Visible to the public Biblio

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2021-06-28
Chen, Yi-Fan, Huang, Ding-Hsiang, Huang, Cheng-Fu, Lin, Yi-Kuei.  2020.  Reliability Evaluation for a Cloud Computer Network with Fog Computing. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :682–683.
The most recent and important developments in the field of computer networks are cloud and fog computing. In this study, modern cloud computer networks comprising computers, internet of things (IoT), fog servers, and cloud servers for data transmission, is investigated. A cloud computer networks can be modeled as a network with nodes and arcs, in which each arc represents a transmission line, and each node represents an IoT device, a fog server, or a cloud server. Each transmission line has several possible capacities and is regarded as a multistate. The network is termed a multi-state cloud computer network (MCCN). this study firstly constructs the mathematic model to elucidate the flow relationship among the IoT devices, edge servers, and cloud servers and subsequently develop an algorithm to evaluate the performance of the MCCN by calculating network reliability which is defined as the probability of the data being successfully processed by the MCCN.
2021-04-09
Mishra, A., Yadav, P..  2020.  Anomaly-based IDS to Detect Attack Using Various Artificial Intelligence Machine Learning Algorithms: A Review. 2nd International Conference on Data, Engineering and Applications (IDEA). :1—7.
Cyber-attacks are becoming more complex & increasing tasks in accurate intrusion detection (ID). Failure to avoid intrusion can reduce the reliability of security services, for example, integrity, Privacy & availability of data. The rapid proliferation of computer networks (CNs) has reformed the perception of network security. Easily accessible circumstances affect computer networks from many threats by hackers. Threats to a network are many & hypothetically devastating. Researchers have recognized an Intrusion Detection System (IDS) up to identifying attacks into a wide variety of environments. Several approaches to intrusion detection, usually identified as Signature-based Intrusion Detection Systems (SIDS) & Anomaly-based Intrusion Detection Systems (AIDS), were proposed in the literature to address computer safety hazards. This survey paper grants a review of current IDS, complete analysis of prominent new works & generally utilized dataset to evaluation determinations. It also introduces avoidance techniques utilized by attackers to avoid detection. This paper delivers a description of AIDS for attack detection. IDS is an applied research area in artificial intelligence (AI) that uses multiple machine learning algorithms.
2021-04-08
Zhang, H., Ma, J., Wang, Y., Pei, Q..  2009.  An Active Defense Model and Framework of Insider Threats Detection and Sense. 2009 Fifth International Conference on Information Assurance and Security. 1:258—261.
Insider attacks is a well-known problem acknowledged as a threat as early as 1980s. The threat is attributed to legitimate users who take advantage of familiarity with the computational environment and abuse their privileges, can easily cause significant damage or losses. In this paper, we present an active defense model and framework of insider threat detection and sense. Firstly, we describe the hierarchical framework which deal with insider threat from several aspects, and subsequently, show a hierarchy-mapping based insider threats model, the kernel of the threats detection, sense and prediction. The experiments show that the model and framework could sense the insider threat in real-time effectively.
Vyetrenko, S., Khosla, A., Ho, T..  2009.  On combining information-theoretic and cryptographic approaches to network coding security against the pollution attack. 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. :788–792.
In this paper we consider the pollution attack in network coded systems where network nodes are computationally limited. We consider the combined use of cryptographic signature based security and information theoretic network error correction and propose a fountain-like network error correction code construction suitable for this purpose.
2021-03-30
Ashiku, L., Dagli, C..  2020.  Agent Based Cybersecurity Model for Business Entity Risk Assessment. 2020 IEEE International Symposium on Systems Engineering (ISSE). :1—6.

Computer networks and surging advancements of innovative information technology construct a critical infrastructure for network transactions of business entities. Information exchange and data access though such infrastructure is scrutinized by adversaries for vulnerabilities that lead to cyber-attacks. This paper presents an agent-based system modelling to conceptualize and extract explicit and latent structure of the complex enterprise systems as well as human interactions within the system to determine common vulnerabilities of the entity. The model captures emergent behavior resulting from interactions of multiple network agents including the number of workstations, regular, administrator and third-party users, external and internal attacks, defense mechanisms for the network setting, and many other parameters. A risk-based approach to modelling cybersecurity of a business entity is utilized to derive the rate of attacks. A neural network model will generalize the type of attack based on network traffic features allowing dynamic state changes. Rules of engagement to generate self-organizing behavior will be leveraged to appoint a defense mechanism suitable for the attack-state of the model. The effectiveness of the model will be depicted by time-state chart that shows the number of affected assets for the different types of attacks triggered by the entity risk and the time it takes to revert into normal state. The model will also associate a relevant cost per incident occurrence that derives the need for enhancement of security solutions.

2021-03-29
Johanyák, Z. C..  2020.  Fuzzy Logic based Network Intrusion Detection Systems. 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI). :15—16.

Plenary Talk Our everyday life is more and more dependent on electronic communication and network connectivity. However, the threats of attacks and different types of misuse increase exponentially with the expansion of computer networks. In order to alleviate the problem and to identify malicious activities as early as possible Network Intrusion Detection Systems (NIDSs) have been developed and intensively investigated. Several approaches have been proposed and applied so far for these systems. It is a common challenge in this field that often there are no crisp boundaries between normal and abnormal network traffic, there are noisy or inaccurate data and therefore the investigated traffic could represent both attack and normal communication. Fuzzy logic based solutions could be advantageous owing to their capability to define membership levels in different classes and to do different operations with results ensuring reduced false positive and false negative classification compared to other approaches. In this presentation, after a short introduction of NIDSs a survey will be done on typical fuzzy logic based solutions followed by a detailed description of a fuzzy rule interpolation based IDS. The whole development process, i.e. data preprocessing, feature extraction, rule base generation steps are covered as well.

2021-03-09
Anithaashri, T. P., Ravichandran, G..  2020.  Security Enhancement for the Network Amalgamation using Machine Learning Algorithm. 2020 International Conference on Smart Electronics and Communication (ICOSEC). :411—416.

Accessing the secured data through the network is a major task in emerging technology. Data needs to be protected from the network vulnerabilities, malicious users, hackers, sniffers, intruders. The novel framework has been designed to provide high security in data transaction through computer network. The implant of network amalgamation in the recent trends, make the way in security enhancement in an efficient manner through the machine learning algorithm. In this system the usage of the biometric authenticity plays a vital role for unique approach. The novel mathematical approach is used in machine learning algorithms to solve these problems and provide the security enhancement. The result shows that the novel method has consistent improvement in enhancing the security of data transactions in the emerging technologies.

2021-03-01
Zhang, Y., Groves, T., Cook, B., Wright, N. J., Coskun, A. K..  2020.  Quantifying the impact of network congestion on application performance and network metrics. 2020 IEEE International Conference on Cluster Computing (CLUSTER). :162–168.
In modern high-performance computing (HPC) systems, network congestion is an important factor that contributes to performance degradation. However, how network congestion impacts application performance is not fully understood. As Aries network, a recent HPC network architecture featuring a dragonfly topology, is equipped with network counters measuring packet transmission statistics on each router, these network metrics can potentially be utilized to understand network performance. In this work, by experiments on a large HPC system, we quantify the impact of network congestion on various applications' performance in terms of execution time, and we correlate application performance with network metrics. Our results demonstrate diverse impacts of network congestion: while applications with intensive MPI operations (such as HACC and MILC) suffer from more than 40% extension in their execution times under network congestion, applications with less intensive MPI operations (such as Graph500 and HPCG) are mostly not affected. We also demonstrate that a stall-to-flit ratio metric derived from Aries network counters is positively correlated with performance degradation and, thus, this metric can serve as an indicator of network congestion in HPC systems.
2021-02-23
Yu, M., He, T., McDaniel, P., Burke, Q. K..  2020.  Flow Table Security in SDN: Adversarial Reconnaissance and Intelligent Attacks. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :1519—1528.

The performance-driven design of SDN architectures leaves many security vulnerabilities, a notable one being the communication bottleneck between the controller and the switches. Functioning as a cache between the controller and the switches, the flow table mitigates this bottleneck by caching flow rules received from the controller at each switch, but is very limited in size due to the high cost and power consumption of the underlying storage medium. It thus presents an easy target for attacks. Observing that many existing defenses are based on simplistic attack models, we develop a model of intelligent attacks that exploit specific cache-like behaviors of the flow table to infer its internal configuration and state, and then design attack parameters accordingly. Our evaluations show that such attacks can accurately expose the internal parameters of the target flow table and cause measurable damage with the minimum effort.

2021-02-16
Lotfalizadeh, H., Kim, D. S..  2020.  Investigating Real-Time Entropy Features of DDoS Attack Based on Categorized Partial-Flows. 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM). :1—6.
With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect initiation and termination of DDoS attacks are investigated. The method to extract these features is devised with respect to some openflowbased switch capabilities. These features provide us with a higher resolution to view and process packet count entropies, thus improving DDoS attack detection capabilities. Although some of the technical assumptions are based on SDN technology and openflow protocol, the methodology can be applied in other networking paradigms as well.
Navabi, S., Nayyar, A..  2020.  A Dynamic Mechanism for Security Management in Multi-Agent Networked Systems. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :1628—1637.
We study the problem of designing a dynamic mechanism for security management in an interconnected multi-agent system with N strategic agents and one coordinator. The system is modeled as a network of N vertices. Each agent resides in one of the vertices of the network and has a privately known security state that describes its safety level at each time. The evolution of an agent's security state depends on its own state, the states of its neighbors in the network and on actions taken by a network coordinator. Each agent's utility at time instant t depends on its own state, the states of its neighbors in the network and on actions taken by a network coordinator. The objective of the network coordinator is to take security actions in order to maximize the long-term expected social surplus. Since agents are strategic and their security states are private information, the coordinator needs to incentivize agents to reveal their information. This results in a dynamic mechanism design problem for the coordinator. We leverage the inter-temporal correlations between the agents' security states to identify sufficient conditions under which an incentive compatible expected social surplus maximizing mechanism can be constructed. We then identify two special cases of our formulation and describe how the desired mechanism is constructed in these cases.
2021-01-25
Ghazo, A. T. Al, Ibrahim, M., Ren, H., Kumar, R..  2020.  A2G2V: Automatic Attack Graph Generation and Visualization and Its Applications to Computer and SCADA Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 50:3488–3498.
Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks.
More, S., Jamadar, I., Kazi, F..  2020.  Security Visualization and Active Querying for OT Network. :1—6.

Traditionally Industrial Control System(ICS) used air-gap mechanism to protect Operational Technology (OT) networks from cyber-attacks. As internet is evolving and so are business models, customer supplier relationships and their needs are changing. Hence lot of ICS are now connected to internet by providing levels of defense strategies in between OT network and business network to overcome the traditional mechanism of air-gap. This upgrade made OT networks available and accessible through internet. OT networks involve number of physical objects and computer networks. Physical damages to system have become rare but the number of cyber-attacks occurring are evidently increasing. To tackle cyber-attacks, we have a number of measures in place like Firewalls, Intrusion Detection System (IDS) and Intrusion Prevention System (IPS). To ensure no attack on or suspicious behavior within network takes place, we can use visual aids like creating dashboards which are able to flag any such activity and create visual alert about same. This paper describes creation of parser object to convert Common Event Format(CEF) to Comma Separated Values(CSV) format and dashboard to extract maximum amount of data and analyze network behavior. And working of active querying by leveraging packet level data from network to analyze network inclusion in real-time. The mentioned methodology is verified on data collected from Waste Water Treatment Plant and results are presented.,} booktitle = {2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

2021-01-22
Akbari, I., Tahoun, E., Salahuddin, M. A., Limam, N., Boutaba, R..  2020.  ATMoS: Autonomous Threat Mitigation in SDN using Reinforcement Learning. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1—9.
Machine Learning has revolutionized many fields of computer science. Reinforcement Learning (RL), in particular, stands out as a solution to sequential decision making problems. With the growing complexity of computer networks in the face of new emerging technologies, such as the Internet of Things and the growing complexity of threat vectors, there is a dire need for autonomous network systems. RL is a viable solution for achieving this autonomy. Software-defined Networking (SDN) provides a global network view and programmability of network behaviour, which can be employed for security management. Previous works in RL-based threat mitigation have mostly focused on very specific problems, mostly non-sequential, with ad-hoc solutions. In this paper, we propose ATMoS, a general framework designed to facilitate the rapid design of RL applications for network security management using SDN. We evaluate our framework for implementing RL applications for threat mitigation, by showcasing the use of ATMoS with a Neural Fitted Q-learning agent to mitigate an Advanced Persistent Threat. We present the RL model's convergence results showing the feasibility of our solution for active threat mitigation.
2020-12-02
Ye, J., Liu, R., Xie, Z., Feng, L., Liu, S..  2019.  EMPTCP: An ECN Based Approach to Detect Shared Bottleneck in MPTCP. 2019 28th International Conference on Computer Communication and Networks (ICCCN). :1—10.

The major challenge of Real Time Protocol is to balance efficiency and fairness over limited bandwidth. MPTCP has proved to be effective for multimedia and real time networks. Ideally, an MPTCP sender should couple the subflows sharing the bottleneck link to provide TCP friendliness. However, existing shared bottleneck detection scheme either utilize end-to-end delay without consideration of multiple bottleneck scenario, or identify subflows on switch at the expense of operation overhead. In this paper, we propose a lightweight yet accurate approach, EMPTCP, to detect shared bottleneck. EMPTCP uses the widely deployed ECN scheme to capture the real congestion state of shared bottleneck, while at the same time can be transparently utilized by various enhanced MPTCP protocols. Through theory analysis, simulation test and real network experiment, we show that EMPTCP achieves higher than 90% accuracy in shared bottleneck detection, thus improving the network efficiency and fairness.

Gliksberg, J., Capra, A., Louvet, A., García, P. J., Sohier, D..  2019.  High-Quality Fault-Resiliency in Fat-Tree Networks (Extended Abstract). 2019 IEEE Symposium on High-Performance Interconnects (HOTI). :9—12.
Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of HPC systems. In this paper we present Dmodc, a fast deterministic routing algorithm for Parallel Generalized Fat-Trees (PGFTs) which minimizes congestion risk even under massive topology degradation caused by equipment failure. It applies a modulo-based computation of forwarding tables among switches closer to the destination, using only knowledge of subtrees for pre-modulo division. Dmodc allows complete re-routing of topologies with tens of thousands of nodes in less than a second, which greatly helps centralized fabric management react to faults with high-quality routing tables and no impact to running applications in current and future very large-scale HPC clusters. We compare Dmodc against routing algorithms available in the InfiniBand control software (OpenSM) first for routing execution time to show feasibility at scale, and then for congestion risk under degradation to demonstrate robustness. The latter comparison is done using static analysis of routing tables under random permutation (RP), shift permutation (SP) and all-to-all (A2A) traffic patterns. Results for Dmodc show A2A and RP congestion risks similar under heavy degradation as the most stable algorithms compared, and near-optimal SP congestion risk up to 1% of random degradation.
Wang, C., Huang, N., Sun, L., Wen, G..  2018.  A Titration Mechanism Based Congestion Model. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :491—496.

Congestion diffusion resulting from the coupling by resource competing is a kind of typical failure propagation in network systems. The existing models of failure propagation mainly focused on the coupling by direct physical connection between nodes, the most efficiency path, or dependence group, while the coupling by resource competing is ignored. In this paper, a model of network congestion diffusion with resource competing is proposed. With the analysis of the similarities to resource competing in biomolecular network, the model describing the dynamic changing process of biomolecule concentration based on titration mechanism provides reference for our model. Then the innovation on titration mechanism is proposed to describe the dynamic changing process of link load in networks, and a novel congestion model is proposed. By this model, the global congestion can be evaluated. Simulations show that network congestion with resource competing can be obtained from our model.

2020-11-23
Mohammadian, M..  2018.  Network Security Risk Assessment Using Intelligent Agents. 2018 International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR). :1–6.
Network security is an important issue in today's world with existence of network systems that communicate data and information about all aspects of our life, work and business. Network security is an important issue with connected networks and data communication between organisations of that specialized in different areas. Network security engineers spend a considerable amount of time to investigate network for security breaches and to enhance the security of their networks and data communications on their networks. They use Attack Graphs (AGs) which are graphical representation of networks to assist them in analysing large networks. With increase size of networks and their complexity, the use of attack graphs alone does not provide the necessary risk analysis and assessment facilities. There is a need for automated intelligent systems such as multiagent systems to assist in analysing, assessing and testing networks. Network systems changes with the increase in the size of organisation and connectivity of network of organisations based on the business needs or organisational or governmental rules and regulations. In this paper a multi-agent system is developed assist in analysing interconnected network to identify security risks. The multi-agent system is capable of security network analysis to identify paths using an attack graph of the network under consideration to protect network systems, as the networks grow and change, against possible attacks. The multiagent system uses a model developed by Mohammadian [3] for converting AGs to Fuzzy Cognitive Maps (FCMs) to identify attack paths from attack graphs and perform security risk analysis. In this paper a novel decision-making approach using FCMs is employed.
2020-11-09
Kemp, C., Calvert, C., Khoshgoftaar, T..  2018.  Utilizing Netflow Data to Detect Slow Read Attacks. 2018 IEEE International Conference on Information Reuse and Integration (IRI). :108–116.
Attackers can leverage several techniques to compromise computer networks, ranging from sophisticated malware to DDoS (Distributed Denial of Service) attacks that target the application layer. Application layer DDoS attacks, such as Slow Read, are implemented with just enough traffic to tie up CPU or memory resources causing web and application servers to go offline. Such attacks can mimic legitimate network requests making them difficult to detect. They also utilize less volume than traditional DDoS attacks. These low volume attack methods can often go undetected by network security solutions until it is too late. In this paper, we explore the use of machine learners for detecting Slow Read DDoS attacks on web servers at the application layer. Our approach uses a generated dataset based upon Netflow data collected at the application layer on a live network environment. Our Netflow data uses the IP Flow Information Export (IPFIX) standard providing significant flexibility and features. These Netflow features can process and handle a growing amount of traffic and have worked well in our previous DDoS work detecting evasion techniques. Our generated dataset consists of real-world network data collected from a production network. We use eight different classifiers to build Slow Read attack detection models. Our wide selection of learners provides us with a more comprehensive analysis of Slow Read detection models. Experimental results show that the machine learners were quite successful in identifying the Slow Read attacks with a high detection and low false alarm rate. The experiment demonstrates that our chosen Netflow features are discriminative enough to detect such attacks accurately.
2020-09-04
Qader, Karwan, Adda, Mo.  2019.  DOS and Brute Force Attacks Faults Detection Using an Optimised Fuzzy C-Means. 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). :1—6.
This paper explains how the commonly occurring DOS and Brute Force attacks on computer networks can be efficiently detected and network performance improved, which reduces costs and time. Therefore, network administrators attempt to instantly diagnose any network issues. The experimental work used the SNMP-MIB parameter datasets, which are collected via a specialised MIB dataset consisting of seven types of attack as noted in section three. To resolves such issues, this researched carried out several important contributions which are related to fault management concerns in computer network systems. A central task in the detection of the attacks relies on MIB feature behaviours using the suggested SFCM method. It was concluded that the DOS and Brute Force fault detection results for three different clustering methods demonstrated that the proposed SFCM detected every data point in the related group. Consequently, the FPC approached 1.0, its highest record, and an improved performance solution better than the EM methods and K-means are based on SNMP-MIB variables.
2020-08-17
Kohnhäuser, Florian, Büscher, Niklas, Katzenbeisser, Stefan.  2019.  A Practical Attestation Protocol for Autonomous Embedded Systems. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :263–278.
With the recent advent of the Internet of Things (IoT), embedded devices increasingly operate collaboratively in autonomous networks. A key technique to guard the secure and safe operation of connected embedded devices is remote attestation. It allows a third party, the verifier, to ensure the integrity of a remote device, the prover. Unfortunately, existing attestation protocols are impractical when applied in autonomous networks of embedded systems due to their limited scalability, performance, robustness, and security guarantees. In this work, we propose PASTA, a novel attestation protocol that is particularly suited for autonomous embedded systems. PASTA is the first that (i) enables many low-end prover devices to attest their integrity towards many potentially untrustworthy low-end verifier devices, (ii) is fully decentralized, thus, able to withstand network disruptions and arbitrary device outages, and (iii) is in addition to software attacks capable of detecting physical attacks in a much more robust way than any existing protocol. We implemented our protocol, conducted measurements, and simulated large networks. The results show that PASTA is practical on low-end embedded devices, scales to large networks with millions of devices, and improves robustness by multiple orders of magnitude compared with the best existing protocols.
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.
2020-08-07
Safar, Jamie L., Tummala, Murali, McEachen, John C., Bollmann, Chad.  2019.  Modeling Worm Propagation and Insider Threat in Air-Gapped Network using Modified SEIQV Model. 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS). :1—6.
Computer worms pose a major threat to computer and communication networks due to the rapid speed at which they propagate. Biologically based epidemic models have been widely used to analyze the propagation of worms in computer networks. For an air-gapped network with an insider threat, we propose a modified Susceptible-Exposed-Infected-Quarantined-Vaccinated (SEIQV) model called the Susceptible-Exposed-Infected-Quarantined-Patched (SEIQP) model. We describe the assumptions that apply to this model, define a set of differential equations that characterize the system dynamics, and solve for the basic reproduction number. We then simulate and analyze the parameters controlled by the insider threat to determine where resources should be allocated to attain different objectives and results.
2020-07-20
Castiglione, Arcangelo, Palmieri, Francesco, Colace, Francesco, Lombardi, Marco, Santaniello, Domenico.  2019.  Lightweight Ciphers in Automotive Networks: A Preliminary Approach. 2019 4th International Conference on System Reliability and Safety (ICSRS). :142–147.
Nowadays, the growing need to connect modern vehicles through computer networks leads to increased risks of cyberattacks. The internal network, which governs the several electronic components of a vehicle, is becoming increasingly overexposed to external attacks. The Controller Area Network (CAN) protocol, used to interconnect those devices is the key point of the internal network of modern vehicles. Therefore, securing such protocol is crucial to ensure a safe driving experience. However, the CAN is a standard that has undergone little changes since it was introduced in 1983. More precisely, in an attempt to reduce latency, the transfer of information remains unencrypted, which today represents a weak point in the protocol. Hence, the need to protect communications, without introducing low-level alterations, while preserving the performance characteristics of the protocol. In this work, we investigate the possibility of using symmetric encryption algorithms for securing messages exchanged by CAN protocol. In particular, we evaluate the using of lightweight ciphers to secure CAN-level communication. Such ciphers represent a reliable solution on hardware-constrained devices, such as microcontrollers.
2020-07-16
Farivar, Faezeh, Haghighi, Mohammad Sayad, Barchinezhad, Soheila, Jolfaei, Alireza.  2019.  Detection and Compensation of Covert Service-Degrading Intrusions in Cyber Physical Systems through Intelligent Adaptive Control. 2019 IEEE International Conference on Industrial Technology (ICIT). :1143—1148.

Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects.