trace. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :818—826.
Binary code search has received much attention recently due to its impactful applications, e.g., plagiarism detection, malware detection and software vulnerability auditing. However, developing an effective binary code search tool is challenging due to the gigantic syntax and structural differences in binaries resulted from different compilers, compiler options and malware family. In this paper, we propose a scalable and accurate binary search engine which performs syntactic matching by combining a set of key techniques to address the challenges above. The key contribution is binary code searching technique which combined function filtering and partial trace method to match the function code relatively quick and accurate. In addition, a simhash and basic information based function filtering is proposed to dramatically reduce the irrelevant target functions. Besides, we introduce a partial trace method for matching the shortlisted function accurately. The experimental results show that our method can find similar functions, even with the presence of program structure distortion, in a scalable manner.
Luo, Lan, Zhang, Yue, Zou, Cliff, Shao, Xinhui, Ling, Zhen, Fu, Xinwen.  2020.  On Runtime Software Security of TrustZone-M Based IoT Devices. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. :1–7.
Internet of Things (IoT) devices have been increasingly integrated into our daily life. However, such smart devices suffer a broad attack surface. Particularly, attacks targeting the device software at runtime are challenging to defend against if IoT devices use resource-constrained microcontrollers (MCUs). TrustZone-M, a TrustZone extension for MCUs, is an emerging security technique fortifying MCU based IoT devices. This paper presents the first security analysis of potential software security issues in TrustZone-M enabled MCUs. We explore the stack-based buffer overflow (BOF) attack for code injection, return-oriented programming (ROP) attack, heap-based BOF attack, format string attack, and attacks against Non-secure Callable (NSC) functions in the context of TrustZone-M. We validate these attacks using the Microchip SAM L11 MCU, which uses the ARM Cortex-M23 processor with the TrustZone-M technology. Strategies to mitigate these software attacks are also discussed.
Piplai, A., Ranade, P., Kotal, A., Mittal, S., Narayanan, S. N., Joshi, A..  2020.  Using Knowledge Graphs and Reinforcement Learning for Malware Analysis. 2020 IEEE International Conference on Big Data (Big Data). :2626—2633.

Machine learning algorithms used to detect attacks are limited by the fact that they cannot incorporate the back-ground knowledge that an analyst has. This limits their suitability in detecting new attacks. Reinforcement learning is different from traditional machine learning algorithms used in the cybersecurity domain. Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points. This is important for the cybersecurity domain, especially for malware detection and mitigation, as not all problems have a single, known, correct answer. Often, security researchers have to resort to guided trial and error to understand the presence of a malware and mitigate it.In this paper, we incorporate prior knowledge, represented as Cybersecurity Knowledge Graphs (CKGs), to guide the exploration of an RL algorithm to detect malware. CKGs capture semantic relationships between cyber-entities, including that mined from open source. Instead of trying out random guesses and observing the change in the environment, we aim to take the help of verified knowledge about cyber-attack to guide our reinforcement learning algorithm to effectively identify ways to detect the presence of malicious filenames so that they can be deleted to mitigate a cyber-attack. We show that such a guided system outperforms a base RL system in detecting malware.

Gui, J., Li, D., Chen, Z., Rhee, J., Xiao, X., Zhang, M., Jee, K., Li, Z., Chen, H..  2020.  APTrace: A Responsive System for Agile Enterprise Level Causality Analysis. 2020 IEEE 36th International Conference on Data Engineering (ICDE). :1701–1712.
While backtracking analysis has been successful in assisting the investigation of complex security attacks, it faces a critical dependency explosion problem. To address this problem, security analysts currently need to tune backtracking analysis manually with different case-specific heuristics. However, existing systems fail to fulfill two important system requirements to achieve effective backtracking analysis. First, there need flexible abstractions to express various types of heuristics. Second, the system needs to be responsive in providing updates so that the progress of backtracking analysis can be frequently inspected, which typically involves multiple rounds of manual tuning. In this paper, we propose a novel system, APTrace, to meet both of the above requirements. As we demonstrate in the evaluation, security analysts can effectively express heuristics to reduce more than 99.5% of irrelevant events in the backtracking analysis of real-world attack cases. To improve the responsiveness of backtracking analysis, we present a novel execution-window partitioning algorithm that significantly reduces the waiting time between two consecutive updates (especially, 57 times reduction for the top 1% waiting time).
Colbaugh, R., Glass, K., Bauer, T..  2013.  Dynamic information-theoretic measures for security informatics. 2013 IEEE International Conference on Intelligence and Security Informatics. :45–49.
Many important security informatics problems require consideration of dynamical phenomena for their solution; examples include predicting the behavior of individuals in social networks and distinguishing malicious and innocent computer network activities based on activity traces. While information theory offers powerful tools for analyzing dynamical processes, to date the application of information-theoretic methods in security domains has focused on static analyses (e.g., cryptography, natural language processing). This paper leverages information-theoretic concepts and measures to quantify the similarity of pairs of stochastic dynamical systems, and shows that this capability can be used to solve important problems which arise in security applications. We begin by presenting a concise review of the information theory required for our development, and then address two challenging tasks: 1.) characterizing the way influence propagates through social networks, and 2.) distinguishing malware from legitimate software based on the instruction sequences of the disassembled programs. In each application, case studies involving real-world datasets demonstrate that the proposed techniques outperform standard methods.
Ayub, M. A., Continella, A., Siraj, A..  2020.  An I/O Request Packet (IRP) Driven Effective Ransomware Detection Scheme using Artificial Neural Network. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). :319–324.
In recent times, there has been a global surge of ransomware attacks targeted at industries of various types and sizes from retail to critical infrastructure. Ransomware researchers are constantly coming across new kinds of ransomware samples every day and discovering novel ransomware families out in the wild. To mitigate this ever-growing menace, academia and industry-based security researchers have been utilizing unique ways to defend against this type of cyber-attacks. I/O Request Packet (IRP), a low-level file system I/O log, is a newly found research paradigm for defense against ransomware that is being explored frequently. As such in this study, to learn granular level, actionable insights of ransomware behavior, we analyze the IRP logs of 272 ransomware samples belonging to 18 different ransomware families captured during individual execution. We further our analysis by building an effective Artificial Neural Network (ANN) structure for successful ransomware detection by learning the underlying patterns of the IRP logs. We evaluate the ANN model with three different experimental settings to prove the effectiveness of our approach. The model demonstrates outstanding performance in terms of accuracy, precision score, recall score, and F1 score, i.e., in the range of 99.7%±0.2%.
Westland, T., Niu, N., Jha, R., Kapp, D., Kebede, T..  2020.  Relating the Empirical Foundations of Attack Generation and Vulnerability Discovery. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). :37–44.
Automatically generating exploits for attacks receives much attention in security testing and auditing. However, little is known about the continuous effect of automatic attack generation and detection. In this paper, we develop an analytic model to understand the cost-benefit tradeoffs in light of the process of vulnerability discovery. We develop a three-phased model, suggesting that the cumulative malware detection has a productive period before the rate of gain flattens. As the detection mechanisms co-evolve, the gain will likely increase. We evaluate our analytic model by using an anti-virus tool to detect the thousands of Trojans automatically created. The anti-virus scanning results over five months show the validity of the model and point out future research directions.
Zimmo, S., Refaey, A., Shami, A..  2020.  Trusted Boot for Embedded Systems Using Hypothesis Testing Benchmark. 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). :1—2.

Security has become a crucial consideration and is one of the most important design goals for an embedded system. This paper examines the type of boot sequence, and more specifically a trusted boot which utilizes the method of chain of trust. After defining these terms, this paper will examine the limitations of the existing safe boot, and finally propose the method of trusted boot based on hypothesis testing benchmark and the cost it takes to perform this method.

Moti, Z., Hashemi, S., Jahromi, A. N..  2020.  A Deep Learning-based Malware Hunting Technique to Handle Imbalanced Data. 2020 17th International ISC Conference on Information Security and Cryptology (ISCISC). :48–53.
Nowadays, with the increasing use of computers and the Internet, more people are exposed to cyber-security dangers. According to antivirus companies, malware is one of the most common threats of using the Internet. Therefore, providing a practical solution is critical. Current methods use machine learning approaches to classify malware samples automatically. Despite the success of these approaches, the accuracy and efficiency of these techniques are still inadequate, especially for multiple class classification problems and imbalanced training data sets. To mitigate this problem, we use deep learning-based algorithms for classification and generation of new malware samples. Our model is based on the opcode sequences, which are given to the model without any pre-processing. Besides, we use a novel generative adversarial network to generate new opcode sequences for oversampling minority classes. Also, we propose the model that is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) to classify malware samples. CNN is used to consider short-term dependency between features; while, LSTM is used to consider longer-term dependence. The experiment results show our method could classify malware to their corresponding family effectively. Our model achieves 98.99% validation accuracy.
Chauhan, R., Heydari, S. Shah.  2020.  Polymorphic Adversarial DDoS attack on IDS using GAN. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1–6.
Intrusion Detection systems are important tools in preventing malicious traffic from penetrating into networks and systems. Recently, Intrusion Detection Systems are rapidly enhancing their detection capabilities using machine learning algorithms. However, these algorithms are vulnerable to new unknown types of attacks that can evade machine learning IDS. In particular, they may be vulnerable to attacks based on Generative Adversarial Networks (GAN). GANs have been widely used in domains such as image processing, natural language processing to generate adversarial data of different types such as graphics, videos, texts, etc. We propose a model using GAN to generate adversarial DDoS attacks that can change the attack profile and can be undetected. Our simulation results indicate that by continuous changing of attack profile, defensive systems that use incremental learning will still be vulnerable to new attacks.
Sai, C. C., Prakash, C. S., Jose, J., Mana, S. C., Samhitha, B. K..  2020.  Analysing Android App Privacy Using Classification Algorithm. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184). :551–555.
The interface permits the client to scan for a subjective utility on the Play Store; the authorizations posting and the protection arrangement are then routinely recovered, on all events imaginable. The client has then the capability of choosing an interesting authorization, and a posting of pertinent sentences are separated with the guide of the privateer's inclusion and introduced to them, alongside a right depiction of the consent itself. Such an interface allows the client to rapidly assess the security-related dangers of an Android application, by utilizing featuring the pertinent segments of the privateer's inclusion and by introducing helpful data about shrewd authorizations. A novel procedure is proposed for the assessment of privateer's protection approaches with regards to Android applications. The gadget actualized widely facilitates the way toward understanding the security ramifications of placing in 1/3 birthday celebration applications and it has just been checked in a situation to feature troubling examples of uses. The gadget is created in light of expandability, and correspondingly inclines in the strategy can without trouble be worked in to broaden the unwavering quality and adequacy. Likewise, if your application handles non-open or delicate individual information, it would be ideal if you also allude to the extra necessities in the “Individual and Sensitive Information” territory underneath. These Google Play necessities are notwithstanding any prerequisites endorsed by method for material security or data assurance laws. It has been proposed that, an individual who needs to perform the establishment and utilize any 1/3 festival application doesn't perceive the significance and which methods for the consents mentioned by method for an application, and along these lines sincerely gives all the authorizations as a final product of which unsafe applications furthermore get set up and work their malevolent leisure activity in the rear of the scene.
Wang, B., Dou, Y., Sang, Y., Zhang, Y., Huang, J..  2020.  IoTCMal: Towards A Hybrid IoT Honeypot for Capturing and Analyzing Malware. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—7.

Nowadays, the emerging Internet-of-Things (IoT) emphasize the need for the security of network-connected devices. Additionally, there are two types of services in IoT devices that are easily exploited by attackers, weak authentication services (e.g., SSH/Telnet) and exploited services using command injection. Based on this observation, we propose IoTCMal, a hybrid IoT honeypot framework for capturing more comprehensive malicious samples aiming at IoT devices. The key novelty of IoTC-MAL is three-fold: (i) it provides a high-interactive component with common vulnerable service in real IoT device by utilizing traffic forwarding technique; (ii) it also contains a low-interactive component with Telnet/SSH service by running in virtual environment. (iii) Distinct from traditional low-interactive IoT honeypots[1], which only analyze family categories of malicious samples, IoTCMal primarily focuses on homology analysis of malicious samples. We deployed IoTCMal on 36 VPS1 instances distributed in 13 cities of 6 countries. By analyzing the malware binaries captured from IoTCMal, we discover 8 malware families controlled by at least 11 groups of attackers, which mainly launched DDoS attacks and digital currency mining. Among them, about 60% of the captured malicious samples ran in ARM or MIPs architectures, which are widely used in IoT devices.

Murali, R., Velayutham, C. S..  2020.  A Conceptual Direction on Automatically Evolving Computer Malware using Genetic and Evolutionary Algorithms. 2020 International Conference on Inventive Computation Technologies (ICICT). :226—229.

The widespread use of computing devices and the heavy dependence on the internet has evolved the cyberspace to a cyber world - something comparable to an artificial world. This paper focuses on one of the major problems of the cyber world - cyber security or more specifically computer malware. We show that computer malware is a perfect example of an artificial ecosystem with a co-evolutionary predator-prey framework. We attempt to merge the two domains of biologically inspired computing and computer malware. Under the aegis of proactive defense, this paper discusses the possibilities, challenges and opportunities in fusing evolutionary computing techniques with malware creation.

Akram, B., Ogi, D..  2020.  The Making of Indicator of Compromise using Malware Reverse Engineering Techniques. 2020 International Conference on ICT for Smart Society (ICISS). CFP2013V-ART:1—6.

Malware threats often go undetected immediately, because attackers can camouflage well within the system. The users realize this after the devices stop working and cause harm for them. One way to deceive malicious content detection, malware authors use packers. Malware analysis is an activity to gain knowledge about malware. Reverse engineering is a technique used to identify and deal with new viruses or to understand malware behavior. Therefore, this technique can be the right choice for conducting malware analysis, especially for malware with packers. The results of the analysis are used as a source for making creating indicator of compromise in the YARA rule format. YARA rule is used as a component for detecting malware using the indicators obtained in the analysis process.

Susanto, Stiawan, D., Arifin, M. A. S., Idris, M. Y., Budiarto, R..  2020.  IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning. 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI). :15—20.

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.

Memos, V. A., Psannis, K. E..  2020.  AI-Powered Honeypots for Enhanced IoT Botnet Detection. 2020 3rd World Symposium on Communication Engineering (WSCE). :64—68.

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.

Lingenfelter, B., Vakilinia, I., Sengupta, S..  2020.  Analyzing Variation Among IoT Botnets Using Medium Interaction Honeypots. 2020 10th Annual Computing and Communication Workshop and Conference (CCWC). :0761—0767.

Through analysis of sessions in which files were created and downloaded on three Cowrie SSH/Telnet honeypots, we find that IoT botnets are by far the most common source of malware on connected systems with weak credentials. We detail our honeypot configuration and describe a simple method for listing near-identical malicious login sessions using edit distance. A large number of IoT botnets attack our honeypots, but the malicious sessions which download botnet software to the honeypot are almost all nearly identical to one of two common attack patterns. It is apparent that the Mirai worm is still the dominant botnet software, but has been expanded and modified by other hackers. We also find that the same loader devices deploy several different botnet malware strains to the honeypot over the course of a 40 day period, suggesting multiple botnet deployments from the same source. We conclude that Mirai continues to be adapted but can be effectively tracked using medium interaction honeypots such as Cowrie.

Muhammad, A., Asad, M., Javed, A. R..  2020.  Robust Early Stage Botnet Detection using Machine Learning. 2020 International Conference on Cyber Warfare and Security (ICCWS). :1—6.

Among the different types of malware, botnets are rising as the most genuine risk against cybersecurity as they give a stage to criminal operations (e.g., Distributed Denial of Service (DDOS) attacks, malware dispersal, phishing, and click fraud and identity theft). Existing botnet detection techniques work only on specific botnet Command and Control (C&C) protocols and lack in providing early-stage botnet detection. In this paper, we propose an approach for early-stage botnet detection. The proposed approach first selects the optimal features using feature selection techniques. Next, it feeds these features to machine learning classifiers to evaluate the performance of the botnet detection. Experiments reveals that the proposed approach efficiently classifies normal and malicious traffic at an early stage. The proposed approach achieves the accuracy of 99%, True Positive Rate (TPR) of 0.99 %, and False Positive Rate (FPR) of 0.007 % and provide an efficient detection rate in comparison with the existing approach.

Kamilin, M. H. B., Yamaguchi, S..  2020.  White-Hat Worm Launcher Based on Deep Learning in Botnet Defense System. 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). :1—2.

This paper proposes a deep learning-based white-hat worm launcher in Botnet Defense System (BDS). BDS uses white-hat botnets to defend an IoT system against malicious botnets. White-hat worm launcher literally launches white-hat worms to create white-hat botnets according to the strategy decided by BDS. The proposed launcher learns with deep learning where is the white-hat worms' right place to successfully drive out malicious botnets. Given a system situation invaded by malicious botnets, it predicts a worms' placement by the learning result and launches them. We confirmed the effect of the proposed launcher through simulating evaluation.

Hajizadeh, M., Afraz, N., Ruffini, M., Bauschert, T..  2020.  Collaborative Cyber Attack Defense in SDN Networks using Blockchain Technology. 2020 6th IEEE Conference on Network Softwarization (NetSoft). :487—492.

The legacy security defense mechanisms cannot resist where emerging sophisticated threats such as zero-day and malware campaigns have profoundly changed the dimensions of cyber-attacks. Recent studies indicate that cyber threat intelligence plays a crucial role in implementing proactive defense operations. It provides a knowledge-sharing platform that not only increases security awareness and readiness but also enables the collaborative defense to diminish the effectiveness of potential attacks. In this paper, we propose a secure distributed model to facilitate cyber threat intelligence sharing among diverse participants. The proposed model uses blockchain technology to assure tamper-proof record-keeping and smart contracts to guarantee immutable logic. We use an open-source permissioned blockchain platform, Hyperledger Fabric, to implement the blockchain application. We also utilize the flexibility and management capabilities of Software-Defined Networking to be integrated with the proposed sharing platform to enhance defense perspectives against threats in the system. In the end, collaborative DDoS attack mitigation is taken as a case study to demonstrate our approach.