Visible to the public Biblio

Filters: Keyword is signature based defense  [Clear All Filters]
2022-04-19
Giechaskiel, Ilias, Tian, Shanquan, Szefer, Jakub.  2021.  Cross-VM Information Leaks in FPGA-Accelerated Cloud Environments. 2021 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :91–101.
The availability of FPGAs in cloud data centers offers rapid, on-demand access to hardware compute resources that users can configure to their own needs. However, the low-level access to the hardware FPGA and associated resources such as PCIe, SSD, or DRAM also opens up threats of malicious attackers uploading designs that are able to infer information about other users or about the cloud infrastructure itself. In particular, this work presents a new, fast PCIe-contention-based channel that is able to transmit data between different FPGA-accelerated virtual machines with bandwidths reaching 2 kbps with 97% accuracy. This paper further demonstrates that the PCIe receiver circuits are able to not just receive covert transmissions, but can also perform fine-grained monitoring of the PCIe bus or detect different types of activities from other users' FPGA-accelerated virtual machines based on their PCIe traffic signatures. Beyond leaking information across different virtual machines, the ability to monitor the PCIe bandwidth over hours or days can be used to estimate the data center utilization and map the behavior of the other users. The paper also introduces further novel threats in FPGA-accelerated instances, including contention due to shared NVMe SSDs as well as thermal monitoring to identify FPGA co-location using the DRAM modules attached to the FPGA boards. This is the first work to demonstrate that it is possible to break the separation of privilege in FPGA-accelerated cloud environments, and highlights that defenses for public clouds using FPGAs need to consider PCIe, SSD, and DRAM resources as part of the attack surface that should be protected.
Hemmati, Mojtaba, Hadavi, Mohammad Ali.  2021.  Using Deep Reinforcement Learning to Evade Web Application Firewalls. 2021 18th International ISC Conference on Information Security and Cryptology (ISCISC). :35–41.
Web application firewalls (WAF) are the last line of defense in protecting web applications from application layer security threats like SQL injection and cross-site scripting. Currently, most evasion techniques from WAFs are still developed manually. In this work, we propose a solution, which automatically scans the WAFs to find payloads through which the WAFs can be bypassed. Our solution finds out rules defects, which can be further used in rule tuning for rule-based WAFs. Also, it can enrich the machine learning-based dataset for retraining. To this purpose, we provide a framework based on reinforcement learning with an environment compatible with OpenAI gym toolset standards, employed for training agents to implement WAF evasion tasks. The framework acts as an adversary and exploits a set of mutation operators to mutate the malicious payload syntactically without affecting the original semantics. We use Q-learning and proximal policy optimization algorithms with the deep neural network. Our solution is successful in evading signature-based and machine learning-based WAFs.
Wai, Fok Kar, Thing, Vrizlynn L. L..  2021.  Clustering Based Opcode Graph Generation for Malware Variant Detection. 2021 18th International Conference on Privacy, Security and Trust (PST). :1–11.
Malwares are the key means leveraged by threat actors in the cyber space for their attacks. There is a large array of commercial solutions in the market and significant scientific research to tackle the challenge of the detection and defense against malwares. At the same time, attackers also advance their capabilities in creating polymorphic and metamorphic malwares to make it increasingly challenging for existing solutions. To tackle this issue, we propose a methodology to perform malware detection and family attribution. The proposed methodology first performs the extraction of opcodes from malwares in each family and constructs their respective opcode graphs. We explore the use of clustering algorithms on the opcode graphs to detect clusters of malwares within the same malware family. Such clusters can be seen as belonging to different sub-family groups. Opcode graph signatures are built from each detected cluster. Hence, for each malware family, a group of signatures is generated to represent the family. These signatures are used to classify an unknown sample as benign or belonging to one the malware families. We evaluate our methodology by performing experiments on a dataset consisting of both benign files and malware samples belonging to a number of different malware families and comparing the results to existing approach.
Sahu, Indra Kumar, Nene, Manisha J.  2021.  Identity-Based Integrity Verification (IBIV) Protocol for Cloud Data Storage. 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). :1–6.
With meteoric advancement in quantum computing, the traditional data integrity verifying schemes are no longer safe for cloud data storage. A large number of the current techniques are dependent on expensive Public Key Infrastructure (PKI). They cost computationally and communicationally heavy for verification which do not stand with the advantages when quantum computing techniques are applied. Hence, a quantum safe and efficient integrity verification protocol is a research hotspot. Lattice-based signature constructions involve matrix-matrix or matrix vector multiplications making computation competent, simple and resistant to quantum computer attacks. Study in this paper uses Bloom Filter which offers high efficiency in query and search operations. Further, we propose an Identity-Based Integrity Verification (IBIV) protocol for cloud storage from Lattice and Bloom filter. We focus on security against attacks from Cloud Service Provider (CSP), data privacy attacks against Third Party Auditor (TPA) and improvement in efficiency.
Srinivasan, Sudarshan, Begoli, Edmon, Mahbub, Maria, Knight, Kathryn.  2021.  Nomen Est Omen - The Role of Signatures in Ascribing Email Author Identity with Transformer Neural Networks. 2021 IEEE Security and Privacy Workshops (SPW). :291–297.
Authorship attribution, an NLP problem where anonymous text is matched to its author, has important, cross-disciplinary applications, particularly those concerning cyber-defense. Our research examines the degree of sensitivity that attention-based models have to adversarial perturbations. We ask, what is the minimal amount of change necessary to maximally confuse a transformer model? In our investigation we examine a balanced subset of emails from the Enron email dataset, calculating the performance of our model before and after email signatures have been perturbed. Results show that the model's performance changed significantly in the absence of a signature, indicating the importance of email signatures in email authorship detection. Furthermore, we show that these models rely on signatures for shorter emails much more than for longer emails. We also indicate that additional research is necessary to investigate stylometric features and adversarial training to further improve classification model robustness.
Johnson, Andrew, Haddad, Rami J..  2021.  Evading Signature-Based Antivirus Software Using Custom Reverse Shell Exploit. SoutheastCon 2021. :1–6.
Antivirus software is considered to be the primary line of defense against malicious software in modern computing systems. The purpose of this paper is to expose exploitation that can evade Antivirus software that uses signature-based detection algorithms. In this paper, a novel approach was proposed to change the source code of a common Metasploit-Framework used to compile the reverse shell payload without altering its functionality but changing its signature. The proposed method introduced an additional stage to the shellcode program. Instead of the shellcode being generated and stored within the program, it was generated separately and stored on a remote server and then only accessed when the program is executed. This approach was able to reduce its detectability by the Antivirus software by 97% compared to a typical reverse shell program.
Arfeen, Asad, Ahmed, Saad, Khan, Muhammad Asim, Jafri, Syed Faraz Ali.  2021.  Endpoint Detection Amp; Response: A Malware Identification Solution. 2021 International Conference on Cyber Warfare and Security (ICCWS). :1–8.
Malicious hackers breach security perimeters, cause infrastructure disruptions as well as steal proprietary information, financial data, and violate consumers' privacy. Protection of the whole organization by using the firm's security officers can be besieged with faulty warnings. Engineers must shift from console to console to put together investigative clues as a result of today's fragmented security technologies that cause frustratingly sluggish investigations. Endpoint Detection and Response (EDR) solutions adds an extra layer of protection to prevent an endpoint action into a breach. EDR is the region's foremost detection and response tool that combines endpoint and network data to recognize and respond to sophisticated threats. Offering unrivaled security and operational effectiveness, it integrates prevention, investigation, detection, and responding in a single platform. EDR provides enterprise coverage and uninterrupted defense with its continuous monitoring and response to threats. We have presented a comprehensive review of existing EDRs through various security layers that includes detection, response and management capabilities which enables security teams to have unified end-to-end corporate accessibility, powerful analytics along with additional features such as web threat scan, external device scan and automatic reaction across the whole technological tower.
Chen, Quan, Snyder, Peter, Livshits, Ben, Kapravelos, Alexandros.  2021.  Detecting Filter List Evasion with Event-Loop-Turn Granularity JavaScript Signatures. 2021 IEEE Symposium on Security and Privacy (SP). :1715–1729.

Content blocking is an important part of a per-formant, user-serving, privacy respecting web. Current content blockers work by building trust labels over URLs. While useful, this approach has many well understood shortcomings. Attackers may avoid detection by changing URLs or domains, bundling unwanted code with benign code, or inlining code in pages.The common flaw in existing approaches is that they evaluate code based on its delivery mechanism, not its behavior. In this work we address this problem by building a system for generating signatures of the privacy-and-security relevant behavior of executed JavaScript. Our system uses as the unit of analysis each script's behavior during each turn on the JavaScript event loop. Focusing on event loop turns allows us to build highly identifying signatures for JavaScript code that are robust against code obfuscation, code bundling, URL modification, and other common evasions, as well as handle unique aspects of web applications.This work makes the following contributions to the problem of measuring and improving content blocking on the web: First, we design and implement a novel system to build per-event-loop-turn signatures of JavaScript behavior through deep instrumentation of the Blink and V8 runtimes. Second, we apply these signatures to measure how much privacy-and-security harming code is missed by current content blockers, by using EasyList and EasyPrivacy as ground truth and finding scripts that have the same privacy and security harming patterns. We build 1,995,444 signatures of privacy-and-security relevant behaviors from 11,212 unique scripts blocked by filter lists, and find 3,589 unique scripts hosting known harmful code, but missed by filter lists, affecting 12.48% of websites measured. Third, we provide a taxonomy of ways scripts avoid detection and quantify the occurrence of each. Finally, we present defenses against these evasions, in the form of filter list additions where possible, and through a proposed, signature based system in other cases.As part of this work, we share the implementation of our signature-generation system, the data gathered by applying that system to the Alexa 100K, and 586 AdBlock Plus compatible filter list rules to block instances of currently blocked code being moved to new URLs.

Kara, Mustafa, \c Sanlıöz, \c Sevki Gani, Merzeh, Hisham R. J., Aydın, Muhammed Ali, Balık, Hasan Hüseyin.  2021.  Blockchain Based Mutual Authentication for VoIP Applications with Biometric Signatures. 2021 6th International Conference on Computer Science and Engineering (UBMK). :133–138.

In this study, a novel decentralized authentication model is proposed for establishing a secure communications structure in VoIP applications. The proposed scheme considers a distributed architecture called the blockchain. With this scheme, we highlight the multimedia data is more resistant to some of the potential attacks according to the centralized architecture. Our scheme presents the overall system authentication architecture, and it is suitable for mutual authentication in terms of privacy and anonymity. We construct an ECC-based model in the encryption infrastructure because our structure is time-constrained during communications. This study differs from prior work in that blockchain platforms with ECC-Based Biometric Signature. We generate a biometric key for creating a unique ID value with ECC to verify the caller and device authentication together in blockchain. We validated the proposed model by comparing with the existing method in VoIP application used centralized architecture.

2022-02-24
Muhati, Eric, Rawat, Danda B..  2021.  Adversarial Machine Learning for Inferring Augmented Cyber Agility Prediction. IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :1–6.
Security analysts conduct continuous evaluations of cyber-defense tools to keep pace with advanced and persistent threats. Cyber agility has become a critical proactive security resource that makes it possible to measure defense adjustments and reactions to rising threats. Subsequently, machine learning has been applied to support cyber agility prediction as an essential effort to anticipate future security performance. Nevertheless, apt and treacherous actors motivated by economic incentives continue to prevail in circumventing machine learning-based protection tools. Adversarial learning, widely applied to computer security, especially intrusion detection, has emerged as a new area of concern for the recently recognized critical cyber agility prediction. The rationale is, if a sophisticated malicious actor obtains the cyber agility parameters, correct prediction cannot be guaranteed. Unless with a demonstration of white-box attack failures. The challenge lies in recognizing that unconstrained adversaries hold vast potential capabilities. In practice, they could have perfect-knowledge, i.e., a full understanding of the defense tool in use. We address this challenge by proposing an adversarial machine learning approach that achieves accurate cyber agility forecast through mapped nefarious influence on static defense tools metrics. Considering an adversary would aim at influencing perilous confidence in a defense tool, we demonstrate resilient cyber agility prediction through verified attack signatures in dynamic learning windows. After that, we compare cyber agility prediction under negative influence with and without our proposed dynamic learning windows. Our numerical results show the model's execution degrades without adversarial machine learning. Such a feigned measure of performance could lead to incorrect software security patching.
2021-11-29
Nait-Abdesselam, Farid, Darwaish, Asim, Titouna, Chafiq.  2020.  An Intelligent Malware Detection and Classification System Using Apps-to-Images Transformations and Convolutional Neural Networks. 2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :1–6.
With the proliferation of Mobile Internet, handheld devices are facing continuous threats from apps that contain malicious intents. These malicious apps, or malware, have the capability of dynamically changing their intended code as they spread. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses, which typically use signature-based techniques, and make them unable to detect the previously unknown malware. However, the variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns, obtained either statically or dynamically, can be exploited to detect and classify unknown malware into their known families using machine learning techniques. In this paper, we propose a new approach for detecting and analyzing a malware. Mainly focused on android apps, our approach adopts the two following steps: (1) performs a transformation of an APK file into a lightweight RGB image using a predefined dictionary and intelligent mapping, and (2) trains a convolutional neural network on the obtained images for the purpose of signature detection and malware family classification. The results obtained using the Androzoo dataset show that our system classifies both legacy and new malware apps with high accuracy, low false-negative rate (FNR), and low false-positive rate (FPR).
Sun, Yixin, Jee, Kangkook, Sivakorn, Suphannee, Li, Zhichun, Lumezanu, Cristian, Korts-Parn, Lauri, Wu, Zhenyu, Rhee, Junghwan, Kim, Chung Hwan, Chiang, Mung et al..  2020.  Detecting Malware Injection with Program-DNS Behavior. 2020 IEEE European Symposium on Security and Privacy (EuroS P). :552–568.
Analyzing the DNS traffic of Internet hosts has been a successful technique to counter cyberattacks and identify connections to malicious domains. However, recent stealthy attacks hide malicious activities within seemingly legitimate connections to popular web services made by benign programs. Traditional DNS monitoring and signature-based detection techniques are ineffective against such attacks. To tackle this challenge, we present a new program-level approach that can effectively detect such stealthy attacks. Our method builds a fine-grained Program-DNS profile for each benign program that characterizes what should be the “expected” DNS behavior. We find that malware-injected processes have DNS activities which significantly deviate from the Program-DNS profile of the benign program. We then develop six novel features based on the Program-DNS profile, and evaluate the features on a dataset of over 130 million DNS requests collected from a real-world enterprise and 8 million requests from malware-samples executed in a sandbox environment. We compare our detection results with that of previously-proposed features and demonstrate that our new features successfully detect 190 malware-injected processes which fail to be detected by previously-proposed features. Overall, our study demonstrates that fine-grained Program-DNS profiles can provide meaningful and effective features in building detectors for attack campaigns that bypass existing detection systems.
Gajjar, Himali, Malek, Zakiya.  2020.  A Survey of Intrusion Detection System (IDS) using Openstack Private Cloud. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :162–168.
Computer Networks fights with a continues issues with attackers and intruders. Attacks on distributed systems becoming more powerful and more frequent day by day. Intrusion detection methods are performing main role to detect intruders and attackers. To identify intrusion on computer or computer networks an intrusion detection system methods are used. Network Intrusion Detection System (NIDS) performs an prime role by presenting the network security. It gives a defense layer by monitoring the traffic on network for predefined distrustful activity or pattern. In this paper we have analyze and compare existing signature based and anomaly based algorithm with Openstack private cloud.
Yilmaz, Ibrahim, Siraj, Ambareen, Ulybyshev, Denis.  2020.  Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning. 2020 IEEE 4th Conference on Information Communication Technology (CICT). :1–6.
Domain Generation Algorithms (DGAs) are used by adversaries to establish Command and Control (C&C) server communications during cyber attacks. Blacklists of known/identified C&C domains are used as one of the defense mechanisms. However, static blacklists generated by signature-based approaches can neither keep up nor detect never-seen-before malicious domain names. To address this weakness, we applied a DGA-based malicious domain classifier using the Long Short-Term Memory (LSTM) method with a novel feature engineering technique. Our model's performance shows a greater accuracy compared to a previously reported model. Additionally, we propose a new adversarial machine learning-based method to generate never-before-seen malware-related domain families. We augment the training dataset with new samples to make the training of the models more effective in detecting never-before-seen malicious domain names. To protect blacklists of malicious domain names against adversarial access and modifications, we devise secure data containers to store and transfer blacklists.
Li, Taojin, Lei, Songgui, Wang, Wei, Wang, Qingli.  2020.  Research on MR virtual scene location method based on image recognition. 2020 International Conference on Information Science, Parallel and Distributed Systems (ISPDS). :109–113.
In order to solve the problem of accurate positioning of mixed reality virtual scene in physical space, this paper, firstly, analyzes the positioning principle of mixed reality virtual scene. Secondly, based on the comparison among the three developer kits: ARToolKit, ARTag, and Vuforia and two image optimization algorithms: AHE and ACE, it makes sure to use Vuforia development tool to complete the signature-based tracking and registration task, and use ACE algorithm to optimize the signature-based image. It improves the efficiency, stability and accuracy of image recognition registration. Then the multi-target recognition and registration technology is used to realize the multi-location of virtual scene. Finally, Hololens glasses are used as the hardware carrier to verify the above method. The experimental results show that the above method not only realizes the precise location of MR virtual scene based on image recognition, but also ensures the absolute position of the virtual model in the real space, bringing users a more real virtual experience. Keywords-mixed reality, multi-person collaboration, virtual positioning, gesture interaction.
2021-08-17
Tseng, Chia-Wei, Wu, Li-Fan, Hsu, Shih-Chun, Yu, Sheng-Wang.  2020.  IPv6 DoS Attacks Detection Using Machine Learning Enhanced IDS in SDN/NFV Environment. 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS). :263–266.
The rapid growth of IPv6 traffic makes security issues become more important. This paper proposes an IPv6 network security system that integrates signature-based Intrusion Detection Systems (IDS) and machine learning classification technologies to improve the accuracy of IPv6 denial-of-service (DoS) attacks detection. In addition, this paper has also enhanced IPv6 network security defense capabilities through software-defined networking (SDN) and network function virtualization (NFV) technologies. The experimental results prove that the detection and defense mechanisms proposed in this paper can effectively strengthen IPv6 network security.
2021-07-08
Chaturvedi, Amit Kumar, Kumar, Punit, Sharma, Kalpana.  2020.  Proposing Innovative Intruder Detection System for Host Machines in Cloud Computing. 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART). :292—296.
There is very significant role of Virtualization in cloud computing. The physical hardware in the cloud computing reside with the host machine and the virtualization software runs on it. The virtualization allows virtual machines to exist. The host machine shares its physical components such as memory, storage, and processor ultimately to handle the needs of the virtual machines. If an attacker effectively compromises one VM, it could outbreak others on the same host on the network over long periods of time. This is an gradually more popular method for cross-virtual-machine attacks, since traffic between VMs cannot be examined by standard IDS/IPS software programs. As we know that the cloud environment is distributed in nature and hence more susceptible to various types of intrusion attacks which include installing malicious software and generating backdoors. In a cloud environment, where organizations have hosted important and critical data, the security of underlying technologies becomes critical. To alleviate the hazard to cloud environments, Intrusion Detection Systems (IDS) are a cover of defense. In this paper, we are proposing an innovative model for Intrusion Detection System for securing Host machines in cloud infrastructure. This proposed IDS has two important features: (1) signature based and (2) prompt alert system.
2020-03-12
Lafram, Ichrak, Berbiche, Naoual, El Alami, Jamila.  2019.  Artificial Neural Networks Optimized with Unsupervised Clustering for IDS Classification. 2019 1st International Conference on Smart Systems and Data Science (ICSSD). :1–7.

Information systems are becoming more and more complex and closely linked. These systems are encountering an enormous amount of nefarious traffic while ensuring real - time connectivity. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is intrusion detection systems (IDS). An IDS tries to identify fraudulent activity using predetermined signatures or pre-established user misbehavior while monitoring incoming traffic. Intrusion detection systems based on signature and behavior cannot detect new attacks and fall when small behavior deviations occur. Many researchers have proposed various approaches to intrusion detection using machine learning techniques as a new and promising tool to remedy this problem. In this paper, the authors present a combination of two machine learning methods, unsupervised clustering followed by a supervised classification framework as a Fast, highly scalable and precise packets classification system. This model's performance is assessed on the new proposed dataset by the Canadian Institute for Cyber security and the University of New Brunswick (CICIDS2017). The overall process was fast, showing high accuracy classification results.

Park, Sean, Gondal, Iqbal, Kamruzzaman, Joarder, Zhang, Leo.  2019.  One-Shot Malware Outbreak Detection Using Spatio-Temporal Isomorphic Dynamic Features. 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). :751–756.

Fingerprinting the malware by its behavioural signature has been an attractive approach for malware detection due to the homogeneity of dynamic execution patterns across different variants of similar families. Although previous researches show reasonably good performance in dynamic detection using machine learning techniques on a large corpus of training set, decisions must be undertaken based upon a scarce number of observable samples in many practical defence scenarios. This paper demonstrates the effectiveness of generative adversarial autoencoder for dynamic malware detection under outbreak situations where in most cases a single sample is available for training the machine learning algorithm to detect similar samples that are in the wild.

Shamsi, Kaveh, Pan, David Z., Jin, Yier.  2019.  On the Impossibility of Approximation-Resilient Circuit Locking. 2019 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :161–170.

Logic locking, and Integrated Circuit (IC) Camouflaging, are techniques that try to hide the design of an IC from a malicious foundry or end-user by introducing ambiguity into the netlist of the circuit. While over the past decade an array of such techniques have been proposed, their security has been constantly challenged by algorithmic attacks. This may in part be due to a lack of formally defined notions of security in the first place, and hence a lack of security guarantees based on long-standing hardness assumptions. In this paper we take a formal approach. We define the problem of circuit locking (cL) as transforming an original circuit to a locked one which is ``unintelligable'' without a secret key (this can model camouflaging and split-manufacturing in addition to logic locking). We define several notions of security for cL under different adversary models. Using long standing results from computational learning theory we show the impossibility of exponentially approximation-resilient locking in the presence of an oracle for large classes of Boolean circuits. We then show how exact-recovery-resiliency and a more relaxed notion of security that we coin ``best-possible'' approximation-resiliency can be provably guaranteed with polynomial overhead. Our theoretical analysis directly results in stronger attacks and defenses which we demonstrate through experimental results on benchmark circuits.

Dogruluk, Ertugrul, Costa, Antonio, Macedo, Joaquim.  2019.  A Detection and Defense Approach for Content Privacy in Named Data Network. 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1–5.

The Named Data Network (NDN) is a promising network paradigm for content distribution based on caching. However, it may put consumer privacy at risk, as the adversary may identify the content, the name and the signature (namely a certificate) through side-channel timing responses from the cache of the routers. The adversary may identify the content name and the consumer node by distinguishing between cached and un- cached contents. In order to mitigate the timing attack, effective countermeasure methods have been proposed by other authors, such as random caching, random freshness, and probabilistic caching. In this work, we have implemented a timing attack scenario to evaluate the efficiency of these countermeasures and to demonstrate how the adversary can be detected. For this goal, a brute force timing attack scenario based on a real topology was developed, which is the first brute force attack model applied in NDN. Results show that the adversary nodes can be effectively distinguished from other legitimate consumers during the attack period. It is also proposed a multi-level mechanism to detect an adversary node. Through this approach, the content distribution performance can be mitigated against the attack.

Vieira, Leandro, Santos, Leonel, Gon\c calves, Ramiro, Rabadão, Carlos.  2019.  Identifying Attack Signatures for the Internet of Things: An IP Flow Based Approach. 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). :1–7.

At the time of more and more devices being connected to the internet, personal and sensitive information is going around the network more than ever. Thus, security and privacy regarding IoT communications, devices, and data are a concern due to the diversity of the devices and protocols used. Since traditional security mechanisms cannot always be adequate due to the heterogeneity and resource limitations of IoT devices, we conclude that there are still several improvements to be made to the 2nd line of defense mechanisms like Intrusion Detection Systems. Using a collection of IP flows, we can monitor the network and identify properties of the data that goes in and out. Since network flows collection have a smaller footprint than packet capturing, it makes it a better choice towards the Internet of Things networks. This paper aims to study IP flow properties of certain network attacks, with the goal of identifying an attack signature only by observing those properties.

Ao, Weijun, Fu, Shaojing, Zhang, Chao, Huang, Yuzhou, Xia, Fei.  2019.  A Secure Identity Authentication Scheme Based on Blockchain and Identity-Based Cryptography. 2019 IEEE 2nd International Conference on Computer and Communication Engineering Technology (CCET). :90–95.

Most blockchain-based identity authentication systems focus on using blockchain to establish the public key infrastructure (PKI). It can solve the problem of single point of failure and certificate transparency faced by traditional PKI systems, but there are still some problems such as complex certificate management and complex certificate usage process. In this paper, we propose an identity authentication scheme based on blockchain and identity-based cryptography (IBC). The scheme implements a decentralized private key generator (PKG) by deploying the smart contract in Ethereum blockchain, and uses the IBC signature algorithm and challenge-response protocol during the authentication process. Compared with other blockchain-based identity authentication systems, the scheme not only prevents the single point of failure, but also avoids the complex certificate management, has lower system complexity, and resists impersonation attack, man-in-the-middle attack and replay attack.

Bai, He, Wu, Cangshuai, Yang, Yuexiang, Xia, Geming, Jiang, Yue.  2019.  A Blockchain-Based Traffic Conditions and Driving Behaviors Warning Scheme in the Internet of Vehicles. 2019 IEEE 19th International Conference on Communication Technology (ICCT). :1160–1164.

With the economic development, the number of cars is increasing, and the traffic accidents and congestion problems that follow will not be underestimated. The concept of the Internet of Vehicles is becoming popular, and demand for intelligent traffic is growing. In this paper, the warning scheme we proposed aims to solve the traffic problems. Using intelligent terminals, it is faster and more convenient to obtain driving behaviors and road condition information. The application of blockchain technology can spread information to other vehicles for sharing without third-party certification. Group signature-based authentication protocol guarantees privacy and security while ensuring identity traceability. In experiments and simulations, the recognition accuracy of driving behavior can reach up to 94.90%. The use of blockchain provides secure, distributed, and autonomous features for the solution. Compared with the traditional signature method, the group signature-based authentication time varies less with the increase of the number of vehicles, and the communication time is more stable.

Cortés, Francisco Muñoz, Gaviria Gómez, Natalia.  2019.  A Hybrid Alarm Management Strategy in Signature-Based Intrusion Detection Systems. 2019 IEEE Colombian Conference on Communications and Computing (COLCOM). :1–6.

Signature-based Intrusion Detection Systems (IDS) are a key component in the cybersecurity defense strategy for any network being monitored. In order to improve the efficiency of the intrusion detection system and the corresponding mitigation action, it is important to address the problem of false alarms. In this paper, we present a comparative analysis of two approaches that consider the false alarm minimization and alarm correlation techniques. The output of this analysis provides us the elements to propose a parallelizable strategy designed to achieve better results in terms of precision, recall and alarm load reduction in the prioritization of alarms. We use Prelude SIEM as the event normalizer in order to process security events from heterogeneous sensors and to correlate them. The alarms are verified using the dynamic network context information collected from the vulnerability analysis, and they are prioritized using the HP Arsight priority formula. The results show an important reduction in the volume of alerts, together with a high precision in the identification of false alarms.