Muhammad Taimoor Khan

Khouloud Saadi, Muhammad Taimoor Khan

Lifelong machine learning or continual learning models attempt to learn incrementally by accumulating knowledge across a sequence of tasks. Therefore, these models learn better and faster. They are used in various intelligent systems that have to interact with humans or any dynamic environment e.g., chatbots and self-driving cars. Memory-less approach is more often used with deep neural networks that accommodates incoming information from tasks within its architecture. It allows them to perform well on all the seen tasks. These models suffer from semantic drift or the plasticity-stability dilemma. The existing models use Minkowski distance measures to decide which nodes to freeze, update or duplicate. These distance metrics do not provide better separation of nodes as they are susceptible to high dimensional sparse vectors. In our proposed approach, we use angular distance to evaluate the semantic drift in individual nodes that provide better separation of nodes and thus better balancing between stability and plasticity. The proposed approach outperforms state-of-the art models by maintaining higher accuracy on standard datasets.

Muhammad Taimoor Khan, Dimitrios Serpanos, Howard Shrobe

Conventional approaches for ensuring the security of application software at run-time, through monitoring, either produce (high rates of) false alarms (e.g. intrusion detection systems) or limit application performance (e.g. run-time verification). We present a runtime security monitor that detects both known and unknown cyber attacks by checking that the run-time behavior of the application is consistent with the expected behavior modeled in application specification. This is crucial because, even if the implementation is consistent with its specification, the application may still be vulnerable due to flaws in the supporting infrastructure (e.g. the language runtime system, libraries and operating system). This runtime security monitor is sound and complete, eliminating false alarms, as well as efficient, so that it does not limit runtime application performance and so that it supports real-time systems. The security monitor takes as input the application specification and the application implementation, which may be expressed in different languages. The specification language of the application software is formalized based on monadic second order logic and event calculus interpreted over algebraic data structures. This language allows us to express behavior of an application at any desired (and practical) level of abstraction as well as with high degree of modularity. The security monitor detects every attack by systematically comparing the application execution and specification behaviors at runtime, even though they operate at two different levels of abstraction. We define the denotational semantics of the specification language and prove that the monitor is sound and complete. Furthermore, the monitor is efficient because of the modular application specification at appropriate level(s) of abstraction.

Muhammad Taimoor Khan, Dimitrios Serpanos, Howard Shrobe

We present our ongoing work and initial results towards the (behavioral) correctness analysis of the cognitive middleware AWDRAT. Since, the (provable) behavioral correctness of a software system is a fundamental pre-requisite of the system's security. Therefore, the goal of the work is to first formalize the behavioral semantics of the middleware as a pre-requisite for our proof of the behavioral correctness. However, in this paper, we focus only on the core and critical component of the middleware, i.e. Execution Monitor which is a part of the module "Architectural Differencer" of AWDRAT. The role of the execution monitor is to identify inconsistencies between runtime observations of the target system and predictions of the specification System Architectural Model of the system. As a starting point we have defined the formal (denotational) semantics of the observations (runtime events) and predictions (executable specifications as of System Architectural Model); then based on the aforementioned formal semantices, we have formalized the behavior of the "Execution Monitor" of the middleware.

Muhammad Taimoor Khan, Wolfgang Schreiner

In this paper, we present our ongoing work and initial results on the formal specification and verification of MiniMaple (a substantial subset of Maple with slight extensions) programs. The main goal of our work is to find behavioral errors in such programs w.r.t. their specifications by static analysis. This task is more complex for widely used computer algebra languages like Maple as these are fundamentally different from classical languages: they support non-standard types of objects such as symbols, unevaluated expressions and polynomials and require abstract computer algebraic concepts and objects such as rings and orderings etc. As a starting point we have defined and formalized a syntax, semantics, type system and specification language for MiniMaple.

Muhammad Taimoor Khan, Wolfgang Schreiner

This paper is an example-based demonstration of our initial results on the formal specification of programs written in the computer algebra language MiniMaple (a substantial subset of Maple with slight extensions). The main goal of this work is to define a verification framework for MiniMaple. Formal specification of MiniMaple programs is rather complex task as it supports non-standard types of objects, e.g. symbols and unevaluated expressions, and additional functions and predicates, e.g. runtime type tests etc. We have used the specification language to specify various computer algebra concepts respective objects of the Maple package DifferenceDifferential developed at our institute.