Visible to the public Biblio

Found 15133 results

2022-09-20
Dong, Xingbo, Jin, Zhe, Zhao, Leshan, Guo, Zhenhua.  2021.  BioCanCrypto: An LDPC Coded Bio-Cryptosystem on Fingerprint Cancellable Template. 2021 IEEE International Joint Conference on Biometrics (IJCB). :1—8.
Biometrics as a means of personal authentication has demonstrated strong viability in the past decade. However, directly deriving a unique cryptographic key from biometric data is a non-trivial task due to the fact that biometric data is usually noisy and presents large intra-class variations. Moreover, biometric data is permanently associated with the user, which leads to security and privacy issues. Cancellable biometrics and bio-cryptosystem are two main branches to address those issues, yet both approaches fall short in terms of accuracy performance, security, and privacy. In this paper, we propose a Bio-Crypto system on fingerprint Cancellable template (Bio-CanCrypto), which bridges cancellable biometrics and bio-cryptosystem to achieve a middle-ground for alleviating the limitations of both. Specifically, a cancellable transformation is applied on a fixed-length fingerprint feature vector to generate cancellable templates. Next, an LDPC coding mechanism is introduced into a reusable fuzzy extractor scheme and used to extract the stable cryptographic key from the generated cancellable templates. The proposed system can achieve both cancellability and reusability in one scheme. Experiments are conducted on a public fingerprint dataset, i.e., FVC2002. The results demonstrate that the proposed LDPC coded reusable fuzzy extractor is effective and promising.
Bentahar, Atef, Meraoumia, Abdallah, Bendjenna, Hakim, Chitroub, Salim, Zeroual, Abdelhakim.  2021.  Eigen-Fingerprints-Based Remote Authentication Cryptosystem. 2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI). :1—6.
Nowadays, biometric is a most technique to authenticate /identify human been, because its resistance against theft, loss or forgetfulness. However, biometric is subject to different transmission attacks. Today, the protection of the sensitive biometric information is a big challenge, especially in current wireless networks such as internet of things where the transmitted data is easy to sniffer. For that, this paper proposes an Eigens-Fingerprint-based biometric cryptosystem, where the biometric feature vectors are extracted by the Principal Component Analysis technique with an appropriate quantification. The key-binding principle incorporated with bit-wise and byte-wise correcting code is used for encrypting data and sharing key. Several recognition rates and computation time are used to evaluate the proposed system. The findings show that the proposed cryptosystem achieves a high security without decreasing the accuracy.
Cooley, Rafer, Cutshaw, Michael, Wolf, Shaya, Foster, Rita, Haile, Jed, Borowczak, Mike.  2021.  Comparing Ransomware using TLSH and @DisCo Analysis Frameworks. 2021 IEEE International Conference on Big Data (Big Data). :2084—2091.
Modern malware indicators utilized by the current top threat feeds are easily bypassed and generated through enigmatic methods, leading to a lack of detection capabilities for cyber defenders. Static hash-based algorithms such as MD5 or SHA generate indicators that are rendered obsolete by modifying a single byte of the source file. Conversely, fuzzy hash-based algorithms such as SSDEEP and TLSH are more robust to alterations of source information; however, these methods often utilize context boundaries that are hard to define or not based on meaningful information. In previous work, a custom binary analysis tool was created called @DisCo. In this study, four current ransomware campaigns were analyzed using TLSH fuzzy hashing and the @DisCo tool. While TLSH works on the binary level of the entire program, @DisCo works at an intermediate function level. The results from each analysis method were compared to provide validation between the two as well as introduce a narrative for using combinations of these types of methods for the creation of stronger indicators of compromise.
Thao Nguyen, Thi Ai, Dang, Tran Khanh, Nguyen, Dinh Thanh.  2021.  Non-Invertibility for Random Projection based Biometric Template Protection Scheme. 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM). :1—8.
Nowadays, biometric-based authentication systems are widely used. This fact has led to increased attacks on biometric data of users. Therefore, biometric template protection is sure to keep the attention of researchers for the security of the authentication systems. Many previous works proposed the biometric template protection schemes by transforming the original biometric data into a secure domain, or establishing a cryptographic key with the use of biometric data. The main purpose was that fulfill the all three requirements: cancelability, security, and performance as many as possible. In this paper, using random projection merged with fuzzy commitment, we will introduce a hybrid scheme of biometric template protection. We try to limit their own drawbacks and take full advantages of these techniques at the same time. In addition, an analysis of non-invertibility property will be exercised with regards to the use of random projection aiming at enhancing the security of the system while preserving the discriminability of the original biometric template.
Sreemol, R, Santosh Kumar, M B, Sreekumar, A.  2021.  Improvement of Security in Multi-Biometric Cryptosystem by Modulus Fuzzy Vault Algorithm. 2021 International Conference on Advances in Computing and Communications (ICACC). :1—7.
Numerous prevalent techniques build a Multi-Modal Biometric (MMB) system that struggles in offering security and also revocability onto the templates. This work proffered a MMB system centred on the Modulus Fuzzy Vault (MFV) aimed at resolving these issues. The methodology proposed includes Fingerprint (FP), Palmprint (PP), Ear and also Retina images. Utilizing the Boosted Double Plateau Histogram Equalization (BDPHE) technique, all images are improved. Aimed at removing the unnecessary things as of the ear and the blood vessels are segmented as of the retina images utilizing the Modified Balanced Iterative Reducing and Clustering using Hierarchy (MBIRCH) technique. Next, the input traits features are extracted; then the essential features are chosen as of the features extracted utilizing the Bidirectional Deer Hunting optimization Algorithm (BDHOA). The features chosen are merged utilizing the Normalized Feature Level and Score Level (NFLSL) fusion. The features fused are saved securely utilizing Modulus Fuzzy Vault. Upto fusion, the procedure is repeated aimed at the query image template. Next, the de-Fuzzy Vault procedure is executed aimed at the query template, and then the key is detached by matching the query template’s and input biometric template features. The key separated is analogized with the threshold that categorizes the user as genuine or else imposter. The proposed BDPHE and also MFV techniques function efficiently than the existent techniques.
Korenda, Ashwija Reddy, Afghah, Fatemeh, Razi, Abolfazl, Cambou, Bertrand, Begay, Taylor.  2021.  Fuzzy Key Generator Design using ReRAM-Based Physically Unclonable Functions. 2021 IEEE Physical Assurance and Inspection of Electronics (PAINE). :1—7.
Physical unclonable functions (PUFs) are used to create unique device identifiers from their inherent fabrication variability. Unstable readings and variation of the PUF response over time are key issues that limit the applicability of PUFs in real-world systems. In this project, we developed a fuzzy extractor (FE) to generate robust cryptographic keys from ReRAM-based PUFs. We tested the efficiency of the proposed FE using BCH and Polar error correction codes. We use ReRAM-based PUFs operating in pre-forming range to generate binary cryptographic keys at ultra-low power with an objective of tamper sensitivity. We investigate the performance of the proposed FE with real data using the reading of the resistance of pre-formed ReRAM cells under various noise conditions. The results show a bit error rate (BER) in the range of 10−5 for the Polar-codes based method when 10% of the ReRAM cell array is erroneous at Signal to Noise Ratio (SNR) of 20dB.This error rate is achieved by using helper data length of 512 bits for a 256 bit cryptographic key. Our method uses a 2:1 ratio for helper data and key, much lower than the majority of previously reported methods. This property makes our method more robust against helper data attacks.
Simjanović, Dušan J., Milošević, Dušan M., Milošević, Mimica R..  2021.  Fuzzy AHP based Ranking of Cryptography Indicators. 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (℡SIKS). :237—240.
The progression of cryptographic attacks in the ICT era doubtless leads to the development of new cryptographic algorithms and assessment, and evaluation of the existing ones. In this paper, the artificial intelligence application, through the fuzzy analytic hierarchy process (FAHP) implementation, is used to rank criteria and sub-criteria on which the algorithms are based to determine the most promising criteria and optimize their use. Out of fifteen criteria, security soundness, robustness and hardware failure distinguished as significant ones.
Shaomei, Lv, Xiangyan, Zeng, Long, Huang, Lan, Wu, Wei, Jiang.  2021.  Passenger Volume Interval Prediction based on MTIGM (1,1) and BP Neural Network. 2021 33rd Chinese Control and Decision Conference (CCDC). :6013—6018.
The ternary interval number contains more comprehensive information than the exact number, and the prediction of the ternary interval number is more conducive to intelligent decision-making. In order to reduce the overfitting problem of the neural network model, a combination prediction method of the BP neural network and the matrix GM (1, 1) model for the ternary interval number sequence is proposed in the paper, and based on the proposed method to predict the passenger volume. The matrix grey model for the ternary interval number sequence (MTIGM (1, 1)) can stably predict the overall development trend of a time series. Considering the integrity of interval numbers, the BP neural network model is established by combining the lower, middle and upper boundary points of the ternary interval numbers. The combined weights of MTIGM (1, 1) and the BP neural network are determined based on the grey relational degree. The combined method is used to predict the total passenger volume and railway passenger volume of China, and the prediction effect is better than MTIGM (1, 1) and BP neural network.
Wang, Xuelei, Fidge, Colin, Nourbakhsh, Ghavameddin, Foo, Ernest, Jadidi, Zahra, Li, Calvin.  2021.  Feature Selection for Precise Anomaly Detection in Substation Automation Systems. 2021 13th IEEE PES Asia Pacific Power & Energy Engineering Conference (APPEEC). :1—6.
With the rapid advancement of the electrical grid, substation automation systems (SASs) have been developing continuously. However, with the introduction of advanced features, such as remote control, potential cyber security threats in SASs are also increased. Additionally, crucial components in SASs, such as protection relays, usually come from third-party vendors and may not be fully trusted. Untrusted devices may stealthily perform harmful or unauthorised behaviours which could compromise or damage SASs, and therefore, bring adverse impacts to the primary plant. Thus, it is necessary to detect abnormal behaviours from an untrusted device before it brings about catastrophic impacts. Anomaly detection techniques are suitable to detect anomalies in SASs as they only bring minimal side-effects to normal system operations. Many researchers have developed various machine learning algorithms and mathematical models to improve the accuracy of anomaly detection. However, without prudent feature selection, it is difficult to achieve high accuracy when detecting attacks launched from internal trusted networks, especially for stealthy message modification attacks which only modify message payloads slightly and imitate patterns of benign behaviours. Therefore, this paper presents choices of features which improve the accuracy of anomaly detection within SASs, especially for detecting “stealthy” attacks. By including two additional features, Boolean control data from message payloads and physical values from sensors, our method improved the accuracy of anomaly detection by decreasing the false-negative rate from 25% to 5% approximately.
Samy, Salma, Banawan, Karim, Azab, Mohamed, Rizk, Mohamed.  2021.  Smart Blockchain-based Control-data Protection Framework for Trustworthy Smart Grid Operations. 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). :0963—0969.
The critical nature of smart grids (SGs) attracts various network attacks and malicious manipulations. Existent SG solutions are less capable of ensuring secure and trustworthy operation. This is due to the large-scale nature of SGs and reliance on network protocols for trust management. A particular example of such severe attacks is the false data injection (FDI). FDI refers to a network attack, where meters' measurements are manipulated before being reported in such a way that the energy system takes flawed decisions. In this paper, we exploit the secure nature of blockchains to construct a data management framework based on public blockchain. Our framework enables trustworthy data storage, verification, and exchange between SG components and decision-makers. Our proposed system enables miners to invest their computational power to verify blockchain transactions in a fully distributed manner. The mining logic employs machine learning (ML) techniques to identify the locations of compromised meters in the network, which are responsible for generating FDI attacks. In return, miners receive virtual credit, which may be used to pay their electric bills. Our design circumvents single points of failure and intentional FDI attempts. Our numerical results compare the accuracy of three different ML-based mining logic techniques in two scenarios: focused and distributed FDI attacks for different attack levels. Finally, we proposed a majority-decision mining technique for the practical case of an unknown FDI attack level.
Rajput, Prashant Hari Narayan, Sarkar, Esha, Tychalas, Dimitrios, Maniatakos, Michail.  2021.  Remote Non-Intrusive Malware Detection for PLCs based on Chain of Trust Rooted in Hardware. 2021 IEEE European Symposium on Security and Privacy (EuroS&P). :369—384.
Digitization has been rapidly integrated with manufacturing industries and critical infrastructure to increase efficiency, productivity, and reduce wastefulness, a transition being labeled as Industry 4.0. However, this expansion, coupled with the poor cybersecurity posture of these Industrial Internet of Things (IIoT) devices, has made them prolific targets for exploitation. Moreover, modern Programmable Logic Controllers (PLC) used in the Operational Technology (OT) sector are adopting open-source operating systems such as Linux instead of proprietary software, making such devices susceptible to Linux-based malware. Traditional malware detection approaches cannot be applied directly or extended to such environments due to the unique restrictions of these PLC devices, such as limited computational power and real-time requirements. In this paper, we propose ORRIS, a novel lightweight and out-of-the-device framework that detects malware at both kernel and user-level by processing the information collected using the Joint Test Action Group (JTAG) interface. We evaluate ORRIS against in-the-wild Linux malware achieving maximum detection accuracy of ≈99.7% with very few false-positive occurrences, a result comparable to the state-of-the-art commercial products. Moreover, we also develop and demonstrate a real-time implementation of ORRIS for commercial PLCs.
Afzal-Houshmand, Sam, Homayoun, Sajad, Giannetsos, Thanassis.  2021.  A Perfect Match: Deep Learning Towards Enhanced Data Trustworthiness in Crowd-Sensing Systems. 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). :258—264.
The advent of IoT edge devices has enabled the collection of rich datasets, as part of Mobile Crowd Sensing (MCS), which has emerged as a key enabler for a wide gamut of safety-critical applications ranging from traffic control, environmental monitoring to assistive healthcare. Despite the clear advantages that such unprecedented quantity of data brings forth, it is also subject to inherent data trustworthiness challenges due to factors such as malevolent input and faulty sensors. Compounding this issue, there has been a plethora of proposed solutions, based on the use of traditional machine learning algorithms, towards assessing and sifting faulty data without any assumption on the trustworthiness of their source. However, there are still a number of open issues: how to cope with the presence of strong, colluding adversaries while at the same time efficiently managing this high influx of incoming user data. In this work, we meet these challenges by proposing the hybrid use of Deep Learning schemes (i.e., LSTMs) and conventional Machine Learning classifiers (i.e. One-Class Classifiers) for detecting and filtering out false data points. We provide a prototype implementation coupled with a detailed performance evaluation under various (attack) scenarios, employing both real and synthetic datasets. Our results showcase how the proposed solution outperforms various existing resilient aggregation and outlier detection schemes.
Yan, Weili, Lou, Xin, Yau, David K.Y., Yang, Ying, Saifuddin, Muhammad Ramadan, Wu, Jiyan, Winslett, Marianne.  2021.  A Stealthier False Data Injection Attack against the Power Grid. 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :108—114.
We use discrete-time adaptive control theory to design a novel false data injection (FDI) attack against automatic generation control (AGC), a critical system that maintains a power grid at its requisite frequency. FDI attacks can cause equipment damage or blackouts by falsifying measurements in the streaming sensor data used to monitor the grid's operation. Compared to prior work, the proposed attack (i) requires less knowledge on the part of the attacker, such as correctly forecasting the future demand for power; (ii) is stealthier in its ability to bypass standard methods for detecting bad sensor data and to keep the false sensor readings near historical norms until the attack is well underway; and (iii) can sustain the frequency excursion as long as needed to cause real-world damage, in spite of AGC countermeasures. We validate the performance of the proposed attack on realistic 37-bus and 118-bus setups in PowerWorld, an industry-strength power system simulator trusted by real-world operators. The results demonstrate the attack's improved stealthiness and effectiveness compared to prior work.
Cabelin, Joe Diether, Alpano, Paul Vincent, Pedrasa, Jhoanna Rhodette.  2021.  SVM-based Detection of False Data Injection in Intelligent Transportation System. 2021 International Conference on Information Networking (ICOIN). :279—284.
Vehicular Ad-Hoc Network (VANET) is a subcategory of Intelligent Transportation Systems (ITS) that allows vehicles to communicate with other vehicles and static roadside infrastructure. However, the integration of cyber and physical systems introduce many possible points of attack that make VANET vulnerable to cyber attacks. In this paper, we implemented a machine learning-based intrusion detection system that identifies False Data Injection (FDI) attacks on a vehicular network. A co-simulation framework between MATLAB and NS-3 is used to simulate the system. The intrusion detection system is installed in every vehicle and processes the information obtained from the packets sent by other vehicles. The packet is classified into either trusted or malicious using Support Vector Machines (SVM). The comparison of the performance of the system is evaluated in different scenarios using the following metrics: classification rate, attack detection rate, false positive rate, and detection speed. Simulation results show that the SVM-based IDS is able to provide high accuracy detection, low false positive rate, consequently improving the traffic congestion in the simulated highway.
Chang, Fuhong, Li, Qi, Wang, Yuanyuan, Zhang, Wenfeng.  2021.  Dynamic Detection Model of False Data Injection Attack Facing Power Network Security. 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). :317—321.
In order to protect the safety of power grid, improve the early warning precision of false data injection. This paper presents a dynamic detection model for false data injection attacks. Based on the characteristics of APT attacks, a model of attack characteristics for trusted regions is constructed. In order to realize the accurate state estimation, unscented Kalman filtering algorithm is used to estimate the state of nonlinear power system and realize dynamic attack detection. Experimental results show that the precision of this method is higher than 90%, which verifies the effectiveness of this paper in attack detection.
Wood, Adrian, Johnstone, Michael N..  2021.  Detection of Induced False Negatives in Malware Samples. 2021 18th International Conference on Privacy, Security and Trust (PST). :1—6.
Malware detection is an important area of cyber security. Computer systems rely on malware detection applications to prevent malware attacks from succeeding. Malware detection is not a straightforward task, as new variants of malware are generated at an increasing rate. Machine learning (ML) has been utilised to generate predictive classification models to identify new malware variants which conventional malware detection methods may not detect. Machine learning, has however, been found to be vulnerable to different types of adversarial attacks, in which an attacker is able to negatively affect the classification ability of the ML model. Several defensive measures to prevent adversarial poisoning attacks have been developed, but they often rely on the use of a trusted clean dataset to help identify and remove adversarial examples from the training dataset. The defence in this paper does not require a trusted clean dataset, but instead, identifies intentional false negatives (zero day malware classified as benign) at the testing stage by examining the activation weights of the ML model. The defence was able to identify 94.07% of the successful targeted poisoning attacks.
Chen, Lei, Yuan, Yuyu, Jiang, Hongpu, Guo, Ting, Zhao, Pengqian, Shi, Jinsheng.  2021.  A Novel Trust-based Model for Collaborative Filtering Recommendation Systems using Entropy. 2021 8th International Conference on Dependable Systems and Their Applications (DSA). :184—188.
With the proliferation of false redundant information on various e-commerce platforms, ineffective recommendations and other untrustworthy behaviors have seriously hindered the healthy development of e-commerce platforms. Modern recommendation systems often use side information to alleviate these problems and also increase prediction accuracy. One such piece of side information, which has been widely investigated, is trust. However, it is difficult to obtain explicit trust relationship data, so researchers infer trust values from other methods, such as the user-to-item relationship. In this paper, addressing the problems, we proposed a novel trust-based recommender model called UITrust, which uses user-item relationship value to improve prediction accuracy. With the improvement the traditional similarity measures by employing the entropies of user and item history ratings to reflect the global rating behavior on both. We evaluate the proposed model using two real-world datasets. The proposed model performs significantly better than the baseline methods. Also, we can use the UITrust to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.
Emadi, Hamid, Clanin, Joe, Hyder, Burhan, Khanna, Kush, Govindarasu, Manimaran, Bhattacharya, Sourabh.  2021.  An Efficient Computational Strategy for Cyber-Physical Contingency Analysis in Smart Grids. 2021 IEEE Power & Energy Society General Meeting (PESGM). :1—5.
The increasing penetration of cyber systems into smart grids has resulted in these grids being more vulnerable to cyber physical attacks. The central challenge of higher order cyber-physical contingency analysis is the exponential blow-up of the attack surface due to a large number of attack vectors. This gives rise to computational challenges in devising efficient attack mitigation strategies. However, a system operator can leverage private information about the underlying network to maintain a strategic advantage over an adversary equipped with superior computational capability and situational awareness. In this work, we examine the following scenario: A malicious entity intrudes the cyber-layer of a power network and trips the transmission lines. The objective of the system operator is to deploy security measures in the cyber-layer to minimize the impact of such attacks. Due to budget constraints, the attacker and the system operator have limits on the maximum number of transmission lines they can attack or defend. We model this adversarial interaction as a resource-constrained attacker-defender game. The computational intractability of solving large security games is well known. However, we exploit the approximately modular behaviour of an impact metric known as the disturbance value to arrive at a linear-time algorithm for computing an optimal defense strategy. We validate the efficacy of the proposed strategy against attackers of various capabilities and provide an algorithm for a real-time implementation.
Yao, Pengchao, Hao, Weijie, Yan, Bingjing, Yang, Tao, Wang, Jinming, Yang, Qiang.  2021.  Game-Theoretic Model for Optimal Cyber-Attack Defensive Decision-Making in Cyber-Physical Power Systems. 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2). :2359—2364.
Cyber-Physical Power Systems (CPPSs) currently face an increasing number of security attacks and lack methods for optimal proactive security decisions to defend the attacks. This paper proposed an optimal defensive method based on game theory to minimize the system performance deterioration of CPPSs under cyberspace attacks. The reinforcement learning algorithmic solution is used to obtain the Nash equilibrium and a set of metrics of system vulnerabilities are adopted to quantify the cost of defense against cyber-attacks. The minimax-Q algorithm is utilized to obtain the optimal defense strategy without the availability of the attacker's information. The proposed solution is assessed through experiments based on a realistic power generation microsystem testbed and the numerical results confirmed its effectiveness.
Pereira, Luiz Manella, Iyengar, S. S., Amini, M. Hadi.  2021.  On the Impact of the Embedding Process on Network Resilience Quantification. 2021 International Conference on Computational Science and Computational Intelligence (CSCI). :836—839.
Network resilience is crucial to ensure reliable and secure operation of critical infrastructures. Although graph theoretic methods have been developed to quantify the topological resilience of networks, i.e., measuring resilience with respect to connectivity, in this study we propose to use the tools from Topological Data Analysis (TDA), Algebraic Topology, and Optimal Transport (OT). In our prior work, we used these tools to create a resilience metric that bypassed the need to embed a network onto a space. We also hypothesized that embeddings could encode different information about a network and that different embeddings could result in different outcomes when computing resilience. In this paper we attempt to test this hypothesis. We will utilize the WEGL framework to compute the embedding for the considered network and compare the results against our prior work, which did not use an embedding process. To our knowledge, this is the first attempt to study the ramifications of choosing an embedding, thus providing a novel understanding into how to choose an embedding and whether such a choice matters when quantifying resilience.
Ndemeye, Bosco, Hussain, Shahid, Norris, Boyana.  2021.  Threshold-Based Analysis of the Code Quality of High-Performance Computing Software Packages. 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C). :222—228.
Many popular metrics used for the quantification of the quality or complexity of a codebase (e.g. cyclomatic complexity) were developed in the 1970s or 1980s when source code sizes were significantly smaller than they are today, and before a number of modern programming language features were introduced in different languages. Thus, the many thresholds that were suggested by researchers for deciding whether a given function is lacking in a given quality dimension need to be updated. In the pursuit of this goal, we study a number of open-source high-performance codes, each of which has been in development for more than 15 years—a characteristic which we take to imply good design to score them in terms of their source codes' quality and to relax the above-mentioned thresholds. First, we employ the LLVM/Clang compiler infrastructure and introduce a Clang AST tool to gather AST-based metrics, as well as an LLVM IR pass for those based on a source code's static call graph. Second, we perform statistical analysis to identify the reference thresholds of 22 code quality and callgraph-related metrics at a fine grained level.
Singh, Jagdeep, Behal, Sunny.  2021.  A Novel Approach for the Detection of DDoS Attacks in SDN using Information Theory Metric. 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom). :512—516.
Internet always remains the target for the cyberattacks, and attackers are getting equipped with more potent tools due to the advancement of technology to preach the security of the Internet. Industries and organizations are sponsoring many projects to avoid these kinds of problems. As a result, SDN (Software Defined Network) architecture is becoming an acceptable alternative for the traditional IP based networks which seems a better approach to defend the Internet. However, SDN is also vulnerable to many new threats because of its architectural concept. SDN might be a primary target for DoS (Denial of Service) and DDoS (Distributed Denial of Service) attacks due to centralized control and linking of data plane and control plane. In this paper, the we propose a novel technique for detection of DDoS attacks using information theory metric. We compared our approach with widely used Intrusion Detection Systems (IDSs) based on Shannon entropy and Renyi entropy, and proved that our proposed methodology has more power to detect malicious flows in SDN based networks. We have used precision, detection rate and FPR (False Positive Rate) as performance parameters for comparison, and validated the methodology using a topology implemented in Mininet network emulator.
Chandramouli, Athreya, Jana, Sayantan, Kothapalli, Kishore.  2021.  Efficient Parallel Algorithms for Computing Percolation Centrality. 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC). :111—120.
Centrality measures on graphs have found applications in a large number of domains including modeling the spread of an infection/disease, social network analysis, and transportation networks. As a result, parallel algorithms for computing various centrality metrics on graphs are gaining significant research attention in recent years. In this paper, we study parallel algorithms for the percolation centrality measure which extends the betweenness-centrality measure by incorporating a time dependent state variable with every node. We present parallel algorithms that compute the source-based and source-destination variants of the percolation centrality values of nodes in a network. Our algorithms extend the algorithm of Brandes, introduce optimizations aimed at exploiting the structural properties of graphs, and extend the algorithmic techniques introduced by Sariyuce et al. [26] in the context of centrality computation. Experimental studies of our algorithms on an Intel Xeon(R) Silver 4116 CPU and an Nvidia Tesla V100 GPU on a collection of 12 real-world graphs indicate that our algorithmic techniques offer a significant speedup.
Zhao, Lianying, Oshman, Muhammad Shafayat, Zhang, Mengyuan, Moghaddam, Fereydoun Farrahi, Chander, Shubham, Pourzandi, Makan.  2021.  Towards 5G-ready Security Metrics. ICC 2021 - IEEE International Conference on Communications. :1—6.
The fifth-generation (5G) mobile telecom network has been garnering interest in both academia and industry, with better flexibility and higher performance compared to previous generations. Along with functionality improvements, new attack vectors also made way. Network operators and regulatory organizations wish to have a more precise idea about the security posture of 5G environments. Meanwhile, various security metrics for IT environments have been around and attracted the community’s attention. However, 5G-specific factors are less taken into consideration.This paper considers such 5G-specific factors to identify potential gaps if existing security metrics are to be applied to the 5G environments. In light of the layered nature and multi-ownership, the paper proposes a new approach to the modular computation of security metrics based on cross-layer projection as a means of information sharing between layers. Finally, the proposed approach is evaluated through simulation.
Koteshwara, Sandhya.  2021.  Security Risk Assessment of Server Hardware Architectures Using Graph Analysis. 2021 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). :1—4.
The growing complexity of server architectures, which incorporate several components with state, has necessitated rigorous assessment of the security risk both during design and operation. In this paper, we propose a novel technique to model the security risk of servers by mapping their architectures to graphs. This allows us to leverage tools from computational graph theory, which we combine with probability theory for deriving quantitative metrics for risk assessment. Probability of attack is derived for server components, with prior probabilities assigned based on knowledge of existing vulnerabilities and countermeasures. The resulting analysis is further used to compute measures of impact and exploitability of attack. The proposed methods are demonstrated on two open-source server designs with different architectures.