Visible to the public Biblio

Found 235 results

Filters: Keyword is Network security  [Clear All Filters]
2020-09-18
Zolanvari, Maede, Teixeira, Marcio A., Gupta, Lav, Khan, Khaled M., Jain, Raj.  2019.  Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things. IEEE Internet of Things Journal. 6:6822—6834.
It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.
Yao, Bing, Zhao, Meimei, Mu, Yarong, Sun, Yirong, Zhang, Xiaohui, Zhang, Mingjun, Yang, Sihua.  2019.  Matrices From Topological Graphic Coding of Network Security. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 1:1992—1996.
Matrices as mathematical models have been used in each branch of scientific fields for hundred years. We propose a new type of matrices, called topological coding matrices (Topcode-matrices). Topcode-matrices show us the following advantages: Topcode-matrices can be saved in computer easily and run quickly in computation; since a Topcode-matrix corresponds two or more Topsnut-gpws, so Topcode-matrices can be used to encrypt networks such that the encrypted networks have higher security; Topcode-matrices can be investigated and applied by people worked in more domains; Topcode-matrices can help us to form new operations, new parameters and new topics of graph theory, such as vertex/edge splitting operations and connectivities of graphs. Several properties and applications on Topcode-matrices, and particular Topcode-matrices, as well as unknown problems are introduced.
2020-09-11
ALEKSIEVA, Yulia, VALCHANOV, Hristo, ALEKSIEVA, Veneta.  2019.  An approach for host based botnet detection system. 2019 16th Conference on Electrical Machines, Drives and Power Systems (ELMA). :1—4.
Most serious occurrence of modern malware is Botnet. Botnet is a rapidly evolving problem that is still not well understood and studied. One of the main goals for modern network security is to create adequate techniques for the detection and eventual termination of Botnet threats. The article presents an approach for implementing a host-based Intrusion Detection System for Botnet attack detection. The approach is based on a variation of a genetic algorithm to detect anomalies in a case of attacks. An implementation of the approach and experimental results are presented.
2020-09-04
Nursetyo, Arif, Ignatius Moses Setiadi, De Rosal, Rachmawanto, Eko Hari, Sari, Christy Atika.  2019.  Website and Network Security Techniques against Brute Force Attacks using Honeypot. 2019 Fourth International Conference on Informatics and Computing (ICIC). :1—6.
The development of the internet and the web makes human activities more practical, comfortable, and inexpensive. So that the use of the internet and websites is increasing in various ways. Public networks make the security of websites vulnerable to attack. This research proposes a Honeypot for server security against attackers who want to steal data by carrying out a brute force attack. In this research, Honeypot is integrated on the server to protect the server by creating a shadow server. This server is responsible for tricking the attacker into not being able to enter the original server. Brute force attacks tested using Medusa tools. With the application of Honeypot on the server, it is proven that the server can be secured from the attacker. Even the log of activities carried out by the attacker in the shadow server is stored in the Kippo log activities.
Velan, Petr, Husák, Martin, Tovarňák, Daniel.  2018.  Rapid prototyping of flow-based detection methods using complex event processing. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. :1—3.
Detection of network attacks is the first step to network security. Many different methods for attack detection were proposed in the past. However, descriptions of these methods are often not complete and it is difficult to verify that the actual implementation matches the description. In this demo paper, we propose to use Complex Event Processing (CEP) for developing detection methods based on network flows. By writing the detection methods in an Event Processing Language (EPL), we can address the above-mentioned problems. The SQL-like syntax of most EPLs is easily readable so the detection method is self-documented. Moreover, it is directly executable in the CEP system, which eliminates inconsistencies between documentation and implementation. The demo will show a running example of a multi-stage HTTP brute force attack detection using Esper and its EPL.
2020-08-28
Mulinka, Pavol, Casas, Pedro, Vanerio, Juan.  2019.  Continuous and Adaptive Learning over Big Streaming Data for Network Security. 2019 IEEE 8th International Conference on Cloud Networking (CloudNet). :1—4.
Continuous and adaptive learning is an effective learning approach when dealing with highly dynamic and changing scenarios, where concept drift often happens. In a continuous, stream or adaptive learning setup, new measurements arrive continuously and there are no boundaries for learning, meaning that the learning model has to decide how and when to (re)learn from these new data constantly. We address the problem of adaptive and continual learning for network security, building dynamic models to detect network attacks in real network traffic. The combination of fast and big network measurements data with the re-training paradigm of adaptive learning imposes complex challenges in terms of data processing speed, which we tackle by relying on big data platforms for parallel stream processing. We build and benchmark different adaptive learning models on top of a novel big data analytics platform for network traffic monitoring and analysis tasks, and show that high speed-up computations (as high as × 6) can be achieved by parallelizing off-the-shelf stream learning approaches.
2020-08-24
Renners, Leonard, Heine, Felix, Kleiner, Carsten, Rodosek, Gabi Dreo.  2019.  Adaptive and Intelligible Prioritization for Network Security Incidents. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–8.
Incident prioritization is nowadays a part of many approaches and tools for network security and risk management. However, the dynamic nature of the problem domain is often unaccounted for. That is, the prioritization is typically based on a set of static calculations, which are rarely adjusted. As a result, incidents are incorrectly prioritized, leading to an increased and misplaced effort in the incident response. A higher degree of automation could help to address this problem. In this paper, we explicitly consider flaws in the prioritization an unalterable circumstance. We propose an adaptive incident prioritization, which allows to automate certain tasks for the prioritization model management in order to continuously assess and improve a prioritization model. At the same time, we acknowledge the human analyst as the focal point and propose to keep the human in the loop, among others by treating understandability as a crucial requirement.
2020-08-13
Yang, Huiting, Bai, Yunxiao, Zou, Zhenwan, Shi, Yuanyuan, Chen, Shuting, Ni, Chenxi.  2019.  Research on Security Self-defense of Power Information Network Based on Artificial Intelligence. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 1:1248—1251.
By studying the problems of network information security in power system, this paper proposes a self-defense research and solution for power information network based on artificial intelligence. At the same time, it proposes active defense new technologies such as vulnerability scanning, baseline scanning, network security attack and defense drills in power information network security, aiming at improving the security level of network information and ensuring the security of the information network in the power system.
2020-08-10
Wasi, Sarwar, Shams, Sarmad, Nasim, Shahzad, Shafiq, Arham.  2019.  Intrusion Detection Using Deep Learning and Statistical Data Analysis. 2019 4th International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). :1–5.
Innovation and creativity have played an important role in the development of every field of life, relatively less but it has created several problems too. Intrusion detection is one of those problems which became difficult with the advancement in computer networks, multiple researchers with multiple techniques have come forward to solve this crucial issue, but network security is still a challenge. In our research, we have come across an idea to detect intrusion using a deep learning algorithm in combination with statistical data analysis of KDD cup 99 datasets. Firstly, we have applied statistical analysis on the given data set to generate a simplified form of data, so that a less complex binary classification model of artificial neural network could apply for data classification. Our system has decreased the complexity of the system and has improved the response time.
2020-08-03
POLAT, Hüseyin, POLAT, Onur, SÖĞÜT, Esra, ERDEM, O. Ayhan.  2019.  Performance Analysis of Between Software Defined Wireless Network and Mobile Ad Hoc Network Under DoS Attack. 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). :1–5.

The traditional network used today is unable to meet the increasing needs of technology in terms of management, scaling, and performance criteria. Major developments in information and communication technologies show that the traditional network structure is quite lacking in meeting the current requirements. In order to solve these problems, Software Defined Network (SDN) is capable of responding as it, is flexible, easier to manage and offers a new structure. Software Defined Networks have many advantages over traditional network structure. However, it also brings along many security threats due to its new architecture. For example, the DoS attack, which overloads the controller's processing and communication capacity in the SDN structure, is a significant threat. Mobile Ad Hoc Network (MANET), which is one of the wireless network technologies, is different from SDN technology. MANET is exposed to various attacks such as DoS due to its security vulnerabilities. The aim of the study is to reveal the security problems in SDN structure presented with a new understanding. This is based on the currently used network structures such as MANET. The study consists of two parts. First, DoS attacks against the SDN controller were performed. Different SDN controllers were used for more accurate results. Second, MANET was established and DoS attacks against this network were performed. Different MANET routing protocols were used for more accurate results. According to the scenario, attacks were performed and the performance values of the networks were tested. The reason for using two different networks in this study is to compare the performance values of these networks at the time of attack. According to the test results, both networks were adversely affected by the attacks. It was observed that network performance decreased in MANET structure but there was no network interruption. The SDN controller becomes dysfunctional and collapses as a result of the attack. While the innovations offered by the SDN structure are expected to provide solutions to many problems in traditional networks, there are still many vulnerabilities for network security.

2020-07-24
Obert, James, Chavez, Adrian.  2019.  Graph-Based Event Classification in Grid Security Gateways. 2019 Second International Conference on Artificial Intelligence for Industries (AI4I). :63—66.
In recent years the use of security gateways (SG) located within the electrical grid distribution network has become pervasive. SGs in substations and renewable distributed energy resource aggregators (DERAs) protect power distribution control devices from cyber and cyber-physical attacks. When encrypted communications within a DER network is used, TCP/IP packet inspection is restricted to packet header behavioral analysis which in most cases only allows the SG to perform anomaly detection of blocks of time-series data (event windows). Packet header anomaly detection calculates the probability of the presence of a threat within an event window, but fails in such cases where the unreadable encrypted payload contains the attack content. The SG system log (syslog) is a time-series record of behavioral patterns of network users and processes accessing and transferring data through the SG network interfaces. Threatening behavioral pattern in the syslog are measurable using both anomaly detection and graph theory. In this paper it will be shown that it is possible to efficiently detect the presence of and classify a potential threat within an SG syslog using light-weight anomaly detection and graph theory.
2020-07-16
Xiao, Jiaping, Jiang, Jianchun.  2018.  Real-time Security Evaluation for Unmanned Aircraft Systems under Data-driven Attacks*. 2018 13th World Congress on Intelligent Control and Automation (WCICA). :842—847.

With rapid advances in the fields of the Internet of Things and autonomous systems, the network security of cyber-physical systems(CPS) becomes more and more important. This paper focuses on the real-time security evaluation for unmanned aircraft systems which are cyber-physical systems relying on information communication and control system to achieve autonomous decision making. Our problem formulation is motivated by scenarios involving autonomous unmanned aerial vehicles(UAVs) working continuously under data-driven attacks when in an open, uncertain, and even hostile environment. Firstly, we investigated the state estimation method in CPS integrated with data-driven attacks model, and then proposed a real-time security scoring algorithm to evaluate the security condition of unmanned aircraft systems under different threat patterns, considering the vulnerability of the systems and consequences brought by data attacks. Our simulation in a UAV illustrated the efficiency and reliability of the algorithm.

2020-07-03
Jia, Guanbo, Miller, Paul, Hong, Xin, Kalutarage, Harsha, Ban, Tao.  2019.  Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a Sparse Autoencoder. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :458—465.

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and sparse Autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series of dynamic bipartite graph increments. One mode projection is performed on each bipartite graph increment and the adjacency matrix derived. Columns of the resultant adjacency matrix are then used to train a sparse autoencoder to reconstruct it. The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated. An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution. Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks. In the experiment, a network emulator was used to generate representative ecommerce traffic flows over a time period of 225 minutes with five attacks injected, including SYN scans, host emulation and DDoS attacks. ROC curves were generated to investigate the influence of the autoencoder hyper-parameters. It was found that increasing the number of hidden nodes and their activation level, and increasing sparseness resulted in improved performance. Analysis showed that the sparse autoencoder was unable to encode the highly structured adjacency matrix structures associated with attacks, hence they were detected as anomalies. In contrast, SVD and variants, such as the compact matrix decomposition, were found to accurately encode the attack matrices, hence they went undetected.

2020-06-29
Luo, Wenliang, Han, Wenzhi.  2019.  DDOS Defense Strategy in Software Definition Networks. 2019 International Conference on Computer Network, Electronic and Automation (ICCNEA). :186–190.
With the advent of the network economy and the network society, the network will enter a ubiquitous and omnipresent situation. Economic, cultural, military and social life will strongly depend on the network, while network security issues have become a common concern of all countries in the world. DDOS attack is undoubtedly one of the greatest threats to network security and the defense against DDOS attack is very important. In this paper, the principle of DDOS attack is summarized from the defensive purpose. Then the attack prevention in software definition network is analyzed, and the source, intermediate network, victim and distributed defense strategies are elaborated.
Rahman, Md. Mahmudur, Roy, Shanto, Yousuf, Mohammad Abu.  2019.  DDoS Mitigation and Intrusion Prevention in Content Delivery Networks using Distributed Virtual Honeypots. 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). :1–6.

Content Delivery Networks(CDN) is a standout amongst the most encouraging innovations that upgrade performance for its clients' websites by diverting web demands from browsers to topographically dispersed CDN surrogate nodes. However, due to the variable nature of CDN, it suffers from various security and resource allocation issues. The most common attack which is used to bring down a whole network as well as CDN without even finding a loophole in the security is DDoS. In this proposal, we proposed a distributed virtual honeypot model for diminishing DDoS attacks and prevent intrusion in securing CDN. Honeypots are specially utilized to imitate the primary server with the goal that the attack is alleviated to the fake rather than the main server. Our proposed layer based model utilizes honeypot to be more effective reducing the cost of the system as well as maintaining the smooth delivery in geographically dispersed servers without performance degradation.

2020-06-26
Aung, Tun Myat, Hla, Ni Ni.  2019.  A complex number approach to elliptic curve cryptosystems over finite fields: implementations and experiments. 2019 International Conference on Computer Communication and Informatics (ICCCI). :1—8.

Network security is a general idea to ensure information transmission over PC and portable systems. Elliptic curve cryptosystems are nowadays widely used in public communication channels for network security. Their security relies upon the complexity of clarifying the elliptic curve discrete alogarithm issue. But, there are several general attacks in them. Elliptic bend number juggling is actualized over complex fields to enhance the security of elliptic curve cryptosystems. This paper starts with the qualities of elliptic curve cryptosystems and their security administrations. At that point we talk about limited field number-crunching and its properties, prime field number-crunching, twofold field math and complex number-crunching, and elliptic bend number-crunching over prime field and parallel field. This paper proposes how to execute the unpredictable number of math under prime field and double field utilizing java BigInteger class. also, we actualize elliptic bend math and elliptic bend cryptosystems utilizing complex numbers over prime field and double field and talk about our trials that got from the usage.

2020-06-15
Chen, JiaYou, Guo, Hong, Hu, Wei.  2019.  Research on Improving Network Security of Embedded System. 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :136–138.
With the continuous development of information technology, our country has achieved great progress and development in Electronic Science and technology. Nowadays mobile embedded systems are gradually coming into people's vision. Mobile embedded system is a brand-new computer technology in the current computer technology. Now it has been widely used in enterprises. Mobile embedded system extends its functions mainly by combining the access capability of the Internet. Nowadays, embedded system network is widely welcomed by people. But for the embedded system network, there are also a variety of network attacks. Therefore, in the research process of this paper, we mainly start with the way of embedded network security and network attack, and then carry out the countermeasures to improve the network security of embedded system, which is to provide a good reference for improving the security and stability of embedded system.
2020-06-12
Chiba, Zouhair, Abghour, Noreddine, Moussaid, Khalid, Omri, Amina El, Rida, Mohamed.  2018.  A Hybrid Optimization Framework Based on Genetic Algorithm and Simulated Annealing Algorithm to Enhance Performance of Anomaly Network Intrusion Detection System Based on BP Neural Network. 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT). :1—6.

Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an organization plays a vital part in attack mitigation, and it has become an integral part of a secure organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.

2020-06-08
Huang, Jiamin, Lu, Yueming, Guo, Kun.  2019.  A Hybrid Packet Classification Algorithm Based on Hash Table and Geometric Space Partition. 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC). :587–592.
The emergence of integrated space-ground network (ISGN), with more complex network conditions compared with tradition network, requires packet classification to achieve high performance. Packet classification plays an important role in the field of network security. Although several existing classification schemes have been proposed recently to improve classification performance, the performance of these schemes is unable to meet the high-speed packet classification requirement in ISGN. To tackle this problem, a hybrid packet classification algorithm based on hash table and geometric space partition (HGSP) is proposed in this paper. HGSP falls into two sections: geometric space partition and hash matching. To improve the classification speed under the same accuracy, a parallel structure of hash table is designed to match the huge packets for classifying. The experimental results demonstrate that the matching time of HGSP algorithm is reduced by 40%-70% compared with traditional Hicuts algorithm. Particularly, with the growth of ruleset, the advantage of HGSP algorithm will become more obvious.
2020-06-01
Pomak, Wiphop, Limpiyakom, Yachai.  2018.  Enterprise WiFi Hotspot Authentication with Hybrid Encryption on NFC- Enabled Smartphones. 2018 8th International Conference on Electronics Information and Emergency Communication (ICEIEC). :247–250.
Nowadays, some workplaces have adopted the policy of BYOD (bring your own device) that permits employees to bring personally owned devices, and to use those devices to access company information and applications. Especially, small devices like smartphones are widely used due to the greater mobility and connectivity. A majority of organizations provide the wireless local area network which is necessary for small devices and business data transmission. The resources access through Wi-Fi network of the organization needs intense restriction. WPA2 Enterprise with 802.1X standard is typically introduced to handle user authentication on the network using the EAP framework. However, credentials management for all users is a hassle for administrators. Strong authentication provides higher security whereas the difficulty of deployment is still open issues. This research proposes the utility of Near Field Communication to securely transmit certificate data that rely on the hybrid cryptosystem. The approach supports enterprise Wi-Fi hotspot authentication based on WPA2-802.1X model with the EAP-TLS method. It also applies multi-factor authentication for enhancing the security of networks and users. The security analysis and experiment on establishing connection time were conducted to evaluate the presented approach.
Ye, Yu, Guo, Jun, Xu, Xunjian, Li, Qinpu, Liu, Hong, Di, Yuelun.  2019.  High-risk Problem of Penetration Testing of Power Grid Rainstorm Disaster Artificial Intelligence Prediction System and Its Countermeasures. 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2). :2675–2680.
System penetration testing is an important measure of discovering information system security issues. This paper summarizes and analyzes the high-risk problems found in the penetration testing of the artificial storm prediction system for power grid storm disasters from four aspects: application security, middleware security, host security and network security. In particular, in order to overcome the blindness of PGRDAIPS current SQL injection penetration test, this paper proposes a SQL blind bug based on improved second-order fragmentation reorganization. By modeling the SQL injection attack behavior and comparing the SQL injection vulnerability test in PGRDAIPS, this method can effectively reduce the blindness of SQL injection penetration test and improve its accuracy. With the prevalence of ubiquitous power internet of things, the electric power information system security defense work has to be taken seriously. This paper can not only guide the design, development and maintenance of disaster prediction information systems, but also provide security for the Energy Internet disaster safety and power meteorological service technology support.
Vishwakarma, Ruchi, Jain, Ankit Kumar.  2019.  A Honeypot with Machine Learning based Detection Framework for defending IoT based Botnet DDoS Attacks. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). :1019–1024.

With the tremendous growth of IoT botnet DDoS attacks in recent years, IoT security has now become one of the most concerned topics in the field of network security. A lot of security approaches have been proposed in the area, but they still lack in terms of dealing with newer emerging variants of IoT malware, known as Zero-Day Attacks. In this paper, we present a honeypot-based approach which uses machine learning techniques for malware detection. The IoT honeypot generated data is used as a dataset for the effective and dynamic training of a machine learning model. The approach can be taken as a productive outset towards combatting Zero-Day DDoS Attacks which now has emerged as an open challenge in defending IoT against DDoS Attacks.

2020-05-29
HOU, RUI, Han, Min, Chen, Jing, Hu, Wenbin, Tan, Xiaobin, Luo, Jiangtao, Ma, Maode.  2019.  Theil-Based Countermeasure against Interest Flooding Attacks for Named Data Networks. IEEE Network. 33:116—121.

NDN has been widely regarded as a promising representation and implementation of information- centric networking (ICN) and serves as a potential candidate for the future Internet architecture. However, the security of NDN is threatened by a significant safety hazard known as an IFA, which is an evolution of DoS and distributed DoS attacks on IP-based networks. The IFA attackers can create numerous malicious interest packets into a named data network to quickly exhaust the bandwidth of communication channels and cache capacity of NDN routers, thereby seriously affecting the routers' ability to receive and forward packets for normal users. Accurate detection of the IFAs is the most critical issue in the design of a countermeasure. To the best of our knowledge, the existing IFA countermeasures still have limitations in terms of detection accuracy, especially for rapidly volatile attacks. This article proposes a TC to detect the distributions of normal and malicious interest packets in the NDN routers to further identify the IFA. The trace back method is used to prevent further attempts. The simulation results show the efficiency of the TC for mitigating the IFAs and its advantages over other typical IFA countermeasures.

2020-05-26
Fu, Yulong, Li, Guoquan, Mohammed, Atiquzzaman, Yan, Zheng, Cao, Jin, Li, Hui.  2019.  A Study and Enhancement to the Security of MANET AODV Protocol Against Black Hole Attacks. 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :1431–1436.
Mobile AdHoc Networks (MANET) can be fast implemented, and it is very popular in many specific network requirements, such as UAV (Unmanned Aerial Unit), Disaster Recovery and IoT (Internet of Things) etc. However, MANET is also vulnerable. AODV (Ad hoc On-Demand Distance Vector Routing) protocol is one type of MANET routing protocol and many attacks can be implemented to break the connections on AODV based AdHoc networks. In this article, aim of protecting the MANET security, we modeled the AODV protocol with one type of Automata and analyzed the security vulnerabilities of it; then based on the analyzing results, we proposed an enhancement to AODV protocol to against the Black Hole Attacks. We also implemented the proposed enhancement in NS3 simulator and verified the correctness, usability and efficiency.
Junnarkar, Aparna A., Singh, Y. P., Deshpande, Vivek S..  2018.  SQMAA: Security, QoS and Mobility Aware ACO Based Opportunistic Routing Protocol for MANET. 2018 4th International Conference for Convergence in Technology (I2CT). :1–6.
The QoS performance of MANET routing protocols is significantly affected by the mobility conditions in network. Secondly, as MANET open nature network, there is strong possibility of different types of vulnerabilities such as blackhole attack, malicious attack, DoS attacks etc. In this research work, we are designing the novel opportunistic routing protocol in order to address the challenges of network security as well as QoS improvement. There two algorithms designed in this paper. First we proposed and designed novel QoS improvement algorithm based on optimization scheme called Ant Colony Optimization (ACO) with swarm intelligence approach. This proposed method used the RSSI measurements to determine the distance between two mobile nodes in order to select efficient path for communication. This new routing protocol is named as QoS Mobility Aware ACO (QMAA) Routing Protocol. Second, we designed security algorithm for secure communication and user's authentication in MANET under the presence attackers in network. With security algorithm the QoS aware protocol is proposed named as Secure-QMAA (SQMAA). The SQMAA achieved secure communications while guaranteed QoS performance against existing routing protocols. The simulation results shows that under the presence of malicious attackers, the performance of SQMAA are efficient as compared to QMAA and state-of-art routing protocol.